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  • Disk fragmentation when dealing with many small files

    - by Zorlack
    On a daily basis we generate about 3.4 Million small jpeg files. We also delete about 3.4 Million 90 day old images. To date, we've dealt with this content by storing the images in a hierarchical manner. The heriarchy is something like this: /Year/Month/Day/Source/ This heirarchy allows us to effectively delete days worth of content across all sources. The files are stored on a Windows 2003 server connected to a 14 disk SATA RAID6. We've started having significant performance issues when writing-to and reading-from the disks. This may be due to the performance of the hardware, but I suspect that disk fragmentation may be a culprit at well. Some people have recommended storing the data in a database, but I've been hesitant to do this. An other thought was to use some sort of container file, like a VHD or something. Does anyone have any advice for mitigating this kind of fragmentation? Additional Info: The average file size is 8-14KB Format information from fsutil: NTFS Volume Serial Number : 0x2ae2ea00e2e9d05d Version : 3.1 Number Sectors : 0x00000001e847ffff Total Clusters : 0x000000003d08ffff Free Clusters : 0x000000001c1a4df0 Total Reserved : 0x0000000000000000 Bytes Per Sector : 512 Bytes Per Cluster : 4096 Bytes Per FileRecord Segment : 1024 Clusters Per FileRecord Segment : 0 Mft Valid Data Length : 0x000000208f020000 Mft Start Lcn : 0x00000000000c0000 Mft2 Start Lcn : 0x000000001e847fff Mft Zone Start : 0x0000000002163b20 Mft Zone End : 0x0000000007ad2000

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  • Using multiple USB webcams in Linux

    - by rachelderp
    Running more than one USB webcam in Debian/Linux results in the the following error: libv4l2: error turning on stream: No space left on device VIDIOC_STREAMON: No space left on device What initially seemed to be a programming issue in OpenCV turned into a quest for a mysterious hardware/software problem after the same errors were produced by running cheese and xawtv. Apparently it's caused by webcams requesting all the available bandwidth on the USB host controller. With that in mind I decided to run wireshark and capinfos to see just how much bandwidth a single camera used. 4 megabits per second at 320x240 14 megabits per second at 640x480 32 megabits per second at 1920x1080 Interesting! That might explain why two cameras at 320x240 work but any higher resolution fails. It's as if my USB controller is only operating at USB 1 speeds, yet lsusb shows both webcams belonging to a device which supposedly supports 480 megabits per second. One solution proposed forcing the webcams to calculate their bandwidth usage instead of requesting their maximum by running the following commands: sudo rmmod uvcvideo sudo modprobe uvcvideo quirks=128 Unfortunately that made no difference, so I decided to try another solution. A post on StackOverflow suggested telling my webcams to use a lower FPS or compressed video format like MJPEG, but after running v4lctl list it doesn't appear either of my webcams support changing their video mode. And that's where I'm stuck. Why would two webcams operating well below the maximum speed of USB 2 would produce this error? ps: It's not a disk space issue, df displays no change when the webcams are started. pps: If it makes a difference, here's the output of lsusb

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  • World Record Batch Rate on Oracle JD Edwards Consolidated Workload with SPARC T4-2

