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  • PostgreSQL Backup/Restore

    - by Ian
    What's the best way to backup a postgresql database? I've tried using the documentation at www.postgresql.org, but I always get integrity errors when restoring. Right now I'm using this for backup: pg_dump -U myuser -d mydatabase db.pg.dump for restore: pg_restore -c -r -U myuser -d mydatabase db.pg.dump But I'm not getting the desired results..

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  • gunzip: invalid compressed data--format violated

    - by Arunjith
    Problem definition: I transferred a tar.gz file from a Linux machine to a Windows partition.The Windows partition has mounted with the Linux server as cifs. OS : Red Hat Enterprise Linux Server release 5 Symptom: After the copy process is successful, doing an integrity check with gunzip -t and the process get the following error: gunzip -t Backup-28--Jun--2011--Tuesday.tar.gz gunzip: Backup-28--Jun--2011--Tuesday.tar.gz: invalid compressed data--format violated And further tried to untar (tar -xvzf) and the process as well is failed.

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  • how can i check if a file is well transfered when using sftp ?

    - by achraf
    Hi guys, i'm working on a issue that i have on my sftp server. the issue is that when folder quota is reached and someone try to upload a file, he didnt get any error code and he deposit a 0 bytes file. so i want to know if there's any solution (integrity check for example) to check if the file is well transferred. thanks !!

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  • Full Control Over CD/DVD burning

    - by Hypertext
    Suppose I want to burn just the 6000th byte(if thats not possible say the 10th sector) on a CDR without touching anyother region. Sounds possible but is there any tool to accomplish this. Yeah sure, there may be no clear point doing this but is this possible technically?. (Doesnt matter maintaining the CDFS, integrity or whatever standard here. Just the task.) It would be great to know if there are any software regarding this.

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  • SQL SERVER – Shrinking Database is Bad – Increases Fragmentation – Reduces Performance

    - by pinaldave
    Earlier, I had written two articles related to Shrinking Database. I wrote about why Shrinking Database is not good. SQL SERVER – SHRINKDATABASE For Every Database in the SQL Server SQL SERVER – What the Business Says Is Not What the Business Wants I received many comments on Why Database Shrinking is bad. Today we will go over a very interesting example that I have created for the same. Here are the quick steps of the example. Create a test database Create two tables and populate with data Check the size of both the tables Size of database is very low Check the Fragmentation of one table Fragmentation will be very low Truncate another table Check the size of the table Check the fragmentation of the one table Fragmentation will be very low SHRINK Database Check the size of the table Check the fragmentation of the one table Fragmentation will be very HIGH REBUILD index on one table Check the size of the table Size of database is very HIGH Check the fragmentation of the one table Fragmentation will be very low Here is the script for the same. USE MASTER GO CREATE DATABASE ShrinkIsBed GO USE ShrinkIsBed GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Create FirstTable CREATE TABLE FirstTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_FirstTable_ID] ON FirstTable ( [ID] ASC ) ON [PRIMARY] GO -- Create SecondTable CREATE TABLE SecondTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_SecondTable_ID] ON SecondTable ( [ID] ASC ) ON [PRIMARY] GO -- Insert One Hundred Thousand Records INSERT INTO FirstTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Insert One Hundred Thousand Records INSERT INTO SecondTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO Let us check the table size and fragmentation. Now let us TRUNCATE the table and check the size and Fragmentation. USE MASTER GO CREATE DATABASE ShrinkIsBed GO USE ShrinkIsBed GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Create FirstTable CREATE TABLE FirstTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_FirstTable_ID] ON FirstTable ( [ID] ASC ) ON [PRIMARY] GO -- Create SecondTable CREATE TABLE SecondTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_SecondTable_ID] ON SecondTable ( [ID] ASC ) ON [PRIMARY] GO -- Insert One Hundred Thousand Records INSERT INTO FirstTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Insert One Hundred Thousand Records INSERT INTO SecondTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can clearly see that after TRUNCATE, the size of the database is not reduced and it is still the same as before TRUNCATE operation. After the Shrinking database operation, we were able to reduce the size of the database. If you notice the fragmentation, it is considerably high. The major problem with the Shrink operation is that it increases fragmentation of the database to very high value. Higher fragmentation reduces the performance of the database as reading from that particular table becomes very expensive. One of the ways to reduce the fragmentation is to rebuild index on the database. Let us rebuild the index and observe fragmentation and database size. -- Rebuild Index on FirstTable ALTER INDEX IX_SecondTable_ID ON SecondTable REBUILD GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can notice that after rebuilding, Fragmentation reduces to a very low value (almost same to original value); however the database size increases way higher than the original. Before rebuilding, the size of the database was 5 MB, and after rebuilding, it is around 20 MB. Regular rebuilding the index is rebuild in the same user database where the index is placed. This usually increases the size of the database. Look at irony of the Shrinking database. One person shrinks the database to gain space (thinking it will help performance), which leads to increase in fragmentation (reducing performance). To reduce the fragmentation, one rebuilds index, which leads to size of the database to increase way more than the original size of the database (before shrinking). Well, by Shrinking, one did not gain what he was looking for usually. Rebuild indexing is not the best suggestion as that will create database grow again. I have always remembered the excellent post from Paul Randal regarding Shrinking the database is bad. I suggest every one to read that for accuracy and interesting conversation. Let us run following script where we Shrink the database and REORGANIZE. -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO -- Shrink the Database DBCC SHRINKDATABASE (ShrinkIsBed); GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO -- Rebuild Index on FirstTable ALTER INDEX IX_SecondTable_ID ON SecondTable REORGANIZE GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can see that REORGANIZE does not increase the size of the database or remove the fragmentation. Again, I no way suggest that REORGANIZE is the solution over here. This is purely observation using demo. Read the blog post of Paul Randal. Following script will clean up the database -- Clean up USE MASTER GO ALTER DATABASE ShrinkIsBed SET SINGLE_USER WITH ROLLBACK IMMEDIATE GO DROP DATABASE ShrinkIsBed GO There are few valid cases of the Shrinking database as well, but that is not covered in this blog post. We will cover that area some other time in future. Additionally, one can rebuild index in the tempdb as well, and we will also talk about the same in future. Brent has written a good summary blog post as well. Are you Shrinking your database? Well, when are you going to stop Shrinking it? Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Index, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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  • Is Berkeley DB a NoSQL solution?

    - by Gregory Burd
    Berkeley DB is a library. To use it to store data you must link the library into your application. You can use most programming languages to access the API, the calls across these APIs generally mimic the Berkeley DB C-API which makes perfect sense because Berkeley DB is written in C. The inspiration for Berkeley DB was the DBM library, a part of the earliest versions of UNIX written by AT&T's Ken Thompson in 1979. DBM was a simple key/value hashtable-based storage library. In the early 1990s as BSD UNIX was transitioning from version 4.3 to 4.4 and retrofitting commercial code owned by AT&T with unencumbered code, it was the future founders of Sleepycat Software who wrote libdb (aka Berkeley DB) as the replacement for DBM. The problem it addressed was fast, reliable local key/value storage. At that time databases almost always lived on a single node, even the most sophisticated databases only had simple fail-over two node solutions. If you had a lot of data to store you would choose between the few commercial RDBMS solutions or to write your own custom solution. Berkeley DB took the headache out of the custom approach. These basic market forces inspired other DBM implementations. There was the "New DBM" (ndbm) and the "GNU DBM" (GDBM) and a few others, but the theme was the same. Even today TokyoCabinet calls itself "a modern implementation of DBM" mimicking, and improving on, something first created over thirty years ago. In the mid-1990s, DBM was the name for what you needed if you were looking for fast, reliable local storage. Fast forward to today. What's changed? Systems are connected over fast, very reliable networks. Disks are cheep, fast, and capable of storing huge amounts of data. CPUs continued to follow Moore's Law, processing power that filled a room in 1990 now fits in your pocket. PCs, servers, and other computers proliferated both in business and the personal markets. In addition to the new hardware entire markets, social systems, and new modes of interpersonal communication moved onto the web and started evolving rapidly. These changes cause a massive explosion of data and a need to analyze and understand that data. Taken together this resulted in an entirely different landscape for database storage, new solutions were needed. A number of novel solutions stepped up and eventually a category called NoSQL emerged. The new market forces inspired the CAP theorem and the heated debate of BASE vs. ACID. But in essence this was simply the market looking at what to trade off to meet these new demands. These new database systems shared many qualities in common. There were designed to address massive amounts of data, millions of requests per second, and scale out across multiple systems. The first large-scale and successful solution was Dynamo, Amazon's distributed key/value database. Dynamo essentially took the next logical step and added a twist. Dynamo was to be the database of record, it would be distributed, data would be partitioned across many nodes, and it would tolerate failure by avoiding single points of failure. Amazon did this because they recognized that the majority of the dynamic content they provided to customers visiting their web store front didn't require the services of an RDBMS. The queries were simple, key/value look-ups or simple range queries with only a few queries that required more complex joins. They set about to use relational technology only in places where it was the best solution for the task, places like accounting and order fulfillment, but not in the myriad of other situations. The success of Dynamo, and it's design, inspired the next generation of Non-SQL, distributed database solutions including Cassandra, Riak and Voldemort. The problem their designers set out to solve was, "reliability at massive scale" so the first focal point was distributed database algorithms. Underneath Dynamo there is a local transactional database; either Berkeley DB, Berkeley DB Java Edition, MySQL or an in-memory key/value data structure. Dynamo was an evolution of local key/value storage onto networks. Cassandra, Riak, and Voldemort all faced similar design decisions and one, Voldemort, choose Berkeley DB Java Edition for it's node-local storage. Riak at first was entirely in-memory, but has recently added write-once, append-only log-based on-disk storage similar type of storage as Berkeley DB except that it is based on a hash table which must reside entirely in-memory rather than a btree which can live in-memory or on disk. Berkeley DB evolved too, we added high availability (HA) and a replication manager that makes it easy to setup replica groups. Berkeley DB's replication doesn't partitioned the data, every node keeps an entire copy of the database. For consistency, there is a single node where writes are committed first - a master - then those changes are delivered to the replica nodes as log records. Applications can choose to wait until all nodes are consistent, or fire and forget allowing Berkeley DB to eventually become consistent. Berkeley DB's HA scales-out quite well for read-intensive applications and also effectively eliminates the central point of failure by allowing replica nodes to be elected (using a PAXOS algorithm) to mastership if the master should fail. This implementation covers a wide variety of use cases. MemcacheDB is a server that implements the Memcache network protocol but uses Berkeley DB for storage and HA to replicate the cache state across all the nodes in the cache group. Google Accounts, the user authentication layer for all Google properties, was until recently running Berkeley DB HA. That scaled to a globally distributed system. That said, most NoSQL solutions try to partition (shard) data across nodes in the replication group and some allow writes as well as reads at any node, Berkeley DB HA does not. So, is Berkeley DB a "NoSQL" solution? Not really, but it certainly is a component of many of the existing NoSQL solutions out there. Forgetting all the noise about how NoSQL solutions are complex distributed databases when you boil them down to a single node you still have to store the data to some form of stable local storage. DBMs solved that problem a long time ago. NoSQL has more to do with the layers on top of the DBM; the distributed, sometimes-consistent, partitioned, scale-out storage that manage key/value or document sets and generally have some form of simple HTTP/REST-style network API. Does Berkeley DB do that? Not really. Is Berkeley DB a "NoSQL" solution today? Nope, but it's the most robust solution on which to build such a system. Re-inventing the node-local data storage isn't easy. A lot of people are starting to come to appreciate the sophisticated features found in Berkeley DB, even mimic them in some cases. Could Berkeley DB grow into a NoSQL solution? Absolutely. Our key/value API could be extended over the net using any of a number of existing network protocols such as memcache or HTTP/REST. We could adapt our node-local data partitioning out over replicated nodes. We even have a nice query language and cost-based query optimizer in our BDB XML product that we could reuse were we to build out a document-based NoSQL-style product. XML and JSON are not so different that we couldn't adapt one to work with the other interchangeably. Without too much effort we could add what's missing, we could jump into this No SQL market withing a single product development cycle. Why isn't Berkeley DB already a NoSQL solution? Why aren't we working on it? Why indeed...

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  • MySQL – Scalability on Amazon RDS: Scale out to multiple RDS instances

    - by Pinal Dave
    Today, I’d like to discuss getting better MySQL scalability on Amazon RDS. The question of the day: “What can you do when a MySQL database needs to scale write-intensive workloads beyond the capabilities of the largest available machine on Amazon RDS?” Let’s take a look. In a typical EC2/RDS set-up, users connect to app servers from their mobile devices and tablets, computers, browsers, etc.  Then app servers connect to an RDS instance (web/cloud services) and in some cases they might leverage some read-only replicas.   Figure 1. A typical RDS instance is a single-instance database, with read replicas.  This is not very good at handling high write-based throughput. As your application becomes more popular you can expect an increasing number of users, more transactions, and more accumulated data.  User interactions can become more challenging as the application adds more sophisticated capabilities. The result of all this positive activity: your MySQL database will inevitably begin to experience scalability pressures. What can you do? Broadly speaking, there are four options available to improve MySQL scalability on RDS. 1. Larger RDS Instances – If you’re not already using the maximum available RDS instance, you can always scale up – to larger hardware.  Bigger CPUs, more compute power, more memory et cetera. But the largest available RDS instance is still limited.  And they get expensive. “High-Memory Quadruple Extra Large DB Instance”: 68 GB of memory 26 ECUs (8 virtual cores with 3.25 ECUs each) 64-bit platform High I/O Capacity Provisioned IOPS Optimized: 1000Mbps 2. Provisioned IOPs – You can get provisioned IOPs and higher throughput on the I/O level. However, there is a hard limit with a maximum instance size and maximum number of provisioned IOPs you can buy from Amazon and you simply cannot scale beyond these hardware specifications. 3. Leverage Read Replicas – If your application permits, you can leverage read replicas to offload some reads from the master databases. But there are a limited number of replicas you can utilize and Amazon generally requires some modifications to your existing application. And read-replicas don’t help with write-intensive applications. 4. Multiple Database Instances – Amazon offers a fourth option: “You can implement partitioning,thereby spreading your data across multiple database Instances” (Link) However, Amazon does not offer any guidance or facilities to help you with this. “Multiple database instances” is not an RDS feature.  And Amazon doesn’t explain how to implement this idea. In fact, when asked, this is the response on an Amazon forum: Q: Is there any documents that describe the partition DB across multiple RDS? I need to use DB with more 1TB but exist a limitation during the create process, but I read in the any FAQ that you need to partition database, but I don’t find any documents that describe it. A: “DB partitioning/sharding is not an official feature of Amazon RDS or MySQL, but a technique to scale out database by using multiple database instances. The appropriate way to split data depends on the characteristics of the application or data set. Therefore, there is no concrete and specific guidance.” So now what? The answer is to scale out with ScaleBase. Amazon RDS with ScaleBase: What you get – MySQL Scalability! ScaleBase is specifically designed to scale out a single MySQL RDS instance into multiple MySQL instances. Critically, this is accomplished with no changes to your application code.  Your application continues to “see” one database.   ScaleBase does all the work of managing and enforcing an optimized data distribution policy to create multiple MySQL instances. With ScaleBase, data distribution, transactions, concurrency control, and two-phase commit are all 100% transparent and 100% ACID-compliant, so applications, services and tooling continue to interact with your distributed RDS as if it were a single MySQL instance. The result: now you can cost-effectively leverage multiple MySQL RDS instance to scale out write-intensive workloads to an unlimited number of users, transactions, and data. Amazon RDS with ScaleBase: What you keep – Everything! And how does this change your Amazon environment? 1. Keep your application, unchanged – There is no change your application development life-cycle at all.  You still use your existing development tools, frameworks and libraries.  Application quality assurance and testing cycles stay the same. And, critically, you stay with an ACID-compliant MySQL environment. 2. Keep your RDS value-added services – The value-added services that you rely on are all still available. Amazon will continue to handle database maintenance and updates for you. You can still leverage High Availability via Multi A-Z.  And, if it benefits youra application throughput, you can still use read replicas. 3. Keep your RDS administration – Finally the RDS monitoring and provisioning tools you rely on still work as they did before. With your one large MySQL instance, now split into multiple instances, you can actually use less expensive, smallersmaller available RDS hardware and continue to see better database performance. Conclusion Amazon RDS is a tremendous service, but it doesn’t offer solutions to scale beyond a single MySQL instance. Larger RDS instances get more expensive.  And when you max-out on the available hardware, you’re stuck.  Amazon recommends scaling out your single instance into multiple instances for transaction-intensive apps, but offers no services or guidance to help you. This is where ScaleBase comes in to save the day. It gives you a simple and effective way to create multiple MySQL RDS instances, while removing all the complexities typically caused by “DIY” sharding andwith no changes to your applications . With ScaleBase you continue to leverage the AWS/RDS ecosystem: commodity hardware and value added services like read replicas, multi A-Z, maintenance/updates and administration with monitoring tools and provisioning. SCALEBASE ON AMAZON If you’re curious to try ScaleBase on Amazon, it can be found here – Download NOW. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: MySQL, PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • General monitoring for SQL Server Analysis Services using Performance Monitor

