Search Results

Search found 8083 results on 324 pages for 'total newbie'.

Page 80/324 | < Previous Page | 76 77 78 79 80 81 82 83 84 85 86 87  | Next Page >

• 1600+ 'postfix-queue' processes - OK to have this many?

  - by atomicguava

  I have a Plesk 9.5.4 CentOS server running Postfix. I had been having massive problems with the mailq being full of 'double-bounce' email messages containing errors relating to 'Queue File Write Error', but I believe these are now fixed thanks to this thread. My new problem is that when I run top, I can see lots of processes called 'postfix-queue' and have fairly high load: top - 13:59:44 up 6 days, 21:14, 1 user, load average: 2.33, 2.19, 1.96 Tasks: 1743 total, 1 running, 1742 sleeping, 0 stopped, 0 zombie Cpu(s): 5.1%us, 8.8%sy, 0.0%ni, 85.3%id, 0.8%wa, 0.0%hi, 0.0%si, 0.0%st Mem: 3145728k total, 1950640k used, 1195088k free, 0k buffers Swap: 0k total, 0k used, 0k free, 0k cached PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 1324 apache 16 0 344m 33m 5664 S 21.7 1.1 0:03.17 httpd 32443 apache 15 0 350m 36m 6864 S 14.4 1.2 0:13.83 httpd 1678 root 15 0 13948 2568 952 R 2.0 0.1 0:00.37 top 1890 mysql 15 0 689m 318m 7600 S 1.0 10.4 219:45.23 mysqld 1394 apache 15 0 352m 41m 5972 S 0.7 1.3 0:03.91 httpd 1369 apache 15 0 344m 33m 5444 S 0.3 1.1 0:02.03 httpd 1592 apache 15 0 349m 37m 5912 S 0.3 1.2 0:02.52 httpd 1633 apache 15 0 336m 20m 1828 S 0.3 0.7 0:00.01 httpd 1952 root 19 0 335m 28m 10m S 0.3 0.9 1:35.41 httpd 1 root 15 0 10304 732 612 S 0.0 0.0 0:04.41 init 1034 mhandler 15 0 11520 1160 884 S 0.0 0.0 0:00.00 postfix-queue 1036 mhandler 15 0 11516 1120 860 S 0.0 0.0 0:00.00 postfix-queue 1041 mhandler 17 0 11516 1156 884 S 0.0 0.0 0:00.00 postfix-queue 1043 mhandler 15 0 11512 1116 860 S 0.0 0.0 0:00.00 postfix-queue 1063 mhandler 16 0 11516 1160 884 S 0.0 0.0 0:00.00 postfix-queue 1068 mhandler 15 0 11516 1128 860 S 0.0 0.0 0:00.00 postfix-queue 1071 mhandler 17 0 11512 1152 884 S 0.0 0.0 0:00.00 postfix-queue 1072 mhandler 15 0 11512 1116 860 S 0.0 0.0 0:00.00 postfix-queue 1081 mhandler 16 0 11516 1156 884 S 0.0 0.0 0:00.00 postfix-queue 1082 mhandler 15 0 11512 1120 860 S 0.0 0.0 0:00.00 postfix-queue 1089 popuser 15 0 33892 1972 1200 S 0.0 0.1 0:00.02 pop3d 1116 mhandler 16 0 11516 1164 884 S 0.0 0.0 0:00.00 postfix-queue 1117 mhandler 15 0 11516 1124 860 S 0.0 0.0 0:00.00 postfix-queue 1120 mhandler 16 0 11516 1160 884 S 0.0 0.0 0:00.00 postfix-queue 1121 mhandler 15 0 11512 1120 860 S 0.0 0.0 0:00.00 postfix-queue 1130 mhandler 17 0 11516 1160 884 S 0.0 0.0 0:00.00 postfix-queue 1131 mhandler 15 0 11516 1120 860 S 0.0 0.0 0:00.00 postfix-queue 1149 root 17 -4 12572 680 356 S 0.0 0.0 0:00.00 udevd 1181 mhandler 16 0 11516 1160 884 S 0.0 0.0 0:00.00 postfix-queue 1183 mhandler 15 0 11512 1116 860 S 0.0 0.0 0:00.00 postfix-queue 1224 mhandler 16 0 11516 1160 884 S 0.0 0.0 0:00.00 postfix-queue 1225 mhandler 15 0 11516 1120 860 S 0.0 0.0 0:00.00 postfix-queue 1228 apache 15 0 345m 34m 5472 S 0.0 1.1 0:04.64 httpd 1241 mhandler 16 0 11516 1156 884 S 0.0 0.0 0:00.00 postfix-queue 1242 mhandler 15 0 11512 1120 860 S 0.0 0.0 0:00.00 postfix-queue 1251 mhandler 17 0 11516 1156 884 S 0.0 0.0 0:00.00 postfix-queue 1252 mhandler 15 0 11516 1120 860 S 0.0 0.0 0:00.00 postfix-queue 1258 apache 15 0 349m 37m 5444 S 0.0 1.2 0:01.28 httpd When I run ps -Al | grep -c postfix-queue it returns 1618! My question is this: is this normal or is there something else going wrong with Postfix? Right now, if I run mailq it is empty, and qshape deferred / qshape active are empty too. Thanks in advance for your help.