    - by Brian
    Oracle produced a World Record batch throughput for single system results on Oracle's JD Edwards EnterpriseOne Day-in-the-Life benchmark using Oracle's SPARC T4-2 server running Oracle Solaris Containers and consolidating JD Edwards EnterpriseOne, Oracle WebLogic servers and the Oracle Database 11g Release 2. The workload includes both online and batch workload. The SPARC T4-2 server delivered a result of 8,000 online users while concurrently executing a mix of JD Edwards EnterpriseOne Long and Short batch processes at 95.5 UBEs/min (Universal Batch Engines per minute). In order to obtain this record benchmark result, the JD Edwards EnterpriseOne, Oracle WebLogic and Oracle Database 11g Release 2 servers were executed each in separate Oracle Solaris Containers which enabled optimal system resources distribution and performance together with scalable and manageable virtualization. One SPARC T4-2 server running Oracle Solaris Containers and consolidating JD Edwards EnterpriseOne, Oracle WebLogic servers and the Oracle Database 11g Release 2 utilized only 55% of the available CPU power. The Oracle DB server in a Shared Server configuration allows for optimized CPU resource utilization and significant memory savings on the SPARC T4-2 server without sacrificing performance. This configuration with SPARC T4-2 server has achieved 33% more Users/core, 47% more UBEs/min and 78% more Users/rack unit than the IBM Power 770 server. The SPARC T4-2 server with 2 processors ran the JD Edwards "Day-in-the-Life" benchmark and supported 8,000 concurrent online users while concurrently executing mixed batch workloads at 95.5 UBEs per minute. The IBM Power 770 server with twice as many processors supported only 12,000 concurrent online users while concurrently executing mixed batch workloads at only 65 UBEs per minute. This benchmark demonstrates more than 2x cost savings by consolidating the complete solution in a single SPARC T4-2 server compared to earlier published results of 10,000 users and 67 UBEs per minute on two SPARC T4-2 and SPARC T4-1. The Oracle DB server used mirrored (RAID 1) volumes for the database providing high availability for the data without impacting performance. Performance Landscape JD Edwards EnterpriseOne Day in the Life (DIL) Benchmark Consolidated Online with Batch Workload System Rack Units BatchRate(UBEs/m) Online Users Users /Units Users /Core Version SPARC T4-2 (2 x SPARC T4, 2.85 GHz) 3 95.5 8,000 2,667 500 9.0.2 IBM Power 770 (4 x POWER7, 3.3 GHz, 32 cores) 8 65 12,000 1,500 375 9.0.2 Batch Rate (UBEs/m) — Batch transaction rate in UBEs per minute Configuration Summary Hardware Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 4 x 300 GB 10K RPM SAS internal disk 2 x 300 GB internal SSD 2 x Sun Storage F5100 Flash Arrays Software Configuration: Oracle Solaris 10 Oracle Solaris Containers JD Edwards EnterpriseOne 9.0.2 JD Edwards EnterpriseOne Tools (8.98.4.2) Oracle WebLogic Server 11g (10.3.4) Oracle HTTP Server 11g Oracle Database 11g Release 2 (11.2.0.1) Benchmark Description JD Edwards EnterpriseOne is an integrated applications suite of Enterprise Resource Planning (ERP) software. Oracle offers 70 JD Edwards EnterpriseOne application modules to support a diverse set of business operations. Oracle's Day in the Life (DIL) kit is a suite of scripts that exercises most common transactions of JD Edwards EnterpriseOne applications, including business processes such as payroll, sales order, purchase order, work order, and manufacturing processes, such as ship confirmation. These are labeled by industry acronyms such as SCM, CRM, HCM, SRM and FMS. The kit's scripts execute transactions typical of a mid-sized manufacturing company. The workload consists of online transactions and the UBE – Universal Business Engine workload of 61 short and 4 long UBEs. LoadRunner runs the DIL workload, collects the user’s transactions response times and reports the key metric of Combined Weighted Average Transaction Response time. The UBE processes workload runs from the JD Enterprise Application server. Oracle's UBE processes come as three flavors: Short UBEs < 1 minute engage in Business Report and Summary Analysis, Mid UBEs > 1 minute create a large report of Account, Balance, and Full Address, Long UBEs > 2 minutes simulate Payroll, Sales Order, night only jobs. The UBE workload generates large numbers of PDF files reports and log files. The UBE Queues are categorized as the QBATCHD, a single threaded queue for large and medium UBEs, and the QPROCESS queue for short UBEs run concurrently. Oracle's UBE process performance metric is Number of Maximum Concurrent UBE processes at transaction rate, UBEs/minute. Key Points and Best Practices Two JD Edwards EnterpriseOne Application Servers, two Oracle WebLogic Servers 11g Release 1 coupled with two Oracle Web Tier HTTP server instances and one Oracle Database 11g Release 2 database on a single SPARC T4-2 server were hosted in separate Oracle Solaris Containers bound to four processor sets to demonstrate consolidation of multiple applications, web servers and the database with best resource utilizations. Interrupt fencing was configured on all Oracle Solaris Containers to channel the interrupts to processors other than the processor sets used for the JD Edwards Application server, Oracle WebLogic servers and the database server. A Oracle WebLogic vertical cluster was configured on each WebServer Container with twelve managed instances each to load balance users' requests and to provide the infrastructure that enables scaling to high number of users with ease of deployment and high availability. The database log writer was run in the real time RT class and bound to a processor set. The database redo logs were configured on the raw disk partitions. The Oracle Solaris Container running the Enterprise Application server completed 61 Short UBEs, 4 Long UBEs concurrently as the mixed size batch workload. The mixed size UBEs ran concurrently from the Enterprise Application server with the 8,000 online users driven by the LoadRunner. See Also SPARC T4-2 Server oracle.com OTN JD Edwards EnterpriseOne oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Oracle Fusion Middleware oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 09/30/2012.

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  • Oracle apresenta resultados do ano