    - by Testas
    A recent customer engagement required a setup of a monitoring solution for SSAS, due to the time restrictions placed upon this, native Windows Performance Monitor (Perfmon) and SQL Server Profiler Monitoring Tools was used as using a third party tool would have meant the customer providing an additional monitoring server that was not available.I wanted to outline the performance monitoring counters that was used to monitor the system on which SSAS was running. Due to the slow query performance that was occurring during certain scenarios, perfmon was used to establish if any pressure was being placed on the Disk, CPU or Memory subsystem when concurrent connections access the same query, and Profiler to pinpoint how the query was being managed within SSAS, profiler I will leave for another blogThis guide is not designed to provide a definitive list of what should be used when monitoring SSAS, different situations may require the addition or removal of counters as presented by the situation. However I hope that it serves as a good basis for starting your monitoring of SSAS. I would also like to acknowledge Chris Webb’s awesome chapters from “Expert Cube Development” that also helped shape my monitoring strategy:http://cwebbbi.spaces.live.com/blog/cns!7B84B0F2C239489A!6657.entrySimulating ConnectionsTo simulate the additional connections to the SSAS server whilst monitoring, I used ascmd to simulate multiple connections to the typical and worse performing queries that were identified by the customer. A similar sript can be downloaded from codeplex at http://www.codeplex.com/SQLSrvAnalysisSrvcs.     File name: ASCMD_StressTestingScripts.zip. Performance MonitorWithin performance monitor,  a counter log was created that contained the list of counters below. The important point to note when running the counter log is that the RUN AS property within the counter log properties should be changed to an account that has rights to the SSAS instance when monitoring MSAS counters. Failure to do so means that the counter log runs under the system account, no errors or warning are given while running the counter log, and it is not until you need to view the MSAS counters that they will not be displayed if run under the default account that has no right to SSAS. If your connection simulation takes hours, this could prove quite frustrating if not done beforehand JThe counters used……  Object Counter Instance Justification System Processor Queue legnth N/A Indicates how many threads are waiting for execution against the processor. If this counter is consistently higher than around 5 when processor utilization approaches 100%, then this is a good indication that there is more work (active threads) available (ready for execution) than the machine's processors are able to handle. System Context Switches/sec N/A Measures how frequently the processor has to switch from user- to kernel-mode to handle a request from a thread running in user mode. The heavier the workload running on your machine, the higher this counter will generally be, but over long term the value of this counter should remain fairly constant. If this counter suddenly starts increasing however, it may be an indicating of a malfunctioning device, especially if the Processor\Interrupts/sec\(_Total) counter on your machine shows a similar unexplained increase Process % Processor Time sqlservr Definately should be used if Processor\% Processor Time\(_Total) is maxing at 100% to assess the effect of the SQL Server process on the processor Process % Processor Time msmdsrv Definately should be used if Processor\% Processor Time\(_Total) is maxing at 100% to assess the effect of the SQL Server process on the processor Process Working Set sqlservr If the Memory\Available bytes counter is decreaing this counter can be run to indicate if the process is consuming larger and larger amounts of RAM. Process(instance)\Working Set measures the size of the working set for each process, which indicates the number of allocated pages the process can address without generating a page fault. Process Working Set msmdsrv If the Memory\Available bytes counter is decreaing this counter can be run to indicate if the process is consuming larger and larger amounts of RAM. Process(instance)\Working Set measures the size of the working set for each process, which indicates the number of allocated pages the process can address without generating a page fault. Processor % Processor Time _Total and individual cores measures the total utilization of your processor by all running processes. If multi-proc then be mindful only an average is provided Processor % Privileged Time _Total To see how the OS is handling basic IO requests. If kernel mode utilization is high, your machine is likely underpowered as it's too busy handling basic OS housekeeping functions to be able to effectively run other applications. Processor % User Time _Total To see how the applications is interacting from a processor perspective, a high percentage utilisation determine that the server is dealing with too many apps and may require increasing thje hardware or scaling out Processor Interrupts/sec _Total  The average rate, in incidents per second, at which the processor received and serviced hardware interrupts. Shoulr be consistant over time but a sudden unexplained increase could indicate a device malfunction which can be confirmed using the System\Context Switches/sec counter Memory Pages/sec N/A Indicates the rate at which pages are read from or written to disk to resolve hard page faults. This counter is a primary indicator of the kinds of faults that cause system-wide delays, this is the primary counter to watch for indication of possible insufficient RAM to meet your server's needs. A good idea here is to configure a perfmon alert that triggers when the number of pages per second exceeds 50 per paging disk on your system. May also want to see the configuration of the page file on the Server Memory Available Mbytes N/A is the amount of physical memory, in bytes, available to processes running on the computer. if this counter is greater than 10% of the actual RAM in your machine then you probably have more than enough RAM. monitor it regularly to see if any downward trend develops, and set an alert to trigger if it drops below 2% of the installed RAM. Physical Disk Disk Transfers/sec for each physical disk If it goes above 10 disk I/Os per second then you've got poor response time for your disk. Physical Disk Idle Time _total If Disk Transfers/sec is above  25 disk I/Os per second use this counter. which measures the percent time that your hard disk is idle during the measurement interval, and if you see this counter fall below 20% then you've likely got read/write requests queuing up for your disk which is unable to service these requests in a timely fashion. Physical Disk Disk queue legnth For the OLAP and SQL physical disk A value that is consistently less than 2 means that the disk system is handling the IO requests against the physical disk Network Interface Bytes Total/sec For the NIC Should be monitored over a period of time to see if there is anb increase/decrease in network utilisation Network Interface Current Bandwidth For the NIC is an estimate of the current bandwidth of the network interface in bits per second (BPS). MSAS 2005: Memory Memory Limit High KB N/A Shows (as a percentage) the high memory limit configured for SSAS in C:\Program Files\Microsoft SQL Server\MSAS10.MSSQLSERVER\OLAP\Config\msmdsrv.ini MSAS 2005: Memory Memory Limit Low KB N/A Shows (as a percentage) the low memory limit configured for SSAS in C:\Program Files\Microsoft SQL Server\MSAS10.MSSQLSERVER\OLAP\Config\msmdsrv.ini MSAS 2005: Memory Memory Usage KB N/A Displays the memory usage of the server process. MSAS 2005: Memory File Store KB N/A Displays the amount of memory that is reserved for the Cache. Note if total memory limit in the msmdsrv.ini is set to 0, no memory is reserved for the cache MSAS 2005: Storage Engine Query Queries from Cache Direct / sec N/A Displays the rate of queries answered from the cache directly MSAS 2005: Storage Engine Query Queries from Cache Filtered / Sec N/A Displays the Rate of queries answered by filtering existing cache entry. MSAS 2005: Storage Engine Query Queries from File / Sec N/A Displays the Rate of queries answered from files. MSAS 2005: Storage Engine Query Average time /query N/A Displays the average time of a query MSAS 2005: Connection Current connections N/A Displays the number of connections against the SSAS instance MSAS 2005: Connection Requests / sec N/A Displays the rate of query requests per second MSAS 2005: Locks Current Lock Waits N/A Displays thhe number of connections waiting on a lock MSAS 2005: Threads Query Pool job queue Length N/A The number of queries in the job queue MSAS 2005:Proc Aggregations Temp file bytes written/sec N/A Shows the number of bytes of data processed in a temporary file MSAS 2005:Proc Aggregations Temp file rows written/sec N/A Shows the number of bytes of data processed in a temporary file 

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  • Oracle Expands Sun Blade Portfolio for Cloud and Highly Virtualized Environments

    - by Ferhat Hatay
    Oracle announced the expansion of Sun Blade Portfolio for cloud and highly virtualized environments that deliver powerful performance and simplified management as tightly integrated systems.  Along with the SPARC T3-1B blade server, Oracle VM blade cluster reference configuration and Oracle's optimized solution for Oracle WebLogic Suite, Oracle introduced the dual-node Sun Blade X6275 M2 server module with some impressive benchmark results.   Benchmarks on the Sun Blade X6275 M2 server module demonstrate the outstanding performance characteristics critical for running varied commercial applications used in cloud and highly virtualized environments.  These include best-in-class SPEC CPU2006 results with the Intel Xeon processor 5600 series, six Fluent world records and 1.8 times the price-performance of the IBM Power 755 running NAMD, a prominent bio-informatics workload.   Benchmarks for Sun Blade X6275 M2 server module  SPEC CPU2006  The Sun Blade X6275 M2 server module demonstrated best in class SPECint_rate2006 results for all published results using the Intel Xeon processor 5600 series, with a result of 679.  This result is 97% better than the HP BL460c G7 blade, 80% better than the IBM HS22V blade, and 79% better than the Dell M710 blade.  This result demonstrates the density advantage of the new Oracle's server module for space-constrained data centers.     Sun Blade X6275M2 (2 Nodes, Intel Xeon X5670 2.93GHz) - 679 SPECint_rate2006; HP ProLiant BL460c G7 (2.93 GHz, Intel Xeon X5670) - 347 SPECint_rate2006; IBM BladeCenter HS22V (Intel Xeon X5680)  - 377 SPECint_rate2006; Dell PowerEdge M710 (Intel Xeon X5680, 3.33 GHz) - 380 SPECint_rate2006.  SPEC, SPECint, SPECfp reg tm of Standard Performance Evaluation Corporation. Results from www.spec.org as of 11/24/2010 and this report.    For more specifics about these results, please go to see http://blogs.sun.com/BestPerf   Fluent The Sun Fire X6275 M2 server module produced world-record results on each of the six standard cases in the current "FLUENT 12" benchmark test suite at 8-, 12-, 24-, 32-, 64- and 96-core configurations. These results beat the most recent QLogic score with IBM DX 360 M series platforms and QLogic "Truescale" interconnects.  Results on sedan_4m test case on the Sun Blade X6275 M2 server module are 23% better than the HP C7000 system, and 20% better than the IBM DX 360 M2; Dell has not posted a result for this test case.  Results can be found at the FLUENT website.   ANSYS's FLUENT software solves fluid flow problems, and is based on a numerical technique called computational fluid dynamics (CFD), which is used in the automotive, aerospace, and consumer products industries. The FLUENT 12 benchmark test suite consists of seven models that are well suited for multi-node clustered environments and representative of modern engineering CFD clusters. Vendors benchmark their systems with the principal objective of providing comparative performance information for FLUENT software that, among other things, depends on compilers, optimization, interconnect, and the performance characteristics of the hardware.   FLUENT application performance is representative of other commercial applications that require memory and CPU resources to be available in a scalable cluster-ready format.  FLUENT benchmark has six conventional test cases (eddy_417k, turbo_500k, aircraft_2m, sedan_4m, truck_14m, truck_poly_14m) at various core counts.   All information on the FLUENT website (http://www.fluent.com) is Copyrighted1995-2010 by ANSYS Inc. Results as of November 24, 2010. For more specifics about these results, please go to see http://blogs.sun.com/BestPerf   NAMD Results on the Sun Blade X6275 M2 server module running NAMD (a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems) show up to a 1.8X better price/performance than IBM's Power 7-based system.  For space-constrained environments, the ultra-dense Sun Blade X6275 M2 server module provides a 1.7X better price/performance per rack unit than IBM's system.     IBM Power 755 4-way Cluster (16U). Total price for cluster: $324,212. See IBM United States Hardware Announcement 110-008, dated February 9, 2010, pp. 4, 21 and 39-46.  Sun Blade X6275 M2 8-Blade Cluster (10U). Total price for cluster:  $193,939. Price/performance and performance/RU comparisons based on f1ATPase molecule test results. Sun Blade X6275 M2 cluster: $3,568/step/sec, 5.435 step/sec/RU. IBM Power 755 cluster: $6,355/step/sec, 3.189 step/sec/U. See http://www-03.ibm.com/systems/power/hardware/reports/system_perf.html. See http://www.ks.uiuc.edu/Research/namd/performance.html for more information, results as of 11/24/10.   For more specifics about these results, please go to see http://blogs.sun.com/BestPerf   Reverse Time Migration The Reverse Time Migration is heavily used in geophysical imaging and modeling for Oil & Gas Exploration.  The Sun Blade X6275 M2 server module showed up to a 40% performance improvement over the previous generation server module with super-linear scalability to 16 nodes for the 9-Point Stencil used in this Reverse Time Migration computational kernel.  The balanced combination of Oracle's Sun Storage 7410 system with the Sun Blade X6275 M2 server module cluster showed linear scalability for the total application throughput, including the I/O and MPI communication, to produce a final 3-D seismic depth imaged cube for interpretation. The final image write time from the Sun Blade X6275 M2 server module nodes to Oracle's Sun Storage 7410 system achieved 10GbE line speed of 1.25 GBytes/second or better performance. Between subsequent runs, the effects of I/O buffer caching on the Sun Blade X6275 M2 server module nodes and write optimized caching on the Sun Storage 7410 system gave up to 1.8 GBytes/second effective write performance. The performance results and characterization of this Reverse Time Migration benchmark could serve as a useful measure for many other I/O intensive commercial applications. 3D VTI Reverse Time Migration Seismic Depth Imaging, see http://blogs.sun.com/BestPerf/entry/3d_vti_reverse_time_migration for more information, results as of 11/14/2010.                            