  Read the article

• RedHat 5.5 server does not show per processor memory utilization

  - by Mike S

  I have been searching all over internet but not finding any leads. I have a system with a memory leak that I am trying to troubleshoot. Unfortunately I am not able to see per processor memory utilization. Here are the outputs of TOP and PS commands. Linux SERVER_NAME 2.6.18-194.8.1.el5 #1 SMP Wed Jun 23 10:52:51 EDT 2010 x86_64 x86_64 x86_64 GNU/Linux top - 09:17:13 up 18:43, 3 users, load average: 0.00, 0.00, 0.00 Tasks: 375 total, 1 running, 373 sleeping, 0 stopped, 1 zombie Cpu(s): 0.0%us, 0.0%sy, 0.0%ni,100.0%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st Mem: 32922828k total, 32776712k used, 146116k free, 267128k buffers Swap: 5245212k total, 0k used, 5245212k free, 32141044k cached PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 1 root 15 0 10348 744 620 S 0.0 0.0 0:05.65 init 2 root RT -5 0 0 0 S 0.0 0.0 0:00.05 migration/0 3 root 34 19 0 0 0 S 0.0 0.0 0:00.00 ksoftirqd/0 4 root RT -5 0 0 0 S 0.0 0.0 0:00.00 watchdog/0 5 root RT -5 0 0 0 S 0.0 0.0 0:00.00 migration/1 6 root 34 19 0 0 0 S 0.0 0.0 0:00.00 ksoftirqd/1 7 root RT -5 0 0 0 S 0.0 0.0 0:00.00 watchdog/1 8 root RT -5 0 0 0 S 0.0 0.0 0:00.00 migration/2 9 root 34 19 0 0 0 S 0.0 0.0 0:00.00 ksoftirqd/2 10 root RT -5 0 0 0 S 0.0 0.0 0:00.00 watchdog/2 11 root RT -5 0 0 0 S 0.0 0.0 0:00.01 migration/3 12 root 34 19 0 0 0 S 0.0 0.0 0:00.01 ksoftirqd/3 13 root RT -5 0 0 0 S 0.0 0.0 0:00.00 watchdog/3 14 root RT -5 0 0 0 S 0.0 0.0 0:00.00 migration/4 15 root 34 19 0 0 0 S 0.0 0.0 0:00.01 ksoftirqd/4 16 root RT -5 0 0 0 S 0.0 0.0 0:00.00 watchdog/4 17 root RT -5 0 0 0 S 0.0 0.0 0:00.00 migration/5 18 root 34 19 0 0 0 S 0.0 0.0 0:00.00 ksoftirqd/5 19 root RT -5 0 0 0 S 0.0 0.0 0:00.00 watchdog/5 20 root RT -5 0 0 0 S 0.0 0.0 0:00.00 migration/6 % ps -auxf | sort -nr -k 4 | head -10 Warning: bad syntax, perhaps a bogus '-'? See /usr/share/doc/procps-3.2.7/FAQ xfs 6205 0.0 0.0 23316 3892 ? Ss Aug19 0:00 xfs -droppriv -daemon uuidd 6101 0.0 0.0 60976 224 ? Ss Aug19 0:00 /usr/sbin/uuidd USER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND smmsp 6130 0.0 0.0 57900 1784 ? Ss Aug19 0:00 sendmail: Queue runner@01:00:00 for /var/spool/clientmqueue rpc 5126 0.0 0.0 8052 632 ? Ss Aug19 0:00 portmap root 99 0.0 0.0 0 0 ? S< Aug19 0:00 [events/1] root 98 0.0 0.0 0 0 ? S< Aug19 0:00 [events/0] root 97 0.0 0.0 0 0 ? S< Aug19 0:00 [watchdog/31] root 96 0.0 0.0 0 0 ? SN Aug19 0:00 [ksoftirqd/31] root 95 0.0 0.0 0 0 ? S< Aug19 0:00 [migration/31] Any help with this is appretiate.