    - by pfolgado
    A Oracle acabou de apresentar os resultados do 4º trimestre e do ano fiscal FY11. Os resultados mais relevantes são: Receitas de Vendas cresceram 33%, atingindo um total de 35,6 mil milhões de dólares Vendas de Novas licenças cresceram 23% Receitas de Hardware de 4,4 mil milhões de dólares Resultados operacionais cresceram 39% Resultados por acção de cresceram 38% para 1,67 dólares “In Q4, we achieved a 19% new software license growth rate with almost no help from acquisitions,” said Oracle President and CFO, Safra Catz. “This strong organic growth combined with continuously improving operational efficiencies enabled us to deliver a 48% operating margin in the quarter. As our results reflect, we clearly exceeded even our own high expectations for Sun’s business.” “In addition to record setting software sales, our Exadata and Exalogic systems also made a strong contribution to our growth in Q4,” said Oracle President, Mark Hurd. “Today there are more than 1,000 Exadata machines installed worldwide. Our goal is to triple that number in FY12.” “In FY11 Oracle’s database business experienced its fastest growth in a decade,” said Oracle CEO, Larry Ellison. “Over the past few years we added features to the Oracle database for both cloud computing and in-memory databases that led to increased database sales this past year. Lately we’ve been focused on the big business opportunity presented by Big Data.” Oracle Reports Q4 GAAP EPS Up 34% To 62 Cents; Q4 NON-GAAP EPS Up 25% To 75 Cents Q4 Software New License Sales Up 19%, Q4 Total Revenue Up 13% Oracle today announced fiscal 2011 Q4 GAAP total revenues were up 13% to $10.8 billion, while non-GAAP total revenues were up 12% to $10.8 billion. Both GAAP and non-GAAP new software license revenues were up 19% to $3.7 billion. Both GAAP and non-GAAP software license updates and product support revenues were up 15% to $4.0 billion. Both GAAP and non-GAAP hardware systems products revenues were down 6% to $1.2 billion. GAAP operating income was up 32% to $4.4 billion, and GAAP operating margin was 40%. Non-GAAP operating income was up 19% to $5.2 billion, and non-GAAP operating margin was 48%. GAAP net income was up 36% to $3.2 billion, while non-GAAP net income was up 27% to $3.9 billion. GAAP earnings per share were $0.62, up 34% compared to last year while non-GAAP earnings per share were up 25% to $0.75. GAAP operating cash flow on a trailing twelve-month basis was $11.2 billion. For fiscal year 2011, GAAP total revenues were up 33% to $35.6 billion, while non-GAAP total revenues were up 33% to $35.9 billion. Both GAAP and non-GAAP new software license revenues were up 23% to $9.2 billion. GAAP software license updates and product support revenues were up 13% to $14.8 billion, while non-GAAP software license updates and product support revenues were up 13% to $14.9 billion. Both GAAP and non-GAAP hardware systems products revenues were $4.4 billion. GAAP operating income was up 33% to $12.0 billion, and GAAP operating margin was 34%. Non-GAAP operating income was up 27% to $15.9 billion, and non-GAAP operating margin was 44%. GAAP net income was up 39% to $8.5 billion, while non-GAAP net income was up 34% to $11.4 billion. GAAP earnings per share were $1.67, up 38% compared to last year while non-GAAP earnings per share were up 33% to $2.22. “In Q4, we achieved a 19% new software license growth rate with almost no help from acquisitions,” said Oracle President and CFO, Safra Catz. “This strong organic growth combined with continuously improving operational efficiencies enabled us to deliver a 48% operating margin in the quarter. As our results reflect, we clearly exceeded even our own high expectations for Sun’s business.” “In addition to record setting software sales, our Exadata and Exalogic systems also made a strong contribution to our growth in Q4,” said Oracle President, Mark Hurd. “Today there are more than 1,000 Exadata machines installed worldwide. Our goal is to triple that number in FY12.” “In FY11 Oracle’s database business experienced its fastest growth in a decade,” said Oracle CEO, Larry Ellison. “Over the past few years we added features to the Oracle database for both cloud computing and in-memory databases that led to increased database sales this past year. Lately we’ve been focused on the big business opportunity presented by Big Data.” In addition, Oracle also announced that its Board of Directors declared a quarterly cash dividend of $0.06 per share of outstanding common stock. This dividend will be paid to stockholders of record as of the close of business on July 13, 2011, with a payment date of August 3, 2011.

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  • How can I estimate the entropy of a password?