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  • How to Achieve OC4J RMI Load Balancing

    - by fip
    This is an old, Oracle SOA and OC4J 10G topic. In fact this is not even a SOA topic per se. Questions of RMI load balancing arise when you developed custom web applications accessing human tasks running off a remote SOA 10G cluster. Having returned from a customer who faced challenges with OC4J RMI load balancing, I felt there is still some confusions in the field how OC4J RMI load balancing work. Hence I decide to dust off an old tech note that I wrote a few years back and share it with the general public. Here is the tech note: Overview A typical use case in Oracle SOA is that you are building web based, custom human tasks UI that will interact with the task services housed in a remote BPEL 10G cluster. Or, in a more generic way, you are just building a web based application in Java that needs to interact with the EJBs in a remote OC4J cluster. In either case, you are talking to an OC4J cluster as RMI client. Then immediately you must ask yourself the following questions: 1. How do I make sure that the web application, as an RMI client, even distribute its load against all the nodes in the remote OC4J cluster? 2. How do I make sure that the web application, as an RMI client, is resilient to the node failures in the remote OC4J cluster, so that in the unlikely case when one of the remote OC4J nodes fail, my web application will continue to function? That is the topic of how to achieve load balancing with OC4J RMI client. Solutions You need to configure and code RMI load balancing in two places: 1. Provider URL can be specified with a comma separated list of URLs, so that the initial lookup will land to one of the available URLs. 2. Choose a proper value for the oracle.j2ee.rmi.loadBalance property, which, along side with the PROVIDER_URL property, is one of the JNDI properties passed to the JNDI lookup.(http://docs.oracle.com/cd/B31017_01/web.1013/b28958/rmi.htm#BABDGFBI) More details below: About the PROVIDER_URL The JNDI property java.name.provider.url's job is, when the client looks up for a new context at the very first time in the client session, to provide a list of RMI context The value of the JNDI property java.name.provider.url goes by the format of a single URL, or a comma separate list of URLs. A single URL. For example: opmn:ormi://host1:6003:oc4j_instance1/appName1 A comma separated list of multiple URLs. For examples:  opmn:ormi://host1:6003:oc4j_instanc1/appName, opmn:ormi://host2:6003:oc4j_instance1/appName, opmn:ormi://host3:6003:oc4j_instance1/appName When the client looks up for a new Context the very first time in the client session, it sends a query against the OPMN referenced by the provider URL. The OPMN host and port specifies the destination of such query, and the OC4J instance name and appName are actually the “where clause” of the query. When the PROVIDER URL reference a single OPMN server Let's consider the case when the provider url only reference a single OPMN server of the destination cluster. In this case, that single OPMN server receives the query and returns a list of the qualified Contexts from all OC4Js within the cluster, even though there is a single OPMN server in the provider URL. A context represent a particular starting point at a particular server for subsequent object lookup. For example, if the URL is opmn:ormi://host1:6003:oc4j_instance1/appName, then, OPMN will return the following contexts: appName on oc4j_instance1 on host1 appName on oc4j_instance1 on host2, appName on oc4j_instance1 on host3,  (provided that host1, host2, host3 are all in the same cluster) Please note that One OPMN will be sufficient to find the list of all contexts from the entire cluster that satisfy the JNDI lookup query. You can do an experiment by shutting down appName on host1, and observe that OPMN on host1 will still be able to return you appname on host2 and appName on host3. When the PROVIDER URL reference a comma separated list of multiple OPMN servers When the JNDI propery java.naming.provider.url references a comma separated list of multiple URLs, the lookup will return the exact same things as with the single OPMN server: a list of qualified Contexts from the cluster. The purpose of having multiple OPMN servers is to provide high availability in the initial context creation, such that if OPMN at host1 is unavailable, client will try the lookup via OPMN on host2, and so on. After the initial lookup returns and cache a list of contexts, the JNDI URL(s) are no longer used in the same client session. That explains why removing the 3rd URL from the list of JNDI URLs will not stop the client from getting the EJB on the 3rd server. About the oracle.j2ee.rmi.loadBalance Property After the client acquires the list of contexts, it will cache it at the client side as “list of available RMI contexts”.  This list includes all the servers in the destination cluster. This list will stay in the cache until the client session (JVM) ends. The RMI load balancing against the destination cluster is happening at the client side, as the client is switching between the members of the list. Whether and how often the client will fresh the Context from the list of Context is based on the value of the  oracle.j2ee.rmi.loadBalance. The documentation at http://docs.oracle.com/cd/B31017_01/web.1013/b28958/rmi.htm#BABDGFBI list all the available values for the oracle.j2ee.rmi.loadBalance. Value Description client If specified, the client interacts with the OC4J process that was initially chosen at the first lookup for the entire conversation. context Used for a Web client (servlet or JSP) that will access EJBs in a clustered OC4J environment. If specified, a new Context object for a randomly-selected OC4J instance will be returned each time InitialContext() is invoked. lookup Used for a standalone client that will access EJBs in a clustered OC4J environment. If specified, a new Context object for a randomly-selected OC4J instance will be created each time the client calls Context.lookup(). Please note the regardless of the setting of oracle.j2ee.rmi.loadBalance property, the “refresh” only occurs at the client. The client can only choose from the "list of available context" that was returned and cached from the very first lookup. That is, the client will merely get a new Context object from the “list of available RMI contexts” from the cache at the client side. The client will NOT go to the OPMN server again to get the list. That also implies that if you are adding a node to the server cluster AFTER the client’s initial lookup, the client would not know it because neither the server nor the client will initiate a refresh of the “list of available servers” to reflect the new node. About High Availability (i.e. Resilience Against Node Failure of Remote OC4J Cluster) What we have discussed above is about load balancing. Let's also discuss high availability. This is how the High Availability works in RMI: when the client use the context but get an exception such as socket is closed, it knows that the server referenced by that Context is problematic and will try to get another unused Context from the “list of available contexts”. Again, this list is the list that was returned and cached at the very first lookup in the entire client session.

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  • People, Process & Engagement: WebCenter Partner Keste

    - by Michael Snow
    v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Within the WebCenter group here at Oracle, discussions about people, process and engagement cross over many vertical industries and products. Amidst our growing partner ecosystem, the community provides us insight into great customer use cases every day. Such is the case with our partner, Keste, who provides us a guest post on our blog today with an overview of their innovative solution for a customer in the transportation industry. Keste is an Oracle software solutions and development company headquartered in Dallas, Texas. As a Platinum member of the Oracle® PartnerNetwork, Keste designs, develops and deploys custom solutions that automate complex business processes. Seamless Customer Self-Service Experience in the Trucking Industry with Oracle WebCenter Portal  Keste, Oracle Platinum Partner Customer Overview Omnitracs, Inc., a Qualcomm company provides mobility solutions for trucking fleets to companies in the transportation industry. Omnitracs’ mobility services include basic communications such as text as well as advanced monitoring services such as GPS tracking, temperature tracking of perishable goods, load tracking and weighting distribution, and many others. Customer Business Needs Already the leading provider of mobility solutions for large trucking fleets, they chose to target smaller trucking fleets as new customers. However their existing high-touch customer support method would not be a cost effective or scalable method to manage and service these smaller customers. Omnitracs needed to provide several self-service features to make customer support more scalable while keeping customer satisfaction levels high and the costs manageable. The solution also had to be very intuitive and easy to use. The systems that Omnitracs sells to these trucking customers require professional installation and smaller customers need to track and schedule the installation. Information captured in Oracle eBusiness Suite needed to be readily available for new customers to track these purchases and delivery details. Omnitracs wanted a high impact User Interface to significantly improve customer experience with the ability to integrate with EBS, provisioning systems as well as CRM systems that were already implemented. Omnitracs also wanted to build an architecture platform that could potentially be extended to other Portals. Omnitracs’ stated goal was to deliver an “eBay-like” or “Amazon-like” experience for all of their customers so that they could reach a much broader market beyond their large company customer base. Solution Overview In order to manage the increased complexity, the growing support needs of global customers and improve overall product time-to-market in a cost-effective manner, IT began to deliver a self-service model. This self service model not only transformed numerous business processes but is also allowing the business to keep up with the growing demands of the (internal and external) customers. This solution was a customer service Portal that provided self service capabilities for large and small customers alike for Activation of mobility products, managing add-on applications for the devices (much like the Apple App Store), transferring services when trucks are sold to other companies as well as deactivation all without the involvement of a call service agent or sending multiple emails to different Omnitracs contacts. This is a conceptual view of the Customer Portal showing the details of the components that make up the solution. 12.00 The portal application for transactions was entirely built using ADF 11g R2. Omnitracs’ business had a pressing requirement to have a portal available 24/7 for its customers. Since there were interactions with EBS in the back-end, the downtimes on the EBS would negate this availability. Omnitracs devised a decoupling strategy at the database side for the EBS data. The decoupling of the database was done using Oracle Data Guard and completely insulated the solution from any eBusiness Suite down time. The customer has no knowledge whether eBS is running or not. Here are two sample screenshots of the portal application built in Oracle ADF. Customer Benefits The Customer Portal not only provided the scalability to grow the business but also provided the seamless integration with other disparate applications. Some of the key benefits are: Improved Customer Experience: With a modern look and feel and a Portal that has the aspects of an App Store, the customer experience was significantly improved. Page response times went from several seconds to sub-second for all of the pages. Enabled new product launches: After successfully dominating the large fleet market, Omnitracs now has a scalable solution to sell and manage smaller fleet customers giving them a huge advantage over their nearest competitors. Dozens of new customers have been acquired via this portal through an onboarding process that now takes minutes Seamless Integrations Improves Customer Support: ADF 11gR2 allowed Omnitracs to bring a diverse list of applications into one integrated solution. This provided a seamless experience for customers to route them from Marketing focused application to a customer-oriented portal. Internally, it also allowed Sales Representatives to have an integrated flow for taking a prospect through the various steps to onboard them as a customer. Key integrations included: Unity Core Salesforce.com Merchant e-Solution for credit card Custom Omnitracs Applications like CUPS and AUTO Security utilizing OID and OVD Back end integration with EBS (Data Guard) and iQ Database Business Impact Significant business impacts were realized through the launch of customer portal. It not only allows the business to push through in underserved segments, but also reduces the time it needs to spend on customer support—allowing the business to focus more on sales and identifying the market for new products. Some of the Immediate Benefits are The entire onboarding process is now completely automated and now completes in minutes. This represents an 85% productivity improvement over their previous processes. And it was 160 times faster! With the success of this self-service solution, the business is now targeting about 3X customer growth in the next five years. This represents a tripling of their overall customer base and significant downstream revenue for the ongoing services. 90%+ improvement of customer onboarding and management process by utilizing, single sign on integration using OID/OAM solution, performance improvements and new self-service functionality Unified login for all Customers, Partners and Internal Users enables login to a common portal and seamless access to all other integrated applications targeted at the respective audience Significantly improved customer experience with a better look and feel with a more user experience focused Portal screens. Helped sales of the new product by having an easy way of ordering and activating the product. Data Guard helped increase availability of the Portal to 99%+ and make it independent of EBS downtime. This gave customers the feel of high availability of the portal application. Some of the anticipated longer term Benefits are: Platform that can be leveraged to launch any new product introduction and enable all product teams to reach new customers and new markets Easy integration with content management to allow business owners more control of the product catalog Overall reduced TCO with standardization of the Oracle platform Managed IT support cost savings through optimization of technology skills needed to support and modify this solution ------------------------------------------------------------ 12.00 Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 -"/ /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-family:"Times New Roman","serif";}

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  • Reviewing Retail Predictions for 2011

    - by David Dorf
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} I've been busy thinking about what 2012 and beyond will look like for retail, and I have some interesting predictions to share.  But before I go there, let’s first review this year’s predictions before making new ones for 2012. 1. Alternate Payments We've seen several alternate payment schemes emerge over the last two years, and 2011 may be the year one of them takes hold. Any competition that can drive down fees will be good for everyone. I'm betting that Apple will add NFC chips to their next version of the iPhone, then enable payments in stores using iTunes accounts on the backend. Paypal will continue to make inroads, and Isis will announce a pilot. The iPhone 4S did not contain an NFC chip, so we’ll have to continuing waiting for the iPhone 5. PayPal announced its moving into in-store payments, and Google launched its wallet in selected cities.  Overall I think the payment scene is heating up and that trend will continue. 2. Engineered Systems The industry is moving toward purpose-built appliances that are optimized across the entire stack. Oracle calls these "engineered systems" and the first two examples are Exadata and Exalogic, but there are other examples from other vendors. These are particularly important to the retail industry because of the volume of data that must be processed. There should be continued adoption in 2011. Oracle reports that Exadata is its fasting growing product, and at the recent OpenWorld it announced the SuperCluster and Exalytics products, both continuing the engineered systems trend. SAP’s HANA continues to receive attention, and IBM also seems to be moving in this direction. 3. Social Analytics There are lots of tools that provide insight into how a brand is perceived across popular internet sites, but as far as I know, these tools are not industry specific. The next step needs to mine the data and determine how it should influence retail operations. The data needs to help retailers determine how they create promotions, which products to stock, and how to keep consumers engaged. Social data alone does not provide the answers, but its one more data point that will help retailers make better decisions. Look for some vendor consolidation to help make this happen. In March, Salesforce.com acquired leading social monitoring vendor Radian6 and followed up with acquisitions of Heroku and Model Metrics. The notion of Social CRM seems to be going more mainstream now. 4. 2-D Barcodes Look for more QRCodes on shelf-tags, in newspaper circulars, and on billboards. It's a great portal from the physical world into the digital one that buys us time until augmented reality matures further. Nobody wants to type "www", backslash, and ".com" on their phones. QRCodes are everywhere. ‘Nuff said. 5. In the words of Microsoft, "To the Cloud!" My favorite "cloud application" is Evernote. If you take notes on your work laptop, you will inevitably need those notes on your home PC. And if you manage to solve that problem, you'll need to access them from your mobile phone. Evernote stores your notes in the cloud and provides easy ways to access them. Being able to access a service from anywhere and not having to worry about backups, upgrades, etc. is great. Retailers will start to rely on cloud services, both public and private, in the coming year. There were no shortage of announcements in this area: Amazon’s cloud-based Kindle Fire, Apple’s iCloud, Oracle’s Public Cloud, etc. I saw an interesting presentation showing how BevMo moved their systems to the cloud.  Seems like retailers are starting to consider the cloud for specific uses. 6. F-CommerceTop of Form Move over "E" and "M" so we can introduce "F-Commerce," which should go mainstream in 2011. Already several retailers have created small stores on Facebook, and it won't be long before Facebook becomes a full-fledged channel in the omni-channel world of retail. The battle between Facebook and Google will heat up over retail, where both stand to make lots of money. JCPenney and ASOS both put their entire catalogs on Facebook, and lots of other retailers have connected Facebook to their e-commerce site. I still think selling from the newsfeed is the best approach, and several retailers are trying that approach as well. I just don’t see Google+ as a threat to Facebook, so I think that battle is over.  I called 2011 The Year of F-Commerce, and that was probably accurate. Its good to look back at predictions, but we also have to think about what was missed.  I didn't see Amazon entering the tablet business with such a splash, although in hindsight it was obvious. Nor did I think HP would fall so far so fast.  Look for my 2012 predictions coming soon.