  Read the article

• Parallelism in .NET – Part 5, Partitioning of Work

  - by Reed

  When parallelizing any routine, we start by decomposing the problem.  Once the problem is understood, we need to break our work into separate tasks, so each task can be run on a different processing element.  This process is called partitioning. Partitioning our tasks is a challenging feat.  There are opposing forces at work here: too many partitions adds overhead, too few partitions leaves processors idle.  Trying to work the perfect balance between the two extremes is the goal for which we should aim.  Luckily, the Task Parallel Library automatically handles much of this process.  However, there are situations where the default partitioning may not be appropriate, and knowledge of our routines may allow us to guide the framework to making better decisions. First off, I’d like to say that this is a more advanced topic.  It is perfectly acceptable to use the parallel constructs in the framework without considering the partitioning taking place.  The default behavior in the Task Parallel Library is very well-behaved, even for unusual work loads, and should rarely be adjusted.  I have found few situations where the default partitioning behavior in the TPL is not as good or better than my own hand-written partitioning routines, and recommend using the defaults unless there is a strong, measured, and profiled reason to avoid using them.  However, understanding partitioning, and how the TPL partitions your data, helps in understanding the proper usage of the TPL. I indirectly mentioned partitioning while discussing aggregation.  Typically, our systems will have a limited number of Processing Elements (PE), which is the terminology used for hardware capable of processing a stream of instructions.  For example, in a standard Intel i7 system, there are four processor cores, each of which has two potential hardware threads due to Hyperthreading.  This gives us a total of 8 PEs – theoretically, we can have up to eight operations occurring concurrently within our system. In order to fully exploit this power, we need to partition our work into Tasks.  A task is a simple set of instructions that can be run on a PE.  Ideally, we want to have at least one task per PE in the system, since fewer tasks means that some of our processing power will be sitting idle.  A naive implementation would be to just take our data, and partition it with one element in our collection being treated as one task.  When we loop through our collection in parallel, using this approach, we’d just process one item at a time, then reuse that thread to process the next, etc.  There’s a flaw in this approach, however.  It will tend to be slower than necessary, often slower than processing the data serially. The problem is that there is overhead associated with each task.  When we take a simple foreach loop body and implement it using the TPL, we add overhead.  First, we change the body from a simple statement to a delegate, which must be invoked.  In order to invoke the delegate on a separate thread, the delegate gets added to the ThreadPool’s current work queue, and the ThreadPool must pull this off the queue, assign it to a free thread, then execute it.  If our collection had one million elements, the overhead of trying to spawn one million tasks would destroy our performance. The answer, here, is to partition our collection into groups, and have each group of elements treated as a single task.  By adding a partitioning step, we can break our total work into small enough tasks to keep our processors busy, but large enough tasks to avoid overburdening the ThreadPool.  There are two clear, opposing goals here: Always try to keep each processor working, but also try to keep the individual partitions as large as possible. When using Parallel.For, the partitioning is always handled automatically.  At first, partitioning here seems simple.  A naive implementation would merely split the total element count up by the number of PEs in the system, and assign a chunk of data to each processor.  Many hand-written partitioning schemes work in this exactly manner.  This perfectly balanced, static partitioning scheme works very well if the amount of work is constant for each element.  However, this is rarely the case.  Often, the length of time required to process an element grows as we progress through the collection, especially if we’re doing numerical computations.  In this case, the first PEs will finish early, and sit idle waiting on the last chunks to finish.  Sometimes, work can decrease as we progress, since previous computations may be used to speed up later computations.  In this situation, the first chunks will be working far longer than the last chunks.  In order to balance the workload, many implementations create many small chunks, and reuse threads.  This adds overhead, but does provide better load balancing, which in turn improves performance. The Task Parallel Library handles this more elaborately.  Chunks are determined at runtime, and start small.  They grow slowly over time, getting larger and larger.  This tends to lead to a near optimum load balancing, even in odd cases such as increasing or decreasing workloads.  Parallel.ForEach is a bit more complicated, however. When working with a generic IEnumerable<T>, the number of items required for processing is not known in advance, and must be discovered at runtime.  In addition, since we don’t have direct access to each element, the scheduler must enumerate the collection to process it.  Since IEnumerable<T> is not thread safe, it must lock on elements as it enumerates, create temporary collections for each chunk to process, and schedule this out.  By default, it uses a partitioning method similar to the one described above.  We can see this directly by looking at the Visual Partitioning sample shipped by the Task Parallel Library team, and available as part of the Samples for Parallel Programming.  When we run the sample, with four cores and the default, Load Balancing partitioning scheme, we see this: The colored bands represent each processing core.  You can see that, when we started (at the top), we begin with very small bands of color.  As the routine progresses through the Parallel.ForEach, the chunks get larger and larger (seen by larger and larger stripes). Most of the time, this is fantastic behavior, and most likely will out perform any custom written partitioning.  However, if your routine is not scaling well, it may be due to a failure in the default partitioning to handle your specific case.  With prior knowledge about your work, it may be possible to partition data more meaningfully than the default Partitioner. There is the option to use an overload of Parallel.ForEach which takes a Partitioner<T> instance.  The Partitioner<T> class is an abstract class which allows for both static and dynamic partitioning.  By overriding Partitioner<T>.SupportsDynamicPartitions, you can specify whether a dynamic approach is available.  If not, your custom Partitioner<T> subclass would override GetPartitions(int), which returns a list of IEnumerator<T> instances.  These are then used by the Parallel class to split work up amongst processors.  When dynamic partitioning is available, GetDynamicPartitions() is used, which returns an IEnumerable<T> for each partition.  If you do decide to implement your own Partitioner<T>, keep in mind the goals and tradeoffs of different partitioning strategies, and design appropriately. The Samples for Parallel Programming project includes a ChunkPartitioner class in the ParallelExtensionsExtras project.  This provides example code for implementing your own, custom allocation strategies, including a static allocator of a given chunk size.  Although implementing your own Partitioner<T> is possible, as I mentioned above, this is rarely required or useful in practice.  The default behavior of the TPL is very good, often better than any hand written partitioning strategy.