    - by Wug
    Having read various resources about password strength I'm trying to create an algorithm that will provide a rough estimation of how much entropy a password has. I'm trying to create an algorithm that's as comprehensive as possible. At this point I only have pseudocode, but the algorithm covers the following: password length repeated characters patterns (logical) different character spaces (LC, UC, Numeric, Special, Extended) dictionary attacks It does NOT cover the following, and SHOULD cover it WELL (though not perfectly): ordering (passwords can be strictly ordered by output of this algorithm) patterns (spatial) Can anyone provide some insight on what this algorithm might be weak to? Specifically, can anyone think of situations where feeding a password to the algorithm would OVERESTIMATE its strength? Underestimations are less of an issue. The algorithm: // the password to test password = ? length = length(password) // unique character counts from password (duplicates discarded) uqlca = number of unique lowercase alphabetic characters in password uquca = number of uppercase alphabetic characters uqd = number of unique digits uqsp = number of unique special characters (anything with a key on the keyboard) uqxc = number of unique special special characters (alt codes, extended-ascii stuff) // algorithm parameters, total sizes of alphabet spaces Nlca = total possible number of lowercase letters (26) Nuca = total uppercase letters (26) Nd = total digits (10) Nsp = total special characters (32 or something) Nxc = total extended ascii characters that dont fit into other categorys (idk, 50?) // algorithm parameters, pw strength growth rates as percentages (per character) flca = entropy growth factor for lowercase letters (.25 is probably a good value) fuca = EGF for uppercase letters (.4 is probably good) fd = EGF for digits (.4 is probably good) fsp = EGF for special chars (.5 is probably good) fxc = EGF for extended ascii chars (.75 is probably good) // repetition factors. few unique letters == low factor, many unique == high rflca = (1 - (1 - flca) ^ uqlca) rfuca = (1 - (1 - fuca) ^ uquca) rfd = (1 - (1 - fd ) ^ uqd ) rfsp = (1 - (1 - fsp ) ^ uqsp ) rfxc = (1 - (1 - fxc ) ^ uqxc ) // digit strengths strength = ( rflca * Nlca + rfuca * Nuca + rfd * Nd + rfsp * Nsp + rfxc * Nxc ) ^ length entropybits = log_base_2(strength) A few inputs and their desired and actual entropy_bits outputs: INPUT DESIRED ACTUAL aaa very pathetic 8.1 aaaaaaaaa pathetic 24.7 abcdefghi weak 31.2 H0ley$Mol3y_ strong 72.2 s^fU¬5ü;y34G< wtf 88.9 [a^36]* pathetic 97.2 [a^20]A[a^15]* strong 146.8 xkcd1** medium 79.3 xkcd2** wtf 160.5 * these 2 passwords use shortened notation, where [a^N] expands to N a's. ** xkcd1 = "Tr0ub4dor&3", xkcd2 = "correct horse battery staple" The algorithm does realize (correctly) that increasing the alphabet size (even by one digit) vastly strengthens long passwords, as shown by the difference in entropy_bits for the 6th and 7th passwords, which both consist of 36 a's, but the second's 21st a is capitalized. However, they do not account for the fact that having a password of 36 a's is not a good idea, it's easily broken with a weak password cracker (and anyone who watches you type it will see it) and the algorithm doesn't reflect that. It does, however, reflect the fact that xkcd1 is a weak password compared to xkcd2, despite having greater complexity density (is this even a thing?). How can I improve this algorithm? Addendum 1 Dictionary attacks and pattern based attacks seem to be the big thing, so I'll take a stab at addressing those. I could perform a comprehensive search through the password for words from a word list and replace words with tokens unique to the words they represent. Word-tokens would then be treated as characters and have their own weight system, and would add their own weights to the password. I'd need a few new algorithm parameters (I'll call them lw, Nw ~= 2^11, fw ~= .5, and rfw) and I'd factor the weight into the password as I would any of the other weights. This word search could be specially modified to match both lowercase and uppercase letters as well as common character substitutions, like that of E with 3. If I didn't add extra weight to such matched words, the algorithm would underestimate their strength by a bit or two per word, which is OK. Otherwise, a general rule would be, for each non-perfect character match, give the word a bonus bit. I could then perform simple pattern checks, such as searches for runs of repeated characters and derivative tests (take the difference between each character), which would identify patterns such as 'aaaaa' and '12345', and replace each detected pattern with a pattern token, unique to the pattern and length. The algorithmic parameters (specifically, entropy per pattern) could be generated on the fly based on the pattern. At this point, I'd take the length of the password. Each word token and pattern token would count as one character; each token would replace the characters they symbolically represented. I made up some sort of pattern notation, but it includes the pattern length l, the pattern order o, and the base element b. This information could be used to compute some arbitrary weight for each pattern. I'd do something better in actual code. Modified Example: Password: 1234kitty$$$$$herpderp Tokenized: 1 2 3 4 k i t t y $ $ $ $ $ h e r p d e r p Words Filtered: 1 2 3 4 @W5783 $ $ $ $ $ @W9001 @W9002 Patterns Filtered: @P[l=4,o=1,b='1'] @W5783 @P[l=5,o=0,b='$'] @W9001 @W9002 Breakdown: 3 small, unique words and 2 patterns Entropy: about 45 bits, as per modified algorithm Password: correcthorsebatterystaple Tokenized: c o r r e c t h o r s e b a t t e r y s t a p l e Words Filtered: @W6783 @W7923 @W1535 @W2285 Breakdown: 4 small, unique words and no patterns Entropy: 43 bits, as per modified algorithm The exact semantics of how entropy is calculated from patterns is up for discussion. I was thinking something like: entropy(b) * l * (o + 1) // o will be either zero or one The modified algorithm would find flaws with and reduce the strength of each password in the original table, with the exception of s^fU¬5ü;y34G<, which contains no words or patterns.

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  • Benchmarking MySQL on win7

    - by Patrick
    I've setup a nginx server running php 5.3.6 and mysql 5.5.1.3. My computer is an AMD quadcore 9650, 4gb ram, 500gb 7200rpm HD. I ran the PHP MySQL Benchmark Tool v. 0.1, and got the following results: Testing a(n) MYISAM table using 100000 rows. Successfully created database speedtestdb Sucessfully created table speedtesttable Table Type Verified: MYISAM .. Done. 100000 inserts in 19.73628 seconds or 5067 inserts per second. Done. 100000 row reads in 0.2801 seconds or 357015 row reads per second. Done. 100000 updates in 4.03876 seconds or 24760 updates per second. I'm wondering where this stands as far as performance goes, and what are some steps I can take if any to improve on this. I'm not trying to make anything fantastic, just getting a feel for how to best optimize a web server in this configuration.

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  • Cisco Catalyst 65XX and traffic shaping

    - by Nadz Goldman
    Hello! I have Cisco Catalyst 65XX, many VLANs and about ~1300 users. Users connected to some D-Link switches with second-level management. D-Link switches come to my Cisco Catalyst 65XX by VLANs. So, how I can shape traffic per user? If I use something like this: access-list 145 permit ip any host 192.168.0.1 access-list 145 permit ip any host 192.168.0.2 access-list 145 permit ip any host 192.168.0.3 ... int Gi0/1 traffic-shape group 145 128000 7936 7936 1000 will I have shape traffic per user or it will shape traffic only on interface? I mean - every user will have 128kb/s (per user) or everybody will have 128kb/s ? If it will be for everybody, then what is the solution of my question: how every user can have 128kb/s ?

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  • Mail Enabled Sharepoint 2010 Loop Detected

    - by vlannoob
    I have setup a small Sharepoint 2010 deployment and it is working fine, for now. I have run through one of the more popular step by step guides to mail enable the install and what I have is internal and external mail going to my mail enabled list hitting my Exchange 2010 server (on another Win2k8R2 box) and sitting in the submissions queue with a Loop Detected error and they progres no further. Everything appears OK as per the guide. I have setup an SMTP role on the Sharepoint box, as per the guide. I have setup a new Send Conenctor on the Exchange 2010 server, as per the guide. Any ideas on troubleshooting here?