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  • Best Practices - which domain types should be used to run applications

    - by jsavit
    This post is one of a series of "best practices" notes for Oracle VM Server for SPARC (formerly named Logical Domains) One question that frequently comes up is "which types of domain should I use to run applications?" There used to be a simple answer in most cases: "only run applications in guest domains", but enhancements to T-series servers, Oracle VM Server for SPARC and the advent of SPARC SuperCluster have made this question more interesting and worth qualifying differently. This article reviews the relevant concepts and provides suggestions on where to deploy applications in a logical domains environment. Review: division of labor and types of domain Oracle VM Server for SPARC offloads many functions from the hypervisor to domains (also called virtual machines). This is a modern alternative to using a "thick" hypervisor that provides all virtualization functions, as in traditional VM designs, This permits a simpler hypervisor design, which enhances reliability, and security. It also reduces single points of failure by assigning responsibilities to multiple system components, which further improves reliability and security. In this architecture, management and I/O functionality are provided within domains. Oracle VM Server for SPARC does this by defining the following types of domain, each with their own roles: Control domain - management control point for the server, used to configure domains and manage resources. It is the first domain to boot on a power-up, is an I/O domain, and is usually a service domain as well. I/O domain - has been assigned physical I/O devices: a PCIe root complex, a PCI device, or a SR-IOV (single-root I/O Virtualization) function. It has native performance and functionality for the devices it owns, unmediated by any virtualization layer. Service domain - provides virtual network and disk devices to guest domains. Guest domain - a domain whose devices are all virtual rather than physical: virtual network and disk devices provided by one or more service domains. In common practice, this is where applications are run. Typical deployment A service domain is generally also an I/O domain: otherwise it wouldn't have access to physical device "backends" to offer to its clients. Similarly, an I/O domain is also typically a service domain in order to leverage the available PCI busses. Control domains must be I/O domains, because they boot up first on the server and require physical I/O. It's typical for the control domain to also be a service domain too so it doesn't "waste" the I/O resources it uses. A simple configuration consists of a control domain, which is also the one I/O and service domain, and some number of guest domains using virtual I/O. In production, customers typically use multiple domains with I/O and service roles to eliminate single points of failure: guest domains have virtual disk and virtual devices provisioned from more than one service domain, so failure of a service domain or I/O path or device doesn't result in an application outage. This is also used for "rolling upgrades" in which service domains are upgraded one at a time while their guests continue to operate without disruption. (It should be noted that resiliency to I/O device failures can also be provided by the single control domain, using multi-path I/O) In this type of deployment, control, I/O, and service domains are used for virtualization infrastructure, while applications run in guest domains. Changing application deployment patterns The above model has been widely and successfully used, but more configuration options are available now. Servers got bigger than the original T2000 class machines with 2 I/O busses, so there is more I/O capacity that can be used for applications. Increased T-series server capacity made it attractive to run more vertical applications, such as databases, with higher resource requirements than the "light" applications originally seen. This made it attractive to run applications in I/O domains so they could get bare-metal native I/O performance. This is leveraged by the SPARC SuperCluster engineered system, announced a year ago at Oracle OpenWorld. In SPARC SuperCluster, I/O domains are used for high performance applications, with native I/O performance for disk and network and optimized access to the Infiniband fabric. Another technical enhancement is the introduction of Direct I/O (DIO) and Single Root I/O Virtualization (SR-IOV), which make it possible to give domains direct connections and native I/O performance for selected I/O devices. A domain with either a DIO or SR-IOV device is an I/O domain. In summary: not all I/O domains own PCI complexes, and there are increasingly more I/O domains that are not service domains. They use their I/O connectivity for performance for their own applications. However, there are some limitations and considerations: at this time, a domain using physical I/O cannot be live-migrated to another server. There is also a need to plan for security and introducing unneeded dependencies: if an I/O domain is also a service domain providing virtual I/O go guests, it has the ability to affect the correct operation of its client guest domains. This is even more relevant for the control domain. where the ldm has to be protected from unauthorized (or even mistaken) use that would affect other domains. As a general rule, running applications in the service domain or the control domain should be avoided. To recap: Guest domains with virtual I/O still provide the greatest operational flexibility, including features like live migration. I/O domains can be used for applications with high performance requirements. This is used to great effect in SPARC SuperCluster and in general T4 deployments. Direct I/O (DIO) and Single Root I/O Virtualization (SR-IOV) make this more attractive by giving direct I/O access to more domains. Service domains should in general not be used for applications, because compromised security in the domain, or an outage, can affect other domains that depend on it. This concern can be mitigated by providing guests' their virtual I/O from more than one service domain, so an interruption of service in the service domain does not cause an application outage. The control domain should in general not be used to run applications, for the same reason. SPARC SuperCluster use the control domain for applications, but it is an exception: it's not a general purpose environment; it's an engineered system with specifically configured applications and optimization for optimal performance. These are recommended "best practices" based on conversations with a number of Oracle architects. Keep in mind that "one size does not fit all", so you should evaluate these practices in the context of your own requirements. Summary Higher capacity T-series servers have made it more attractive to use them for applications with high resource requirements. New deployment models permit native I/O performance for demanding applications by running them in I/O domains with direct access to their devices. This is leveraged in SPARC SuperCluster, and can be leveraged in T-series servers to provision high-performance applications running in domains. Carefully planned, this can be used to provide higher performance for critical applications.

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  • How to read oom-killer syslog messages?