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  • Good default for XDG_RUNTIME_DIR?

    - by cadrian
    The XDG Base Directory Specification is a very interesting spec for user directories. It also provides good default values, except for XDG_RUNTIME_DIR. Now I am writing a software that needs to create named pipes. It is a per-user client-server framework (there is a FIFO for the server and a FIFO per client). If XDG_RUNTIME_DIR is not defined, I am currently using a per-user subdirectory in /tmp — but it does not ensure all the specified conditions (viz. the paragraph starting with "The lifetime of the directory MUST be bound to the user being logged in…") Is /tmp/myserver-$USER good enough? Edit I saw elsewhere a few suggestions: . is quite unsatisfactory (at least because it is not an absolute path). I also saw /var/run/user/$USER — not bad, but that directory does not exist (at least on my box running a Debian testing)

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  • AS2 Server Software Costs

    - by CandyCo
    We're currently using Cleo LexiCom as our server software for receiving EDI transmissions via the AS2 protocol. We have 7 trading partners per year, and this runs us about $800/year for support from Cleo. We need to expand from 7 trading partners to 10 or so, and Cleo charges roughly $600 per new host, plus an expanded yearly support fee. My question(s) are: Does anyone know of a cheaper developer of AS2 server software, and perhaps one that doesn't charge per new host? Does anyone have any clue why we are being charged an upfront fee for new hosts, and if this is a standard practice for AS2 software providers? It seems really odd that we are required to pay upfront costs for this. I could completely understand an increase in the yearly support, however.

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  • NetFlow Storage Calculator

    - by javano
    I am planning to deploy a NetFlow server (using NfSen/NfDump) for harvesting data from Cisco devices; Are there standard calculations or guidelines I can use to calculate my server requirements, specifically I need to plan for storage. Is there a way of knowing how much data I will collect per day for example, given N flows? Lets say one device has 10k flows per day, this is typically XYZ MBs, so I can scale this up? If not, how many flows are you guys and girls recording per day, and how much data is this generating? Hopefully we can generate an estimate from everyone else's figures! P.S. If it makes a difference, I'll be collecting from <= 50 devices max (non more than 50Mbps each).

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  • Recommended service account setup for MS SQL Server 2005/2008

    - by boxerbucks
    We have a number of MS SQL servers in our environment running either SQL Server 2005 standard/enterprise or SQL server 2008 enterprise. Currently the SQL services are running as local service or network service and the MS recommended best practice is to run as a domain account which is what we are trying to move towards. Is the best practice with regards to domain accounts to have a separate domain account per service per server? So if we have 4 SQL services we want to run per server and we have 50 servers, we would create 50 * 4 = 200 accounts in AD? This seems excessive to me and I was wondering if anyone has any real experience with this type of setup and it's management.

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  • Auto log off windows machine after certain number of minutes

    - by Marty Heath
    I have three machines on my network, two are windows xp and one is windows 7. i would like to have all three machines log a user off if they are on for more than 60 minutes. And I would like this to be applied to the machine not on a per user basis, because I do not want this policy to apply to those users on any other machine. I have installed winexit.scr on one of the machines but the problem is that I cannot change the default value of 10 minutes for the screensaver because that is controlled through group policy, and I cannot seem to find where to change that through group policy on a per machine basis NOT on a per user basis. If I have left out any details I apologize please let me know anything that is needed

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  • Why is USB-sticks so much slower than Solid State Drives?

    - by Jonas
    From what I understand, USB flash memory and Solid State Drives are based on similar technologies, NAND flash memory. But USB-sticks is usually quite slow with a read and write speed of 5-10MB per second while Solid State Drives usually is very fast, usually 100-570MB per second. Why are Solid State Drives so much faster than USB-sticks? And why isn't USB-sticks faster than 5-10MB per second? Is it simply that SSD-drives uses parallel access to the NAND flash memory or are there other reasons?

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  • How to put 1000 lightweight server applications in the cloud

    - by Dan Bird
    The company I work for sells a commercial desktop/server app that runs on any non dedicated Windows PC or server and uses Tomcat for all interactions with the application. Customers are asking that we host their instance of the application so they don't have to run it locally on their own servers. The app is lightweight and an average server, in theory, could handle 25-50 instances before users would notice a slowdown. However only 1 instance can run per Windows instance (because the application writes to a common registry branch) so we'd need something like VMWare to create 25-50 Windows instances. We know we eventually need to reprogram to make it truly cloud-worthy but what would you recommend for a server farm or whatever for this? We don't have the setup to purchase our own servers so we must use a 3rd party. We have budgeted $500 - $1000 per year per customer for this service. Thanks in advance for your suggestions, experiences and guidance.

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  • apache - subdomains are slower?

    - by matthewsteiner
    Using apache benchmark, I ran the exact same php application at my root domain and a subdomain. Even with multiple tests and high request numbers, the requests per second perform extremely differently. I mean something like this: example.com - 1200 requests per second bench.example.com - 50 requests per second What could be affecting this? These aren't using databases or anything, just mainly getting a simple page displaying. But it's the same app for both of them, and I'm wondering why they perform so differently. Ideas?

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  • How do I analyze an Apache Bench result?

    - by Alan Hoffmeister
    I need some help with analyzing a log from Apache Bench: Benchmarking texteli.com (be patient) Completed 100 requests Completed 200 requests Completed 300 requests Completed 400 requests Completed 500 requests Completed 600 requests Completed 700 requests Completed 800 requests Completed 900 requests Completed 1000 requests Finished 1000 requests Server Software: Server Hostname: texteli.com Server Port: 80 Document Path: /4f84b59c557eb79321000dfa Document Length: 13400 bytes Concurrency Level: 200 Time taken for tests: 37.030 seconds Complete requests: 1000 Failed requests: 0 Write errors: 0 Total transferred: 13524000 bytes HTML transferred: 13400000 bytes Requests per second: 27.01 [#/sec] (mean) Time per request: 7406.024 [ms] (mean) Time per request: 37.030 [ms] (mean, across all concurrent requests) Transfer rate: 356.66 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 27 37 19.5 34 319 Processing: 80 6273 1673.7 6907 8987 Waiting: 47 3436 2085.2 3345 8856 Total: 115 6310 1675.8 6940 9022 Percentage of the requests served within a certain time (ms) 50% 6940 66% 6968 75% 6988 80% 7007 90% 7025 95% 7078 98% 8410 99% 8876 100% 9022 (longest request) What this results can tell me? Isn't 27 rps too slow?

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  • High Lock Wait ratio in MySQL

    - by FunkyChicken
    on my site I log every pageview (date,ip,referrer,page,etc) in a simple mysql table. This table gets very little selects (3 per minute), but a lot of inserts. (about 100 per second) Today I changed this table from an InnoDB table to a MEMORY table, this made sense to me to prevent unnecessary hard disk IO. I also prune this table once per minute, to make sure it never get's too big. -- Performance wise, things are running fine. But I noticed that while running tuning-primer, that my Current Lock Wait ratio is quite high. Current Lock Wait ratio = 1 : 561 My question: Should I worry about this Lock Wait Ratio? And is there something I can change in my my.cnf to improve things so that the lock wait ratio isn't so high?

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  • Do I use the FV function in Excel correctly?

    - by John
    My task: Create a table: Calculate what the revenues of e-trading will be after five years at 15 percent interest rate if we now have 15 000 EUR. Use the FV function from the Financial Group in Excel. My resolution: =FV( 15%; 5; 0; -15000). My question: Is it correct? I know the task lacks information whether the interest rate is per month or per year. I calculate it as 'per year'. My question is orientated more on the usage of the FV function. I, for example, do not understand why '-15000' and not '15000'. Also why the third parameter has to be 0? Maybe I do it wrong. Please help me solve it! Thanks in advance.

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  • Win 8 start screen resolution

    - by Abhijith
    My screen resolution is 1280x1024 running Win 8 RP I formatted my computer and reinstalled Win 8 CP because I had too many BSODs. When I installed Win 8 CP and created a local account. I had 5(or 6) tiles per column. But once I switched to the Microsoft account to get my synced wallpaper and lock-screen, the Start screen resolution changed and I got a max 3 tiles per column. The size of all metro apps including the Settings app changed and became awkwardly bigger. Is there a way to get back 5 tiles per column? Essentially changing the resolution of the start screen?

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  • AS2 Server Software Costs

    - by CandyCo
    We're currently using Cleo LexiCom as our server software for receiving EDI transmissions via the AS2 protocol. We have 7 trading partners per year, and this runs us about $800/year for support from Cleo. We need to expand from 7 trading partners to 10 or so, and Cleo charges roughly $600 per new host, plus an expanded yearly support fee. My question(s) are: Does anyone know of a cheaper developer of AS2 server software, and perhaps one that doesn't charge per new host? Does anyone have any clue why we are being charged an upfront fee for new hosts, and if this is a standard practice for AS2 software providers? It seems really odd that we are required to pay upfront costs for this. I could completely understand an increase in the yearly support, however.

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  • HAProxy overload protection

    - by user2050516
    using the HAProxy, would it be possible to configure an overload protection, to limit the amount of requests sent to the backing http server(s) to a given rate (z.B 100 Request per second ). If the threshold is exceeded requests should be answered with a default response. I am interested in requests per second not connections per second as a connection can have many requests. And yes to improve the servers is not an option here. If yes a configuration example to achieve that would be excellent. Thank you in advance.

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  • Parallelism in .NET – Part 4, Imperative Data Parallelism: Aggregation