    - by Grant
    I have a Ubuntu 12.04 server which sometimes dies completely - no SSH, no ping, nothing until it is physically rebooted. After the reboot, I see in syslog that the oom-killer killed, well, pretty much everything. There's a lot of detailed memory usage information in them. How do I read these logs to see what caused the OOM issue? The server has far more memory than it needs, so it shouldn't be running out of memory. Oct 25 07:28:04 nldedip4k031 kernel: [87946.529511] oom_kill_process: 9 callbacks suppressed Oct 25 07:28:04 nldedip4k031 kernel: [87946.529514] irqbalance invoked oom-killer: gfp_mask=0x80d0, order=0, oom_adj=0, oom_score_adj=0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529516] irqbalance cpuset=/ mems_allowed=0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529518] Pid: 948, comm: irqbalance Not tainted 3.2.0-55-generic-pae #85-Ubuntu Oct 25 07:28:04 nldedip4k031 kernel: [87946.529519] Call Trace: Oct 25 07:28:04 nldedip4k031 kernel: [87946.529525] [] dump_header.isra.6+0x85/0xc0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529528] [] oom_kill_process+0x5c/0x80 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529530] [] out_of_memory+0xc5/0x1c0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529532] [] __alloc_pages_nodemask+0x72c/0x740 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529535] [] __get_free_pages+0x1c/0x30 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529537] [] get_zeroed_page+0x12/0x20 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529541] [] fill_read_buffer.isra.8+0xaa/0xd0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529543] [] sysfs_read_file+0x7d/0x90 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529546] [] vfs_read+0x8c/0x160 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529548] [] ? fill_read_buffer.isra.8+0xd0/0xd0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529550] [] sys_read+0x3d/0x70 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529554] [] sysenter_do_call+0x12/0x28 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529555] Mem-Info: Oct 25 07:28:04 nldedip4k031 kernel: [87946.529556] DMA per-cpu: Oct 25 07:28:04 nldedip4k031 kernel: [87946.529557] CPU 0: hi: 0, btch: 1 usd: 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529558] CPU 1: hi: 0, btch: 1 usd: 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529560] CPU 2: hi: 0, btch: 1 usd: 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529561] CPU 3: hi: 0, btch: 1 usd: 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529562] CPU 4: hi: 0, btch: 1 usd: 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529563] CPU 5: hi: 0, btch: 1 usd: 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529564] CPU 6: hi: 0, btch: 1 usd: 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529565] CPU 7: hi: 0, btch: 1 usd: 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529566] Normal per-cpu: Oct 25 07:28:04 nldedip4k031 kernel: [87946.529567] CPU 0: hi: 186, btch: 31 usd: 179 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529568] CPU 1: hi: 186, btch: 31 usd: 182 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529569] CPU 2: hi: 186, btch: 31 usd: 132 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529570] CPU 3: hi: 186, btch: 31 usd: 175 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529571] CPU 4: hi: 186, btch: 31 usd: 91 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529572] CPU 5: hi: 186, btch: 31 usd: 173 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529573] CPU 6: hi: 186, btch: 31 usd: 159 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529574] CPU 7: hi: 186, btch: 31 usd: 164 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529575] HighMem per-cpu: Oct 25 07:28:04 nldedip4k031 kernel: [87946.529576] CPU 0: hi: 186, btch: 31 usd: 165 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529577] CPU 1: hi: 186, btch: 31 usd: 183 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529578] CPU 2: hi: 186, btch: 31 usd: 185 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529579] CPU 3: hi: 186, btch: 31 usd: 138 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529580] CPU 4: hi: 186, btch: 31 usd: 155 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529581] CPU 5: hi: 186, btch: 31 usd: 104 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529582] CPU 6: hi: 186, btch: 31 usd: 133 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529583] CPU 7: hi: 186, btch: 31 usd: 170 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529586] active_anon:5523 inactive_anon:354 isolated_anon:0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529586] active_file:2815 inactive_file:6849119 isolated_file:0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529587] unevictable:0 dirty:449 writeback:10 unstable:0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529587] free:1304125 slab_reclaimable:104672 slab_unreclaimable:3419 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529588] mapped:2661 shmem:138 pagetables:313 bounce:0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529591] DMA free:4252kB min:780kB low:972kB high:1168kB active_anon:0kB inactive_anon:0kB active_file:4kB inactive_file:0kB unevictable:0kB isolated(anon):0kB isolated(file):0kB present:15756kB mlocked:0kB dirty:0kB writeback:0kB mapped:0kB shmem:0kB slab_reclaimable:11564kB slab_unreclaimable:4kB kernel_stack:0kB pagetables:0kB unstable:0kB bounce:0kB writeback_tmp:0kB pages_scanned:1 all_unreclaimable? yes Oct 25 07:28:04 nldedip4k031 kernel: [87946.529594] lowmem_reserve[]: 0 869 32460 32460 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529599] Normal free:44052kB min:44216kB low:55268kB high:66324kB active_anon:0kB inactive_anon:0kB active_file:616kB inactive_file:568kB unevictable:0kB isolated(anon):0kB isolated(file):0kB present:890008kB mlocked:0kB dirty:0kB writeback:0kB mapped:4kB shmem:0kB slab_reclaimable:407124kB slab_unreclaimable:13672kB kernel_stack:992kB pagetables:0kB unstable:0kB bounce:0kB writeback_tmp:0kB pages_scanned:2083 all_unreclaimable? yes Oct 25 07:28:04 nldedip4k031 kernel: [87946.529602] lowmem_reserve[]: 0 0 252733 252733 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529606] HighMem free:5168196kB min:512kB low:402312kB high:804112kB active_anon:22092kB inactive_anon:1416kB active_file:10640kB inactive_file:27395920kB unevictable:0kB isolated(anon):0kB isolated(file):0kB present:32349872kB mlocked:0kB dirty:1796kB writeback:40kB mapped:10640kB shmem:552kB slab_reclaimable:0kB slab_unreclaimable:0kB kernel_stack:0kB pagetables:1252kB unstable:0kB bounce:0kB writeback_tmp:0kB pages_scanned:0 all_unreclaimable? no Oct 25 07:28:04 nldedip4k031 kernel: [87946.529609] lowmem_reserve[]: 0 0 0 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529611] DMA: 6*4kB 6*8kB 6*16kB 5*32kB 5*64kB 4*128kB 2*256kB 1*512kB 0*1024kB 1*2048kB 0*4096kB = 4232kB Oct 25 07:28:04 nldedip4k031 kernel: [87946.529616] Normal: 297*4kB 180*8kB 119*16kB 73*32kB 67*64kB 47*128kB 35*256kB 13*512kB 5*1024kB 1*2048kB 1*4096kB = 44052kB Oct 25 07:28:04 nldedip4k031 kernel: [87946.529622] HighMem: 1*4kB 6*8kB 27*16kB 11*32kB 2*64kB 1*128kB 0*256kB 0*512kB 4*1024kB 1*2048kB 1260*4096kB = 5168196kB Oct 25 07:28:04 nldedip4k031 kernel: [87946.529627] 6852076 total pagecache pages Oct 25 07:28:04 nldedip4k031 kernel: [87946.529628] 0 pages in swap cache Oct 25 07:28:04 nldedip4k031 kernel: [87946.529629] Swap cache stats: add 0, delete 0, find 0/0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529630] Free swap = 3998716kB Oct 25 07:28:04 nldedip4k031 kernel: [87946.529631] Total swap = 3998716kB Oct 25 07:28:04 nldedip4k031 kernel: [87946.571914] 8437743 pages RAM Oct 25 07:28:04 nldedip4k031 kernel: [87946.571916] 8209409 pages HighMem Oct 25 07:28:04 nldedip4k031 kernel: [87946.571917] 159556 pages reserved Oct 25 07:28:04 nldedip4k031 kernel: [87946.571917] 6862034 pages shared Oct 25 07:28:04 nldedip4k031 kernel: [87946.571918] 123540 pages non-shared Oct 25 07:28:04 nldedip4k031 kernel: [87946.571919] [ pid ] uid tgid total_vm rss cpu oom_adj oom_score_adj name Oct 25 07:28:04 nldedip4k031 kernel: [87946.571927] [ 421] 0 421 709 152 3 0 0 upstart-udev-br Oct 25 07:28:04 nldedip4k031 kernel: [87946.571929] [ 429] 0 429 773 326 5 -17 -1000 udevd Oct 25 07:28:04 nldedip4k031 kernel: [87946.571931] [ 567] 0 567 772 224 4 -17 -1000 udevd Oct 25 07:28:04 nldedip4k031 kernel: [87946.571932] [ 568] 0 568 772 231 7 -17 -1000 udevd Oct 25 07:28:04 nldedip4k031 kernel: [87946.571934] [ 764] 0 764 712 103 1 0 0 upstart-socket- Oct 25 07:28:04 nldedip4k031 kernel: [87946.571936] [ 772] 103 772 815 164 5 0 0 dbus-daemon Oct 25 07:28:04 nldedip4k031 kernel: [87946.571938] [ 785] 0 785 1671 600 1 -17 -1000 sshd Oct 25 07:28:04 nldedip4k031 kernel: [87946.571940] [ 809] 101 809 7766 380 1 0 0 rsyslogd Oct 25 07:28:04 nldedip4k031 kernel: [87946.571942] [ 869] 0 869 1158 213 3 0 0 getty Oct 25 07:28:04 nldedip4k031 kernel: [87946.571943] [ 873] 0 873 1158 214 6 0 0 getty Oct 25 07:28:04 nldedip4k031 kernel: [87946.571945] [ 911] 0 911 1158 215 3 0 0 getty Oct 25 07:28:04 nldedip4k031 kernel: [87946.571947] [ 912] 0 912 1158 214 2 0 0 getty Oct 25 07:28:04 nldedip4k031 kernel: [87946.571949] [ 914] 0 914 1158 213 1 0 0 getty Oct 25 07:28:04 nldedip4k031 kernel: [87946.571950] [ 916] 0 916 618 86 1 0 0 atd Oct 25 07:28:04 nldedip4k031 kernel: [87946.571952] [ 917] 0 917 655 226 3 0 0 cron Oct 25 07:28:04 nldedip4k031 kernel: [87946.571954] [ 948] 0 948 902 159 3 0 0 irqbalance Oct 25 07:28:04 nldedip4k031 kernel: [87946.571956] [ 993] 0 993 1145 363 3 0 0 master Oct 25 07:28:04 nldedip4k031 kernel: [87946.571957] [ 1002] 104 1002 1162 333 1 0 0 qmgr Oct 25 07:28:04 nldedip4k031 kernel: [87946.571959] [ 1016] 0 1016 730 149 2 0 0 mdadm Oct 25 07:28:04 nldedip4k031 kernel: [87946.571961] [ 1057] 0 1057 6066 2160 3 0 0 /usr/sbin/apach Oct 25 07:28:04 nldedip4k031 kernel: [87946.571963] [ 1086] 0 1086 1158 213 3 0 0 getty Oct 25 07:28:04 nldedip4k031 kernel: [87946.571965] [ 1088] 33 1088 6191 1517 0 0 0 /usr/sbin/apach Oct 25 07:28:04 nldedip4k031 kernel: [87946.571967] [ 1089] 33 1089 6191 1451 1 0 0 /usr/sbin/apach Oct 25 07:28:04 nldedip4k031 kernel: [87946.571969] [ 1090] 33 1090 6175 1451 3 0 0 /usr/sbin/apach Oct 25 07:28:04 nldedip4k031 kernel: [87946.571971] [ 1091] 33 1091 6191 1451 1 0 0 /usr/sbin/apach Oct 25 07:28:04 nldedip4k031 kernel: [87946.571972] [ 1092] 33 1092 6191 1451 0 0 0 /usr/sbin/apach Oct 25 07:28:04 nldedip4k031 kernel: [87946.571974] [ 1109] 33 1109 6191 1517 0 0 0 /usr/sbin/apach Oct 25 07:28:04 nldedip4k031 kernel: [87946.571976] [ 1151] 33 1151 6191 1451 1 0 0 /usr/sbin/apach Oct 25 07:28:04 nldedip4k031 kernel: [87946.571978] [ 1201] 104 1201 1803 652 1 0 0 tlsmgr Oct 25 07:28:04 nldedip4k031 kernel: [87946.571980] [ 2475] 0 2475 2435 812 0 0 0 sshd Oct 25 07:28:04 nldedip4k031 kernel: [87946.571982] [ 2494] 0 2494 1745 839 1 0 0 bash Oct 25 07:28:04 nldedip4k031 kernel: [87946.571984] [ 2573] 0 2573 3394 1689 0 0 0 sshd Oct 25 07:28:04 nldedip4k031 kernel: [87946.571986] [ 2589] 0 2589 5014 457 3 0 0 rsync Oct 25 07:28:04 nldedip4k031 kernel: [87946.571988] [ 2590] 0 2590 7970 522 1 0 0 rsync Oct 25 07:28:04 nldedip4k031 kernel: [87946.571990] [ 2652] 104 2652 1150 326 5 0 0 pickup Oct 25 07:28:04 nldedip4k031 kernel: [87946.571992] Out of memory: Kill process 421 (upstart-udev-br) score 1 or sacrifice child Oct 25 07:28:04 nldedip4k031 kernel: [87946.572407] Killed process 421 (upstart-udev-br) total-vm:2836kB, anon-rss:156kB, file-rss:452kB Oct 25 07:28:04 nldedip4k031 kernel: [87946.573107] init: upstart-udev-bridge main process (421) killed by KILL signal Oct 25 07:28:04 nldedip4k031 kernel: [87946.573126] init: upstart-udev-bridge main process ended, respawning Oct 25 07:28:34 nldedip4k031 kernel: [87976.461570] irqbalance invoked oom-killer: gfp_mask=0x80d0, order=0, oom_adj=0, oom_score_adj=0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461573] irqbalance cpuset=/ mems_allowed=0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461576] Pid: 948, comm: irqbalance Not tainted 3.2.0-55-generic-pae #85-Ubuntu Oct 25 07:28:34 nldedip4k031 kernel: [87976.461578] Call Trace: Oct 25 07:28:34 nldedip4k031 kernel: [87976.461585] [] dump_header.isra.6+0x85/0xc0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461588] [] oom_kill_process+0x5c/0x80 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461591] [] out_of_memory+0xc5/0x1c0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461595] [] __alloc_pages_nodemask+0x72c/0x740 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461599] [] __get_free_pages+0x1c/0x30 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461602] [] get_zeroed_page+0x12/0x20 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461606] [] fill_read_buffer.isra.8+0xaa/0xd0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461609] [] sysfs_read_file+0x7d/0x90 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461613] [] vfs_read+0x8c/0x160 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461616] [] ? fill_read_buffer.isra.8+0xd0/0xd0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461619] [] sys_read+0x3d/0x70 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461624] [] sysenter_do_call+0x12/0x28 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461626] Mem-Info: Oct 25 07:28:34 nldedip4k031 kernel: [87976.461628] DMA per-cpu: Oct 25 07:28:34 nldedip4k031 kernel: [87976.461629] CPU 0: hi: 0, btch: 1 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461631] CPU 1: hi: 0, btch: 1 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461633] CPU 2: hi: 0, btch: 1 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461634] CPU 3: hi: 0, btch: 1 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461636] CPU 4: hi: 0, btch: 1 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461638] CPU 5: hi: 0, btch: 1 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461639] CPU 6: hi: 0, btch: 1 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461641] CPU 7: hi: 0, btch: 1 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461642] Normal per-cpu: Oct 25 07:28:34 nldedip4k031 kernel: [87976.461644] CPU 0: hi: 186, btch: 31 usd: 61 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461646] CPU 1: hi: 186, btch: 31 usd: 49 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461647] CPU 2: hi: 186, btch: 31 usd: 8 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461649] CPU 3: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461651] CPU 4: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461652] CPU 5: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461654] CPU 6: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461656] CPU 7: hi: 186, btch: 31 usd: 30 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461657] HighMem per-cpu: Oct 25 07:28:34 nldedip4k031 kernel: [87976.461658] CPU 0: hi: 186, btch: 31 usd: 4 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461660] CPU 1: hi: 186, btch: 31 usd: 204 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461662] CPU 2: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461663] CPU 3: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461665] CPU 4: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461667] CPU 5: hi: 186, btch: 31 usd: 31 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461668] CPU 6: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461670] CPU 7: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461674] active_anon:5441 inactive_anon:412 isolated_anon:0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461674] active_file:2668 inactive_file:6922842 isolated_file:0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461675] unevictable:0 dirty:836 writeback:0 unstable:0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461676] free:1231664 slab_reclaimable:105781 slab_unreclaimable:3399 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461677] mapped:2649 shmem:138 pagetables:313 bounce:0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461682] DMA free:4248kB min:780kB low:972kB high:1168kB active_anon:0kB inactive_anon:0kB active_file:0kB inactive_file:4kB unevictable:0kB isolated(anon):0kB isolated(file):0kB present:15756kB mlocked:0kB dirty:0kB writeback:0kB mapped:0kB shmem:0kB slab_reclaimable:11560kB slab_unreclaimable:4kB kernel_stack:0kB pagetables:0kB unstable:0kB bounce:0kB writeback_tmp:0kB pages_scanned:5687 all_unreclaimable? yes Oct 25 07:28:34 nldedip4k031 kernel: [87976.461686] lowmem_reserve[]: 0 869 32460 32460 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461693] Normal free:44184kB min:44216kB low:55268kB high:66324kB active_anon:0kB inactive_anon:0kB active_file:20kB inactive_file:1096kB unevictable:0kB isolated(anon):0kB isolated(file):0kB present:890008kB mlocked:0kB dirty:4kB writeback:0kB mapped:4kB shmem:0kB slab_reclaimable:411564kB slab_unreclaimable:13592kB kernel_stack:992kB pagetables:0kB unstable:0kB bounce:0kB writeback_tmp:0kB pages_scanned:1816 all_unreclaimable? yes Oct 25 07:28:34 nldedip4k031 kernel: [87976.461697] lowmem_reserve[]: 0 0 252733 252733 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461703] HighMem free:4878224kB min:512kB low:402312kB high:804112kB active_anon:21764kB inactive_anon:1648kB active_file:10652kB inactive_file:27690268kB unevictable:0kB isolated(anon):0kB isolated(file):0kB present:32349872kB mlocked:0kB dirty:3340kB writeback:0kB mapped:10592kB shmem:552kB slab_reclaimable:0kB slab_unreclaimable:0kB kernel_stack:0kB pagetables:1252kB unstable:0kB bounce:0kB writeback_tmp:0kB pages_scanned:0 all_unreclaimable? no Oct 25 07:28:34 nldedip4k031 kernel: [87976.461708] lowmem_reserve[]: 0 0 0 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461711] DMA: 8*4kB 7*8kB 6*16kB 5*32kB 5*64kB 4*128kB 2*256kB 1*512kB 0*1024kB 1*2048kB 0*4096kB = 4248kB Oct 25 07:28:34 nldedip4k031 kernel: [87976.461719] Normal: 272*4kB 178*8kB 76*16kB 52*32kB 42*64kB 36*128kB 23*256kB 20*512kB 7*1024kB 2*2048kB 1*4096kB = 44176kB Oct 25 07:28:34 nldedip4k031 kernel: [87976.461727] HighMem: 1*4kB 45*8kB 31*16kB 24*32kB 5*64kB 3*128kB 1*256kB 2*512kB 4*1024kB 2*2048kB 1188*4096kB = 4877852kB Oct 25 07:28:34 nldedip4k031 kernel: [87976.461736] 6925679 total pagecache pages Oct 25 07:28:34 nldedip4k031 kernel: [87976.461737] 0 pages in swap cache Oct 25 07:28:34 nldedip4k031 kernel: [87976.461739] Swap cache stats: add 0, delete 0, find 0/0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461740] Free swap = 3998716kB Oct 25 07:28:34 nldedip4k031 kernel: [87976.461741] Total swap = 3998716kB Oct 25 07:28:34 nldedip4k031 kernel: [87976.524951] 8437743 pages RAM Oct 25 07:28:34 nldedip4k031 kernel: [87976.524953] 8209409 pages HighMem Oct 25 07:28:34 nldedip4k031 kernel: [87976.524954] 159556 pages reserved Oct 25 07:28:34 nldedip4k031 kernel: [87976.524955] 6936141 pages shared Oct 25 07:28:34 nldedip4k031 kernel: [87976.524956] 124602 pages non-shared Oct 25 07:28:34 nldedip4k031 kernel: [87976.524957] [ pid ] uid tgid total_vm rss cpu oom_adj oom_score_adj name Oct 25 07:28:34 nldedip4k031 kernel: [87976.524966] [ 429] 0 429 773 326 5 -17 -1000 udevd Oct 25 07:28:34 nldedip4k031 kernel: [87976.524968] [ 567] 0 567 772 224 4 -17 -1000 udevd Oct 25 07:28:34 nldedip4k031 kernel: [87976.524971] [ 568] 0 568 772 231 7 -17 -1000 udevd Oct 25 07:28:34 nldedip4k031 kernel: [87976.524973] [ 764] 0 764 712 103 3 0 0 upstart-socket- Oct 25 07:28:34 nldedip4k031 kernel: [87976.524976] [ 772] 103 772 815 164 2 0 0 dbus-daemon Oct 25 07:28:34 nldedip4k031 kernel: [87976.524979] [ 785] 0 785 1671 600 1 -17 -1000 sshd Oct 25 07:28:34 nldedip4k031 kernel: [87976.524981] [ 809] 101 809 7766 380 1 0 0 rsyslogd Oct 25 07:28:34 nldedip4k031 kernel: [87976.524983] [ 869] 0 869 1158 213 3 0 0 getty Oct 25 07:28:34 nldedip4k031 kernel: [87976.524986] [ 873] 0 873 1158 214 6 0 0 getty Oct 25 07:28:34 nldedip4k031 kernel: [87976.524988] [ 911] 0 911 1158 215 3 0 0 getty Oct 25 07:28:34 nldedip4k031 kernel: [87976.524990] [ 912] 0 912 1158 214 2 0 0 getty Oct 25 07:28:34 nldedip4k031 kernel: [87976.524992] [ 914] 0 914 1158 213 1 0 0 getty Oct 25 07:28:34 nldedip4k031 kernel: [87976.524995] [ 916] 0 916 618 86 1 0 0 atd Oct 25 07:28:34 nldedip4k031 kernel: [87976.524997] [ 917] 0 917 655 226 3 0 0 cron Oct 25 07:28:34 nldedip4k031 kernel: [87976.524999] [ 948] 0 948 902 159 5 0 0 irqbalance Oct 25 07:28:34 nldedip4k031 kernel: [87976.525002] [ 993] 0 993 1145 363 3 0 0 master Oct 25 07:28:34 nldedip4k031 kernel: [87976.525004] [ 1002] 104 1002 1162 333 1 0 0 qmgr Oct 25 07:28:34 nldedip4k031 kernel: [87976.525007] [ 1016] 0 1016 730 149 2 0 0 mdadm Oct 25 07:28:34 nldedip4k031 kernel: [87976.525009] [ 1057] 0 1057 6066 2160 3 0 0 /usr/sbin/apach Oct 25 07:28:34 nldedip4k031 kernel: [87976.525012] [ 1086] 0 1086 1158 213 3 0 0 getty Oct 25 07:28:34 nldedip4k031 kernel: [87976.525014] [ 1088] 33 1088 6191 1517 0 0 0 /usr/sbin/apach Oct 25 07:28:34 nldedip4k031 kernel: [87976.525017] [ 1089] 33 1089 6191 1451 1 0 0 /usr/sbin/apach Oct 25 07:28:34 nldedip4k031 kernel: [87976.525019] [ 1090] 33 1090 6175 1451 1 0 0 /usr/sbin/apach Oct 25 07:28:34 nldedip4k031 kernel: [87976.525021] [ 1091] 33 1091 6191 1451 1 0 0 /usr/sbin/apach Oct 25 07:28:34 nldedip4k031 kernel: [87976.525024] [ 1092] 33 1092 6191 1451 0 0 0 /usr/sbin/apach Oct 25 07:28:34 nldedip4k031 kernel: [87976.525026] [ 1109] 33 1109 6191 1517 0 0 0 /usr/sbin/apach Oct 25 07:28:34 nldedip4k031 kernel: [87976.525029] [ 1151] 33 1151 6191 1451 1 0 0 /usr/sbin/apach Oct 25 07:28:34 nldedip4k031 kernel: [87976.525031] [ 1201] 104 1201 1803 652 1 0 0 tlsmgr Oct 25 07:28:34 nldedip4k031 kernel: [87976.525033] [ 2475] 0 2475 2435 812 0 0 0 sshd Oct 25 07:28:34 nldedip4k031 kernel: [87976.525036] [ 2494] 0 2494 1745 839 1 0 0 bash Oct 25 07:28:34 nldedip4k031 kernel: [87976.525038] [ 2573] 0 2573 3394 1689 3 0 0 sshd Oct 25 07:28:34 nldedip4k031 kernel: [87976.525040] [ 2589] 0 2589 5014 457 3 0 0 rsync Oct 25 07:28:34 nldedip4k031 kernel: [87976.525043] [ 2590] 0 2590 7970 522 1 0 0 rsync Oct 25 07:28:34 nldedip4k031 kernel: [87976.525045] [ 2652] 104 2652 1150 326 5 0 0 pickup Oct 25 07:28:34 nldedip4k031 kernel: [87976.525048] [ 2847] 0 2847 709 89 0 0 0 upstart-udev-br Oct 25 07:28:34 nldedip4k031 kernel: [87976.525050] Out of memory: Kill process 764 (upstart-socket-) score 1 or sacrifice child Oct 25 07:28:34 nldedip4k031 kernel: [87976.525484] Killed process 764 (upstart-socket-) total-vm:2848kB, anon-rss:204kB, file-rss:208kB Oct 25 07:28:34 nldedip4k031 kernel: [87976.526161] init: upstart-socket-bridge main process (764) killed by KILL signal Oct 25 07:28:34 nldedip4k031 kernel: [87976.526180] init: upstart-socket-bridge main process ended, respawning Oct 25 07:28:44 nldedip4k031 kernel: [87986.439671] irqbalance invoked oom-killer: gfp_mask=0x80d0, order=0, oom_adj=0, oom_score_adj=0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439674] irqbalance cpuset=/ mems_allowed=0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439676] Pid: 948, comm: irqbalance Not tainted 3.2.0-55-generic-pae #85-Ubuntu Oct 25 07:28:44 nldedip4k031 kernel: [87986.439678] Call Trace: Oct 25 07:28:44 nldedip4k031 kernel: [87986.439684] [] dump_header.isra.6+0x85/0xc0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439686] [] oom_kill_process+0x5c/0x80 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439688] [] out_of_memory+0xc5/0x1c0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439691] [] __alloc_pages_nodemask+0x72c/0x740 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439694] [] __get_free_pages+0x1c/0x30 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439696] [] get_zeroed_page+0x12/0x20 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439699] [] fill_read_buffer.isra.8+0xaa/0xd0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439702] [] sysfs_read_file+0x7d/0x90 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439704] [] vfs_read+0x8c/0x160 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439707] [] ? fill_read_buffer.isra.8+0xd0/0xd0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439709] [] sys_read+0x3d/0x70 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439712] [] sysenter_do_call+0x12/0x28 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439714] Mem-Info: Oct 25 07:28:44 nldedip4k031 kernel: [87986.439714] DMA per-cpu: Oct 25 07:28:44 nldedip4k031 kernel: [87986.439716] CPU 0: hi: 0, btch: 1 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439717] CPU 1: hi: 0, btch: 1 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439718] CPU 2: hi: 0, btch: 1 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439719] CPU 3: hi: 0, btch: 1 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439720] CPU 4: hi: 0, btch: 1 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439721] CPU 5: hi: 0, btch: 1 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439722] CPU 6: hi: 0, btch: 1 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439723] CPU 7: hi: 0, btch: 1 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439724] Normal per-cpu: Oct 25 07:28:44 nldedip4k031 kernel: [87986.439725] CPU 0: hi: 186, btch: 31 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439726] CPU 1: hi: 186, btch: 31 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439727] CPU 2: hi: 186, btch: 31 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439728] CPU 3: hi: 186, btch: 31 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439729] CPU 4: hi: 186, btch: 31 usd: 0 Oct 25 07:33:48 nldedip4k031 kernel: imklog 5.8.6, log source = /proc/kmsg started. Oct 25 07:33:48 nldedip4k031 rsyslogd: [origin software="rsyslogd" swVersion="5.8.6" x-pid="2880" x-info="http://www.rsyslog.com"] start Oct 25 07:33:48 nldedip4k031 rsyslogd: rsyslogd's groupid changed to 103 Oct 25 07:33:48 nldedip4k031 rsyslogd: rsyslogd's userid changed to 101 Oct 25 07:33:48 nldedip4k031 rsyslogd-2039: Could not open output pipe '/dev/xconsole' [try http://www.rsyslog.com/e/2039 ]