    - by Reed
    In the article on simple data parallelism, I described how to perform an operation on an entire collection of elements in parallel.  Often, this is not adequate, as the parallel operation is going to be performing some form of aggregation. Simple examples of this might include taking the sum of the results of processing a function on each element in the collection, or finding the minimum of the collection given some criteria.  This can be done using the techniques described in simple data parallelism, however, special care needs to be taken into account to synchronize the shared data appropriately.  The Task Parallel Library has tools to assist in this synchronization. The main issue with aggregation when parallelizing a routine is that you need to handle synchronization of data.  Since multiple threads will need to write to a shared portion of data.  Suppose, for example, that we wanted to parallelize a simple loop that looked for the minimum value within a dataset: double min = double.MaxValue; foreach(var item in collection) { double value = item.PerformComputation(); min = System.Math.Min(min, value); } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This seems like a good candidate for parallelization, but there is a problem here.  If we just wrap this into a call to Parallel.ForEach, we’ll introduce a critical race condition, and get the wrong answer.  Let’s look at what happens here: // Buggy code! Do not use! double min = double.MaxValue; Parallel.ForEach(collection, item => { double value = item.PerformComputation(); min = System.Math.Min(min, value); }); This code has a fatal flaw: min will be checked, then set, by multiple threads simultaneously.  Two threads may perform the check at the same time, and set the wrong value for min.  Say we get a value of 1 in thread 1, and a value of 2 in thread 2, and these two elements are the first two to run.  If both hit the min check line at the same time, both will determine that min should change, to 1 and 2 respectively.  If element 1 happens to set the variable first, then element 2 sets the min variable, we’ll detect a min value of 2 instead of 1.  This can lead to wrong answers. Unfortunately, fixing this, with the Parallel.ForEach call we’re using, would require adding locking.  We would need to rewrite this like: // Safe, but slow double min = double.MaxValue; // Make a "lock" object object syncObject = new object(); Parallel.ForEach(collection, item => { double value = item.PerformComputation(); lock(syncObject) min = System.Math.Min(min, value); }); This will potentially add a huge amount of overhead to our calculation.  Since we can potentially block while waiting on the lock for every single iteration, we will most likely slow this down to where it is actually quite a bit slower than our serial implementation.  The problem is the lock statement – any time you use lock(object), you’re almost assuring reduced performance in a parallel situation.  This leads to two observations I’ll make: When parallelizing a routine, try to avoid locks. That being said: Always add any and all required synchronization to avoid race conditions. These two observations tend to be opposing forces – we often need to synchronize our algorithms, but we also want to avoid the synchronization when possible.  Looking at our routine, there is no way to directly avoid this lock, since each element is potentially being run on a separate thread, and this lock is necessary in order for our routine to function correctly every time. However, this isn’t the only way to design this routine to implement this algorithm.  Realize that, although our collection may have thousands or even millions of elements, we have a limited number of Processing Elements (PE).  Processing Element is the standard term for a hardware element which can process and execute instructions.  This typically is a core in your processor, but many modern systems have multiple hardware execution threads per core.  The Task Parallel Library will not execute the work for each item in the collection as a separate work item. Instead, when Parallel.ForEach executes, it will partition the collection into larger “chunks” which get processed on different threads via the ThreadPool.  This helps reduce the threading overhead, and help the overall speed.  In general, the Parallel class will only use one thread per PE in the system. Given the fact that there are typically fewer threads than work items, we can rethink our algorithm design.  We can parallelize our algorithm more effectively by approaching it differently.  Because the basic aggregation we are doing here (Min) is communitive, we do not need to perform this in a given order.  We knew this to be true already – otherwise, we wouldn’t have been able to parallelize this routine in the first place.  With this in mind, we can treat each thread’s work independently, allowing each thread to serially process many elements with no locking, then, after all the threads are complete, “merge” together the results. This can be accomplished via a different set of overloads in the Parallel class: Parallel.ForEach<TSource,TLocal>.  The idea behind these overloads is to allow each thread to begin by initializing some local state (TLocal).  The thread will then process an entire set of items in the source collection, providing that state to the delegate which processes an individual item.  Finally, at the end, a separate delegate is run which allows you to handle merging that local state into your final results. To rewriting our routine using Parallel.ForEach<TSource,TLocal>, we need to provide three delegates instead of one.  The most basic version of this function is declared as: public static ParallelLoopResult ForEach<TSource, TLocal>( IEnumerable<TSource> source, Func<TLocal> localInit, Func<TSource, ParallelLoopState, TLocal, TLocal> body, Action<TLocal> localFinally ) The first delegate (the localInit argument) is defined as Func<TLocal>.  This delegate initializes our local state.  It should return some object we can use to track the results of a single thread’s operations. The second delegate (the body argument) is where our main processing occurs, although now, instead of being an Action<T>, we actually provide a Func<TSource, ParallelLoopState, TLocal, TLocal> delegate.  This delegate will receive three arguments: our original element from the collection (TSource), a ParallelLoopState which we can use for early termination, and the instance of our local state we created (TLocal).  It should do whatever processing you wish to occur per element, then return the value of the local state after processing is completed. The third delegate (the localFinally argument) is defined as Action<TLocal>.  This delegate is passed our local state after it’s been processed by all of the elements this thread will handle.  This is where you can merge your final results together.  This may require synchronization, but now, instead of synchronizing once per element (potentially millions of times), you’ll only have to synchronize once per thread, which is an ideal situation. Now that I’ve explained how this works, lets look at the code: // Safe, and fast! double min = double.MaxValue; // Make a "lock" object object syncObject = new object(); Parallel.ForEach( collection, // First, we provide a local state initialization delegate. () => double.MaxValue, // Next, we supply the body, which takes the original item, loop state, // and local state, and returns a new local state (item, loopState, localState) => { double value = item.PerformComputation(); return System.Math.Min(localState, value); }, // Finally, we provide an Action<TLocal>, to "merge" results together localState => { // This requires locking, but it's only once per used thread lock(syncObj) min = System.Math.Min(min, localState); } ); Although this is a bit more complicated than the previous version, it is now both thread-safe, and has minimal locking.  This same approach can be used by Parallel.For, although now, it’s Parallel.For<TLocal>.  When working with Parallel.For<TLocal>, you use the same triplet of delegates, with the same purpose and results. Also, many times, you can completely avoid locking by using a method of the Interlocked class to perform the final aggregation in an atomic operation.  The MSDN example demonstrating this same technique using Parallel.For uses the Interlocked class instead of a lock, since they are doing a sum operation on a long variable, which is possible via Interlocked.Add. By taking advantage of local state, we can use the Parallel class methods to parallelize algorithms such as aggregation, which, at first, may seem like poor candidates for parallelization.  Doing so requires careful consideration, and often requires a slight redesign of the algorithm, but the performance gains can be significant if handled in a way to avoid excessive synchronization.

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  • Con Oracle l’Azienda Sanitaria della Provincia di Trento vince l'HR Innovation Award

    - by Lara Ermacora
    Il 14 giugno, si è tenuto il Convegno di presentazione dei risultati della Ricerca 2011 dell'Osservatorio HR Innovation Practice della School of Management del Politecnico di Milano. La Ricerca ha coinvolto 108 Direttori HR delle più importanti aziende operanti in Italia con l'obiettivo di comprendere l'evoluzione dei modelli organizzativi e promuovere l'innovazione dei processi di gestione e sviluppo delle Risorse Umane attraverso l'utilizzo di nuove tecnologie ICT. La presentazione dei risultati della Ricerca è stata seguita da una Tavola Rotonda a cui hanno partecipato i referenti di alcune delle principali aziende che offrono servizi e soluzioni in ambito HR e dalla consegna dei Premi “HR Innovation Award”, un’importante occasione di confronto su casi di eccellenza nell’innovazione dei processi HR . L’Azienda per i Servizi Sanitari di Trento (APSS) ha ricevuto il premio HR Innovation Award nella categoria “Valutazione delle prestazioni e gestione delle carriere”. Riconoscimento conseguito grazie al progetto di miglioramento della gestione del personale portato avanti facendo leva su Oracle PeopleSoft HCM (Human Capital Management) , la soluzione applicativa integrata di Oracle a supporto della direzione risorse umane. Il progetto nasce da una chiara esigenza dell'azienda sanitaria ad utilizzare un sistema applicativo che consentisse di migliorare i processi di gestione delle risorse umane fornendo una visione univoca delle informazioni relative a ciascun dipendente, contrariamente a quanto accadeva in passato. La scelta è caduta su Oracle Peoplesoft HCM per varie motivazioni. Prima di tutto perchè si tratta di una piattaforma unica e integrata che permette una gestione del personale snella. Questo avviene soprattutto perchè la piattaforma, ricostruendo la soria di ciascun dipendente, lo storico delle sue valutazioni e un quadro chiaro delle gerarchie aziendali, mette l’individuo al centro del sistema e consente di sviluppare assetti organizzativi e modalità operative in grado di garantire il collegamento tra tutte le fasi del processo di gestione delle risorse umane. Per maggiori informazioni sul progetto ecco una breve intervista di cui aveva già parlato ad Ettore Turra , responsabile del programma Sviluppo Risorse Umane APPS Trento:

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  • How do I align my partition table properly?

    - by Jorge Castro
    I am in the process of building my first RAID5 array. I've used mdadm to create the following set up: root@bondigas:~# mdadm --detail /dev/md1 /dev/md1: Version : 00.90 Creation Time : Wed Oct 20 20:00:41 2010 Raid Level : raid5 Array Size : 5860543488 (5589.05 GiB 6001.20 GB) Used Dev Size : 1953514496 (1863.02 GiB 2000.40 GB) Raid Devices : 4 Total Devices : 4 Preferred Minor : 1 Persistence : Superblock is persistent Update Time : Wed Oct 20 20:13:48 2010 State : clean, degraded, recovering Active Devices : 3 Working Devices : 4 Failed Devices : 0 Spare Devices : 1 Layout : left-symmetric Chunk Size : 64K Rebuild Status : 1% complete UUID : f6dc829e:aa29b476:edd1ef19:85032322 (local to host bondigas) Events : 0.12 Number Major Minor RaidDevice State 0 8 16 0 active sync /dev/sdb 1 8 32 1 active sync /dev/sdc 2 8 48 2 active sync /dev/sdd 4 8 64 3 spare rebuilding /dev/sde While that's going I decided to format the beast with the following command: root@bondigas:~# mkfs.ext4 /dev/md1p1 mke2fs 1.41.11 (14-Mar-2010) /dev/md1p1 alignment is offset by 63488 bytes. This may result in very poor performance, (re)-partitioning suggested. Filesystem label= OS type: Linux Block size=4096 (log=2) Fragment size=4096 (log=2) Stride=16 blocks, Stripe width=48 blocks 97853440 inodes, 391394047 blocks 19569702 blocks (5.00%) reserved for the super user First data block=0 Maximum filesystem blocks=0 11945 block groups 32768 blocks per group, 32768 fragments per group 8192 inodes per group Superblock backups stored on blocks: 32768, 98304, 163840, 229376, 294912, 819200, 884736, 1605632, 2654208, 4096000, 7962624, 11239424, 20480000, 23887872, 71663616, 78675968, 102400000, 214990848 Writing inode tables: ^C 27/11945 root@bondigas:~# ^C I am unsure what to do about "/dev/md1p1 alignment is offset by 63488 bytes." and how to properly partition the disks to match so I can format it properly.

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