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  • OpenVPN - Windows 8 to Windows 2008 Server, not connecting

    - by niico
    I have followed this tutorial about setting up an OpenVPN Server on Windows Server - and a client on Windows (in this case Windows 8). The server appears to be running fine - but it is not connecting with this error: Mon Jul 22 19:09:04 2013 Warning: cannot open --log file: C:\Program Files\OpenVPN\log\my-laptop.log: Access is denied. (errno=5) Mon Jul 22 19:09:04 2013 OpenVPN 2.3.2 x86_64-w64-mingw32 [SSL (OpenSSL)] [LZO] [PKCS11] [eurephia] [IPv6] built on Jun 3 2013 Mon Jul 22 19:09:04 2013 MANAGEMENT: TCP Socket listening on [AF_INET]127.0.0.1:25340 Mon Jul 22 19:09:04 2013 Need hold release from management interface, waiting... Mon Jul 22 19:09:05 2013 MANAGEMENT: Client connected from [AF_INET]127.0.0.1:25340 Mon Jul 22 19:09:05 2013 MANAGEMENT: CMD 'state on' Mon Jul 22 19:09:05 2013 MANAGEMENT: CMD 'log all on' Mon Jul 22 19:09:05 2013 MANAGEMENT: CMD 'hold off' Mon Jul 22 19:09:05 2013 MANAGEMENT: CMD 'hold release' Mon Jul 22 19:09:05 2013 Socket Buffers: R=[65536->65536] S=[65536->65536] Mon Jul 22 19:09:05 2013 UDPv4 link local: [undef] Mon Jul 22 19:09:05 2013 UDPv4 link remote: [AF_INET]66.666.66.666:9999 Mon Jul 22 19:09:05 2013 MANAGEMENT: >STATE:1374494945,WAIT,,, Mon Jul 22 19:10:05 2013 TLS Error: TLS key negotiation failed to occur within 60 seconds (check your network connectivity) Mon Jul 22 19:10:05 2013 TLS Error: TLS handshake failed Mon Jul 22 19:10:05 2013 SIGUSR1[soft,tls-error] received, process restarting Mon Jul 22 19:10:05 2013 MANAGEMENT: >STATE:1374495005,RECONNECTING,tls-error,, Mon Jul 22 19:10:05 2013 Restart pause, 2 second(s) Note I have changed the IP and port no (it uses a non-standard port for security reasons). That port is open on the hardware firewall. The server logs are showing a connection attempt from my client: TLS: Initial packet from [AF_INET]118.68.xx.xx:65011, sid=081af4ed xxxxxxxx Mon Jul 22 14:19:15 2013 118.68.xx.xx:65011 TLS Error: TLS key negotiation failed to occur within 60 seconds (check your network connectivity) How can I problem solve this & find the problem? Thx Update - Client config file: ############################################## # Sample client-side OpenVPN 2.0 config file # # for connecting to multi-client server. # # # # This configuration can be used by multiple # # clients, however each client should have # # its own cert and key files. # # # # On Windows, you might want to rename this # # file so it has a .ovpn extension # ############################################## # Specify that we are a client and that we # will be pulling certain config file directives # from the server. client # Use the same setting as you are using on # the server. # On most systems, the VPN will not function # unless you partially or fully disable # the firewall for the TUN/TAP interface. ;dev tap dev tun # Windows needs the TAP-Win32 adapter name # from the Network Connections panel # if you have more than one. On XP SP2, # you may need to disable the firewall # for the TAP adapter. ;dev-node MyTap # Are we connecting to a TCP or # UDP server? Use the same setting as # on the server. ;proto tcp proto udp # The hostname/IP and port of the server. # You can have multiple remote entries # to load balance between the servers. remote 00.00.00.00 1194 ;remote 00.00.00.00 9999 ;remote my-server-2 1194 # Choose a random host from the remote # list for load-balancing. Otherwise # try hosts in the order specified. ;remote-random # Keep trying indefinitely to resolve the # host name of the OpenVPN server. Very useful # on machines which are not permanently connected # to the internet such as laptops. resolv-retry infinite # Most clients don't need to bind to # a specific local port number. nobind # Downgrade privileges after initialization (non-Windows only) ;user nobody ;group nobody # Try to preserve some state across restarts. persist-key persist-tun # If you are connecting through an # HTTP proxy to reach the actual OpenVPN # server, put the proxy server/IP and # port number here. See the man page # if your proxy server requires # authentication. ;http-proxy-retry # retry on connection failures ;http-proxy [proxy server] [proxy port #] # Wireless networks often produce a lot # of duplicate packets. Set this flag # to silence duplicate packet warnings. ;mute-replay-warnings # SSL/TLS parms. # See the server config file for more # description. It's best to use # a separate .crt/.key file pair # for each client. A single ca # file can be used for all clients. ca "C:\\Program Files\\OpenVPN\\config\\ca.crt" cert "C:\\Program Files\\OpenVPN\\config\\my-laptop.crt" key "C:\\Program Files\\OpenVPN\\config\\my-laptop.key" # Verify server certificate by checking # that the certicate has the nsCertType # field set to "server". This is an # important precaution to protect against # a potential attack discussed here: # http://openvpn.net/howto.html#mitm # # To use this feature, you will need to generate # your server certificates with the nsCertType # field set to "server". The build-key-server # script in the easy-rsa folder will do this. ns-cert-type server # If a tls-auth key is used on the server # then every client must also have the key. ;tls-auth ta.key 1 # Select a cryptographic cipher. # If the cipher option is used on the server # then you must also specify it here. ;cipher x # Enable compression on the VPN link. # Don't enable this unless it is also # enabled in the server config file. comp-lzo # Set log file verbosity. verb 3 # Silence repeating messages ;mute 20 Server config file: ################################################# # Sample OpenVPN 2.0 config file for # # multi-client server. # # # # This file is for the server side # # of a many-clients <-> one-server # # OpenVPN configuration. # # # # OpenVPN also supports # # single-machine <-> single-machine # # configurations (See the Examples page # # on the web site for more info). # # # # This config should work on Windows # # or Linux/BSD systems. Remember on # # Windows to quote pathnames and use # # double backslashes, e.g.: # # "C:\\Program Files\\OpenVPN\\config\\foo.key" # # # # Comments are preceded with '#' or ';' # ################################################# # Which local IP address should OpenVPN # listen on? (optional) ;local 00.00.00.00 # Which TCP/UDP port should OpenVPN listen on? # If you want to run multiple OpenVPN instances # on the same machine, use a different port # number for each one. You will need to # open up this port on your firewall. std 1194 port 1194 # TCP or UDP server? ;proto tcp proto udp # "dev tun" will create a routed IP tunnel, # "dev tap" will create an ethernet tunnel. # Use "dev tap0" if you are ethernet bridging # and have precreated a tap0 virtual interface # and bridged it with your ethernet interface. # If you want to control access policies # over the VPN, you must create firewall # rules for the the TUN/TAP interface. # On non-Windows systems, you can give # an explicit unit number, such as tun0. # On Windows, use "dev-node" for this. # On most systems, the VPN will not function # unless you partially or fully disable # the firewall for the TUN/TAP interface. ;dev tap dev tun # Windows needs the TAP-Win32 adapter name # from the Network Connections panel if you # have more than one. On XP SP2 or higher, # you may need to selectively disable the # Windows firewall for the TAP adapter. # Non-Windows systems usually don't need this. ;dev-node MyTap # SSL/TLS root certificate (ca), certificate # (cert), and private key (key). Each client # and the server must have their own cert and # key file. The server and all clients will # use the same ca file. # # See the "easy-rsa" directory for a series # of scripts for generating RSA certificates # and private keys. Remember to use # a unique Common Name for the server # and each of the client certificates. # # Any X509 key management system can be used. # OpenVPN can also use a PKCS #12 formatted key file # (see "pkcs12" directive in man page). ca "C:\\Program Files\\OpenVPN\\config\\ca.crt" cert "C:\\Program Files\\OpenVPN\\config\\server.crt" key "C:\\Program Files\\OpenVPN\\config\\server.key" # Diffie hellman parameters. # Generate your own with: # openssl dhparam -out dh1024.pem 1024 # Substitute 2048 for 1024 if you are using # 2048 bit keys. dh "C:\\Program Files\\OpenVPN\\config\\dh2048.pem" # Configure server mode and supply a VPN subnet # for OpenVPN to draw client addresses from. # The server will take 10.8.0.1 for itself, # the rest will be made available to clients. # Each client will be able to reach the server # on 10.8.0.1. Comment this line out if you are # ethernet bridging. See the man page for more info. server 10.8.0.0 255.255.255.0 # Maintain a record of client <-> virtual IP address # associations in this file. If OpenVPN goes down or # is restarted, reconnecting clients can be assigned # the same virtual IP address from the pool that was # previously assigned. ifconfig-pool-persist ipp.txt # Configure server mode for ethernet bridging. # You must first use your OS's bridging capability # to bridge the TAP interface with the ethernet # NIC interface. Then you must manually set the # IP/netmask on the bridge interface, here we # assume 10.8.0.4/255.255.255.0. Finally we # must set aside an IP range in this subnet # (start=10.8.0.50 end=10.8.0.100) to allocate # to connecting clients. Leave this line commented # out unless you are ethernet bridging. ;server-bridge 10.8.0.4 255.255.255.0 10.8.0.50 10.8.0.100 # Configure server mode for ethernet bridging # using a DHCP-proxy, where clients talk # to the OpenVPN server-side DHCP server # to receive their IP address allocation # and DNS server addresses. You must first use # your OS's bridging capability to bridge the TAP # interface with the ethernet NIC interface. # Note: this mode only works on clients (such as # Windows), where the client-side TAP adapter is # bound to a DHCP client. ;server-bridge # Push routes to the client to allow it # to reach other private subnets behind # the server. Remember that these # private subnets will also need # to know to route the OpenVPN client # address pool (10.8.0.0/255.255.255.0) # back to the OpenVPN server. ;push "route 192.168.10.0 255.255.255.0" ;push "route 192.168.20.0 255.255.255.0" # To assign specific IP addresses to specific # clients or if a connecting client has a private # subnet behind it that should also have VPN access, # use the subdirectory "ccd" for client-specific # configuration files (see man page for more info). # EXAMPLE: Suppose the client # having the certificate common name "Thelonious" # also has a small subnet behind his connecting # machine, such as 192.168.40.128/255.255.255.248. # First, uncomment out these lines: ;client-config-dir ccd ;route 192.168.40.128 255.255.255.248 # Then create a file ccd/Thelonious with this line: # iroute 192.168.40.128 255.255.255.248 # This will allow Thelonious' private subnet to # access the VPN. This example will only work # if you are routing, not bridging, i.e. you are # using "dev tun" and "server" directives. # EXAMPLE: Suppose you want to give # Thelonious a fixed VPN IP address of 10.9.0.1. # First uncomment out these lines: ;client-config-dir ccd ;route 10.9.0.0 255.255.255.252 # Then add this line to ccd/Thelonious: # ifconfig-push 10.9.0.1 10.9.0.2 # Suppose that you want to enable different # firewall access policies for different groups # of clients. There are two methods: # (1) Run multiple OpenVPN daemons, one for each # group, and firewall the TUN/TAP interface # for each group/daemon appropriately. # (2) (Advanced) Create a script to dynamically # modify the firewall in response to access # from different clients. See man # page for more info on learn-address script. ;learn-address ./script # If enabled, this directive will configure # all clients to redirect their default # network gateway through the VPN, causing # all IP traffic such as web browsing and # and DNS lookups to go through the VPN # (The OpenVPN server machine may need to NAT # or bridge the TUN/TAP interface to the internet # in order for this to work properly). ;push "redirect-gateway def1 bypass-dhcp" # Certain Windows-specific network settings # can be pushed to clients, such as DNS # or WINS server addresses. CAVEAT: # http://openvpn.net/faq.html#dhcpcaveats # The addresses below refer to the public # DNS servers provided by opendns.com. ;push "dhcp-option DNS 208.67.222.222" ;push "dhcp-option DNS 208.67.220.220" # Uncomment this directive to allow differenta # clients to be able to "see" each other. # By default, clients will only see the server. # To force clients to only see the server, you # will also need to appropriately firewall the # server's TUN/TAP interface. ;client-to-client # Uncomment this directive if multiple clients # might connect with the same certificate/key # files or common names. This is recommended # only for testing purposes. For production use, # each client should have its own certificate/key # pair. # # IF YOU HAVE NOT GENERATED INDIVIDUAL # CERTIFICATE/KEY PAIRS FOR EACH CLIENT, # EACH HAVING ITS OWN UNIQUE "COMMON NAME", # UNCOMMENT THIS LINE OUT. ;duplicate-cn # The keepalive directive causes ping-like # messages to be sent back and forth over # the link so that each side knows when # the other side has gone down. # Ping every 10 seconds, assume that remote # peer is down if no ping received during # a 120 second time period. keepalive 10 120 # For extra security beyond that provided # by SSL/TLS, create an "HMAC firewall" # to help block DoS attacks and UDP port flooding. # # Generate with: # openvpn --genkey --secret ta.key # # The server and each client must have # a copy of this key. # The second parameter should be '0' # on the server and '1' on the clients. ;tls-auth ta.key 0 # This file is secret # Select a cryptographic cipher. # This config item must be copied to # the client config file as well. ;cipher BF-CBC # Blowfish (default) ;cipher AES-128-CBC # AES ;cipher DES-EDE3-CBC # Triple-DES # Enable compression on the VPN link. # If you enable it here, you must also # enable it in the client config file. comp-lzo # The maximum number of concurrently connected # clients we want to allow. ;max-clients 100 # It's a good idea to reduce the OpenVPN # daemon's privileges after initialization. # # You can uncomment this out on # non-Windows systems. ;user nobody ;group nobody # The persist options will try to avoid # accessing certain resources on restart # that may no longer be accessible because # of the privilege downgrade. persist-key persist-tun # Output a short status file showing # current connections, truncated # and rewritten every minute. status openvpn-status.log # By default, log messages will go to the syslog (or # on Windows, if running as a service, they will go to # the "\Program Files\OpenVPN\log" directory). # Use log or log-append to override this default. # "log" will truncate the log file on OpenVPN startup, # while "log-append" will append to it. Use one # or the other (but not both). ;log openvpn.log ;log-append openvpn.log # Set the appropriate level of log # file verbosity. # # 0 is silent, except for fatal errors # 4 is reasonable for general usage # 5 and 6 can help to debug connection problems # 9 is extremely verbose verb 3 # Silence repeating messages. At most 20 # sequential messages of the same message # category will be output to the log. ;mute 20 I have changed IP's for security

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  • GPGPU

    WhatGPU obviously stands for Graphics Processing Unit (the silicon powering the display you are using to read this blog post). The extra GP in front of that stands for General Purpose computing.So, altogether GPGPU refers to computing we can perform on GPU for purposes beyond just drawing on the screen. In effect, we can use a GPGPU a bit like we already use a CPU: to perform some calculation (that doesn’t have to have any visual element to it). The attraction is that a GPGPU can be orders of magnitude faster than a CPU.WhyWhen I was at the SuperComputing conference in Portland last November, GPGPUs were all the rage. A quick online search reveals many articles introducing the GPGPU topic. I'll just share 3 here: pcper (ignoring all pages except the first, it is a good consumer perspective), gizmodo (nice take using mostly layman terms) and vizworld (answering the question on "what's the big deal").The GPGPU programming paradigm (from a high level) is simple: in your CPU program you define functions (aka kernels) that take some input, can perform the costly operation and return the output. The kernels are the things that execute on the GPGPU leveraging its power (and hence execute faster than what they could on the CPU) while the host CPU program waits for the results or asynchronously performs other tasks.However, GPGPUs have different characteristics to CPUs which means they are suitable only for certain classes of problem (i.e. data parallel algorithms) and not for others (e.g. algorithms with branching or recursion or other complex flow control). You also pay a high cost for transferring the input data from the CPU to the GPU (and vice versa the results back to the CPU), so the computation itself has to be long enough to justify the overhead transfer costs. If your problem space fits the criteria then you probably want to check out this technology.HowSo where can you get a graphics card to start playing with all this? At the time of writing, the two main vendors ATI (owned by AMD) and NVIDIA are the obvious players in this industry. You can read about GPGPU on this AMD page and also on this NVIDIA page. NVIDIA's website also has a free chapter on the topic from the "GPU Gems" book: A Toolkit for Computation on GPUs.If you followed the links above, then you've already come across some of the choices of programming models that are available today. Essentially, AMD is offering their ATI Stream technology accessible via a language they call Brook+; NVIDIA offers their CUDA platform which is accessible from CUDA C. Choosing either of those locks you into the GPU vendor and hence your code cannot run on systems with cards from the other vendor (e.g. imagine if your CPU code would run on Intel chips but not AMD chips). Having said that, both vendors plan to support a new emerging standard called OpenCL, which theoretically means your kernels can execute on any GPU that supports it. To learn more about all of these there is a website: gpgpu.org. The caveat about that site is that (currently) it completely ignores the Microsoft approach, which I touch on next.On Windows, there is already a cross-GPU-vendor way of programming GPUs and that is the DirectX API. Specifically, on Windows Vista and Windows 7, the DirectX 11 API offers a dedicated subset of the API for GPGPU programming: DirectCompute. You use this API on the CPU side, to set up and execute the kernels that run on the GPU. The kernels are written in a language called HLSL (High Level Shader Language). You can use DirectCompute with HLSL to write a "compute shader", which is the term DirectX uses for what I've been referring to in this post as a "kernel". For a comprehensive collection of links about this (including tutorials, videos and samples) please see my blog post: DirectCompute.Note that there are many efforts to build even higher level languages on top of DirectX that aim to expose GPGPU programming to a wider audience by making it as easy as today's mainstream programming models. I'll mention here just two of those efforts: Accelerator from MSR and Brahma by Ananth. Comments about this post welcome at the original blog.

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  • Oracle WebCenter - Well Connected

    - by Brian Dirking
    800x600 Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} An good post from Dan Elam on the state of the ECM industry (http://www.aiim.org/community/blogs/community/ECM-Vendors-go-to-War) . For those of you who don’t know Dan, he is one of the major forces in the content management industry. He founded eVisory and IMERGE Consulting, he is an AIIM Fellow and a former US Technical Expert to the International Standards Organization (ISO), and has been a driving force behind EmTag, AIIM’s Emerging Technologies Group. His post is interesting – it starts out talking about our Moveoff Documentum campaign, but then it becomes a much deeper insight into the ECM industry. Dan points out that Oracle has been making quiet strides in the ECM industry. In fact, analysts share this view Oracle, pointing out Oracle is growing greater than 20% annually while many of the big vendors are shrinking. And as Dan points out, this cements Oracle as one of the big five in the ECM space – the same week that Autonomy was removed from the Gartner Magic Quadrant for ECM. One of the key things points out is that Oracle WebCenter is well connected. WebCenter has out-of-the-box connections to key enterprise applications such as E-Business Suite, PeopleSoft, Siebel and JD Edwards. Those out-of-the-box integrations make it easy for organizations to drive content right into the places where it is needed, in the midst of business processes. At the same time, WebCenter provides composite interface capabilities to bring together two or more of these enterprise applications onto the same screen. Combine that with the capabilities of Oracle Social Network, you start to see how Oracle is providing a full platform for user engagement. But beyond those connections, WebCenter can also connect to other content management systems. It can index and search those systems from a single point of search, bringing back results in a single combined hitlist. WebCenter can also extend records management capabilities into Documentum, SharePoint, and email archiving systems. From a single console, records managers can define a series, set a retention schedule, and place holds – without having to go to each system to make these updates. Dan points out that there are some new competitive dynamics – to be sure. And it is interesting when a system can interact with another system, enforce dispositions and holds, and enable users to search and retrieve content. Oracle WebCenter is providing the infrastructure to build on, and the interfaces to drive user engagement. It’s an interesting time.

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  • Innovative SPARC: Lighting a Fire Under Oracle's New Hardware Business

    - by Paulo Folgado
    "There's a certain level of things you can do with commercially available parts," says Oracle Executive Vice President Mike Splain. But, he notes, you can do so much more if you design the parts yourself. Mike Splain,EVP, OracleYou can, for example, design cryptographic accelerators into your microprocessors so customers can run their networks fully encrypted if they choose.Of course, it helps if you've already built multiple processing "cores" into those chips so they can handle all that encrypting and decrypting while still getting their other work done.System on a ChipAs the leader of Oracle Microelectronics, Mike knows how implementing clever innovations in silicon can give systems a real competitive advantage.The SPARC microprocessors that his team designed at Sun pioneered the concept of multiple cores several years ago, and the UltraSPARC T2 processor--the industry's first "system on a chip"--packs up to eight cores per chip, each running as many as eight threads at once. That's the most cores and threads of any general-purpose processor. Looking back, Mike points out that the real value of large enterprise-class servers was their ability to run a lot of very large applications in parallel."The beauty of our CMT [chip multi-threading] machines is you can get that same kind of parallel-processing capability at a much lower cost and in a much smaller footprint," he says.The Whole StackWhat has Mike excited these days is that suddenly the opportunity to innovate is much bigger as part of Oracle."In my group, we used to look up the software stack and say, 'We can do any innovation we want, provided the only thing we have to change is what's in the Solaris operating system'--or maybe Java," he says. "If we wanted to change things beyond that, we'd have to go outside the walls of Sun and we'd have to convince the vendors: 'You have to align with us, you have to test with us, you have to build for us, and then you'll reap the benefits.' Now we get access to the entire stack. We can look all the way through the stack and say, 'Okay, what would make the database go faster? What would make the middleware go faster?'"Changing the WorldMike and his microelectronics team also like the fact that Oracle is not just any software company. We're #1 in database, middleware, business intelligence, and more."We're like all the other engineers from Sun; we believe we can change the world, if we can just figure out how to get people to pay attention to us," he says. "Now there's a mechanism at Oracle--much more so than we ever had at Sun."He notes, too, that every innovation in SPARC has involved some combination of hardware and softwareoptimization."Take our cryptography framework, for example. Sure, we can accelerate rapidly, but the Solaris OS has to provide the right set of interfaces that applications can tap into," Mike says. "Same thing with our multicore architecture. We have to have software that can utilize all those threads and run in parallel." His engineers, he points out, have never been interested in producing chips that sell as mere components."Our chips are always designed to go into systems and be combined with various pieces of software," he says. "Our job is to enable the creation of systems."

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  • Using NServiceBus behind a custom web service

    - by Michael Stephenson
    In this post I'd like to talk about an architecture scenario we had recently and how we were able to utilise NServiceBus to help us address this problem. Scenario Cognos is a reporting system used by one of my clients. A while back we developed a web service façade to allow line of business applications to be able to access reports from Cognos to support their various functions. The service was intended to provide access to reports which were quick running reports or pre-generated reports which could be accessed real-time on demand. One of the key aims of the web service was to provide a simple generic interface to allow applications to get any report without needing to worry about the complex .net SDK for Cognos. The web service also supported multi-hop kerberos delegation so that report data could be accesses under the context of the end user. This service was working well for a period of time. The Problem The problem we encountered was that reports were now also required to be available to batch processes. The original design was optimised for low latency so users would enjoy a positive experience, however when the batch processes started to request 250+ concurrent reports over an extended period of time you can begin to imagine the sorts of problems that come into play. The key problems this new scenario caused are: Users may be affected and the latency of on demand reports was significantly slower The Cognos infrastructure was not scaled sufficiently to be able to cope with these long peaks of load From a cost perspective it just isn't feasible to scale the Cognos infrastructure to be able to handle the load when it is only for a couple of hour window each night. We really needed to introduce a second pattern for accessing this service which would support high through-put scenarios. We also had little control over the batch process in terms of being able to throttle its load. We could however make some changes to the way it accessed the reports. The Approach My idea was to introduce a throttling mechanism between the Web Service Façade and Cognos. This would allow the batch processes to push reports requests hard at the web service which we were confident the web service can handle. The web service would then queue these requests and process them behind the scenes and make a call back to the batch application to provide the report once it had been accessed. In terms of technology we had some limitations because we were not able to use WCF or IIS7 where the MSMQ-Activated WCF services could have helped, but we did have MSMQ as an option and I thought NServiceBus could do just the job to help us here. The flow of how this would work was as follows: The batch applications would send a request for a report to the web service The web service uses NServiceBus to send the message to a Queue The NServiceBus Generic Host is running as a windows service with a message handler which subscribes to these messages The message handler gets the message, accesses the report from Cognos The message handler calls back to the original batch application, this is decoupled because the calling application provides a call back url The report gets into the batch application and is processed as normal This approach looks something like the below diagram: The key points are an application wanting to take advantage of the batch driven reports needs to do the following: Implement our call back contract Make a call to the service providing a call back url Provide a correlation ID so it knows how to tie each response back to its request What does NServiceBus offer in this solution So this scenario is not the typical messaging service bus type of solution people implement with NServiceBus, but it did offer the following: Simplified interaction with MSMQ Offered the ability to configure the number of processes working through the queue so we could find a balance between load on Cognos versus the applications end to end processing time NServiceBus offers retries and a way to manage failed messages NServiceBus offers a high availability setup The simple thing is that NServiceBus gave us the platform to build the solution on. We just implemented a message handler which functionally processed a message and we could rely on NServiceBus to do all of the hard work around managing the queues and all of the lower level things that would have took ages to write to any kind of robust level. Conclusion With this approach we were able to deal with a fairly significant performance issue with out too much rework. Hopefully this write up gives people some insight into ideas on how to leverage the excellent NServiceBus framework to help solve integration and high through-put scenarios.

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  • Oracle Enterprise Manager 11g is Here!

    - by chung.wu
    We hope that you enjoyed the launch event. If you missed it, you may still watch it via our on demand webcast, which is being produced and will be posted very shortly. 11gR1 is a major release of Oracle Enterprise Manager, and as one would expect from a big release, there are many new capabilities that appeal to a broad set of audience. Before going into the laundry list of new features, let's talk about the key themes for this release to put things in perspective. First, this release is about Business Driven Application Management. The traditional paradigm of component centric systems management simply cannot satisfy the management needs of modern distributed applications, as they do not provide adequate visibility of whether these applications are truly meeting the service level expectations of the business users. Business Driven Application Management helps IT manage applications according to the needs of the business users so that valuable IT resources can be better focused to help deliver better business results. To support Business Driven Application Management, 11gR1 builds on the work that we started in 10g to provide better support for user experience management. This capability helps IT better understand how users use applications and the experience that the applications provide so that IT can take actions to help end users get their work done more effectively. In addition, this release also delivers improved business transaction management capabilities to make it faster and easier to understand and troubleshoot transaction problems that impact end user experience. Second, this release includes strengthened Integrated Application-to-Disk Management. Every component of an application environment, from the application logic to the application server, to database, host machines and storage devices, etc... can affect end user experience. After user experience improvement needs are identified, IT needs tools that can be used do deep dive diagnostics for each of the application environment component, analyze configurations and deploy changes. Enterprise Manager 11gR1 extends coverage of key application environment components to include full support for Oracle Database 11gR2, Exadata V2, and Fusion Middleware 11g. For composite and Java application management, two key pieces of technologies, JVM Diagnostic and Composite Application Monitoring and Modeler, are now fully integrated into Enterprise Manager so there is no need to install and maintain separate tools. In addition, we have delivered the first set of integration between Enterprise Manager Grid Control and Enterprise Manager Ops Center so that hardware level events can be centrally monitored via Grid Control. Finally, this release delivers Integrated Systems Management and Support for customers of Oracle technologies. Traditionally, systems management tools and tech support were separate silos. When problems occur, administrators used internally deployed tools to try to solve the problems themselves. If they couldn't fix the problems, then they would use some sort of support website to get help from the vendor's support staff. Oracle Enterprise Manager 11g integrates problem diagnostic and remediation workflow. Administrators can use Oracle Enterprise Manager's various diagnostic tools to begin the troubleshooting process. They can also use the integrated access to My Oracle Support to look up solutions and download software patches. If further help is needed, administrators can open service requests from right within Oracle Enterprise Manager and track status update. Oracle's support staff, using Enterprise Manager's configuration management capabilities, can collect important configuration information about customer environments in order to expedite problem resolution. This tight integration between Oracle Enterprise Manager and My Oracle Support helps Oracle customers achieve a Superior Ownership Experience for their Oracle products. So there you have it. This is a brief 50,000 feet overview of Oracle Enterprise Manager 11g. We know you are hungry for the details. We are going to write about it in the coming days and weeks. For those of you that absolutely can't wait to find out more, you may download our software to try it out today. In fact, for the first time ever, the initial release of Oracle Enterprise Manager is available for both 32 and 64 bit Linux. Additional O/S ports will arrive in the coming weeks. Please stay tuned on the Oracle Enterprise Manager blog for additional updates.

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  • Windows Azure Use Case: Agility

    - by BuckWoody
    This is one in a series of posts on when and where to use a distributed architecture design in your organization's computing needs. You can find the main post here: http://blogs.msdn.com/b/buckwoody/archive/2011/01/18/windows-azure-and-sql-azure-use-cases.aspx  Description: Agility in this context is defined as the ability to quickly develop and deploy an application. In theory, the speed at which your organization can develop and deploy an application on available hardware is identical to what you could deploy in a distributed environment. But in practice, this is not always the case. Having an option to use a distributed environment can be much faster for the deployment and even the development process. Implementation: When an organization designs code, they are essentially becoming a Software-as-a-Service (SaaS) provider to their own organization. To do that, the IT operations team becomes the Infrastructure-as-a-Service (IaaS) to the development teams. From there, the software is developed and deployed using an Application Lifecycle Management (ALM) process. A simplified view of an ALM process is as follows: Requirements Analysis Design and Development Implementation Testing Deployment to Production Maintenance In an on-premise environment, this often equates to the following process map: Requirements Business requirements formed by Business Analysts, Developers and Data Professionals. Analysis Feasibility studies, including physical plant, security, manpower and other resources. Request is placed on the work task list if approved. Design and Development Code written according to organization’s chosen methodology, either on-premise or to multiple development teams on and off premise. Implementation Code checked into main branch. Code forked as needed. Testing Code deployed to on-premise Testing servers. If no server capacity available, more resources procured through standard budgeting and ordering processes. Manual and automated functional, load, security, etc. performed. Deployment to Production Server team involved to select platform and environments with available capacity. If no server capacity available, standard budgeting and procurement process followed. If no server capacity available, systems built, configured and put under standard organizational IT control. Systems configured for proper operating systems, patches, security and virus scans. System maintenance, HA/DR, backups and recovery plans configured and put into place. Maintenance Code changes evaluated and altered according to need. In a distributed computing environment like Windows Azure, the process maps a bit differently: Requirements Business requirements formed by Business Analysts, Developers and Data Professionals. Analysis Feasibility studies, including budget, security, manpower and other resources. Request is placed on the work task list if approved. Design and Development Code written according to organization’s chosen methodology, either on-premise or to multiple development teams on and off premise. Implementation Code checked into main branch. Code forked as needed. Testing Code deployed to Azure. Manual and automated functional, load, security, etc. performed. Deployment to Production Code deployed to Azure. Point in time backup and recovery plans configured and put into place.(HA/DR and automated backups already present in Azure fabric) Maintenance Code changes evaluated and altered according to need. This means that several steps can be removed or expedited. It also means that the business function requesting the application can be held directly responsible for the funding of that request, speeding the process further since the IT budgeting process may not be involved in the Azure scenario. An additional benefit is the “Azure Marketplace”, In effect this becomes an app store for Enterprises to select pre-defined code and data applications to mesh or bolt-in to their current code, possibly saving development time. Resources: Whitepaper download- What is ALM?  http://go.microsoft.com/?linkid=9743693  Whitepaper download - ALM and Business Strategy: http://go.microsoft.com/?linkid=9743690  LiveMeeting Recording on ALM and Windows Azure (registration required, but free): http://www.microsoft.com/uk/msdn/visualstudio/contact-us.aspx?sbj=Developing with Windows Azure (ALM perspective) - 10:00-11:00 - 19th Jan 2011

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  • Session Update from IASA 2010

    - by [email protected]
    Below: Tom Kristensen, senior vice president at Marsh US Consumer, and Roger Soppe, CLU, LUTCF, senior director of insurance strategy, Oracle Insurance. Tom and Roger participated in a panel discussion on policy administration systems this week at IASA 2010. This week was the 82nd Annual IASA Educational Conference & Business Show held in Grapevine, Texas. While attending the conference, I had the pleasure of serving as a panelist in one of many of the outstanding sessions conducted this year. The session - entitled "Achieving Business Agility and Promoting Growth with a Modern Policy Administration System" - included industry experts Steve Forte from OneShield, Mike Sciole of IFG Companies, and Tom Kristensen, senior vice president at Marsh US Consumer. The session was conducted as a panel discussion and focused on how insurers can leverage best practices to mitigate risk while enabling rapid product innovation through a modern policy administration system. The panelists offered insight into business and technical challenges for both Life & Annuity and Property & Casualty carriers. The session had three primary learning objectives: Identifying how replacing a legacy system with a more modern policy administration solution can deliver agility and growth Identifying how processes and system should be re-engineered or replaced in order to improve speed-to-market and product support Uncovering how to leverage best practices to mitigate risk during a migration to a new platform Tom Kristensen, who is an industry veteran with over 20 years of experience, was able was able to offer a unique perspective as a business process outsourcer (BPO). Marsh US Consumer is currently implementing both the Oracle Insurance Policy Administration solution and the Oracle Revenue Management and Billing platform while at the same time implementing a new BPO customer. Tom offered insight on the need to replace their aging systems and Marsh's ability to drive new products and processes with a modern solution. As a best practice, their current project has empowered their business users to play a major role in both the requirements gathering and configuration phases. Tom stated that working with a modern solution has also enabled his organization to use a more agile implementation methodology and get hands-on experience with the software earlier in the project. He also indicated that Marsh was encouraged by how quickly it will be able to implement new products, which is another major advantage of a modern rules-based system. One of the more interesting issues was raised by an audience member who asked, "With all the vendor solutions available in North American and across Europe, what is going to make some of them more successful than others and help ensure their long term success?" Panelist Mike Sciole, IFG Companies suggested that carriers do their due diligence and follow a structured evaluation process focusing on vendors who demonstrate they have the "cash to invest in long term R&D" and evaluate audited annual statements for verification. Other panelists suggested that the vendor space will continue to evolve and those with a strong strategy focused on the insurance industry and a solid roadmap will likely separate themselves from the rest. The session concluded with the panelists offering advice about not being afraid to evaluate new modern systems. While migrating to a new platform can be challenging and is typically only undertaken every 15+ years by carriers, the ability to rapidly deploy and manage new products, create consistent processes to better service customers, and the ability to manage their business more effectively, transparently and securely are well worth the effort. Roger A.Soppe, CLU, LUTCF, is the Senior Director of Insurance Strategy, Oracle Insurance.

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  • New Release of Oracle EPM (Enterprise Performance Management)

    - by Theresa Hickman
    I'm a huge fan of Hyperion products and consider Hyperion to be one of the best acquisitions Oracle has made in terms of applications. So I am really excited to talk about their latest release, Release 11.1.2 of the Oracle EPM System. This is EPM's largest release in 2 years, and it's jam-packed with new modules and features. In terms of brand new products, there are three: 1. Public Sector Planning and Budgeting meets the needs of public sector agencies, higher education, governments, etc. that have complex budget requirements. It supports position or employee-based budgeting and integrates with MS Office and your ERP ledgers to perform commitment control. 2. Hyperion Financial Close Management is a complete financial close solution that orchestrates the entire close process from subledgers and general ledger to financial reporting and disclosure submissions. And of course, it is integrated with GL systems and consolidation systems. I saw a demo of this and it looked pretty slick. They have this unified close calendar that looks like a regular calendar that gives each person participating in the close process a task list. It comes with a Gantt chart that shows the relationships and dependencies among closing tasks. There are dashboards to allow you to track the close progress and completion of tasks as well as perform trend analysis and see how much time is being spent on different activities in the close process. This gives you visibility that you never had before to understand where the bottlenecks are and where improvements could be made. I think what I liked best about this product was that it provides a central place for all participants to communicate their progress. When I worked as an Accountant, we used ad hoc tools, such as spreadsheets, Word documents, emails, and phone calls during the close process. I like the idea of having a central system to track the overall progress as well as automate the entire financial close process. Who knows, maybe Accountants won't have to revolve their lives around the month end close anymore with a tool like this. Those periodic fire drills can become predictable, well managed processes. 3. Disclosure Management is an out-of-the-box, pre-packaged XBRL solution to meet statutory reporting requirements. This product is really going to help companies improve the timeliness of producing financial reports. Reports can be authored using MS Word and Excel and then XBRL instance documents can be produced with its embedded XBRL tags. It even supports footnotes and disclosures of non-financial information. With a product like this, companies no longer have to outsource their XBRL filing; they can bring it back in house to save costs and time. In terms of other enhancements, they have ERP Integrator that provides integration and drill downs from Hyperion products to source systems, such as Oracle E-Business Suite, PeopleSoft, and SAP. No other vendor offers this level of integration. There's also a new product that links Oracle Essbase directly to Hyperion Financial Management for internal financial reporting, and new integrations between Hyperion Financial Management and Oracle's GRC products. They also improved the usability of Oracle Hyperion Planning. They made it much easier for end users to use the system via the web or via MS Excel when submitting plans and budgets. It is also integrated with intelligent approval workflows that are data-driven, user-configurable, and scenario-specific to efficiently streamline the budgeting process. Here's the press release from April 7, 2010. Here's the pre-recorded web cast where you can see the demos. Just register and watch the hour long presentation. And finally, here's the newsletter

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