  Read the article

• What’s New in The Second Edition of Regular Expressions Cookbook

  - by Jan Goyvaerts

  %COOKBOOKFRAME% The second edition of Regular Expressions Cookbook is a completely revised edition, not just a minor update. All of the content from the first edition has been updated for the latest versions of the regular expression flavors and programming languages we discuss. We’ve corrected all errors that we could find and rewritten many sections that were either unclear or lacking in detail. And lack of detail was not something the first edition was accused of. Expect the second edition to really dot all i’s and cross all t’s. A few sections were removed. In particular, we removed much talk about browser inconsistencies as modern browsers are much more compatible with the official JavaScript standard. There is plenty of new content. The second edition has 101 more pages, bringing the total to 612. It’s almost 20% bigger than the first edition. We’ve added XRegExp as an additional regex flavor to all recipes throughout the book where XRegExp provides a better solution than standard JavaScript. We did keep the standard JavaScript solutions, so you can decide which is better for your needs. The new edition adds 21 recipes, bringing the total to 146. 14 of the new recipes are in the new Source Code and Log Files chapter. These recipes demonstrate techniques that are very useful for manipulating source code in a text editor and for dealing with log files using a grep tool. Chapter 3 which has recipes for programming with regular expressions gets only one new recipe, but it’s a doozy. If anyone has ever flamed you for using a regular expression instead of a parser, you’ll now be able to tell them how you can create your own parser by mixing regular expressions with procedural code. Combined with the recipes from the new Source Code and Log Files chapter, you can create parsers for whatever custom language or file format you like. If you have any interest in regular expressions at all, whether you’re a beginner or already consider yourself an expert, you definitely need a copy of the second edition of Regular Expressions Cookbook if you didn’t already buy the first. If you did buy the first edition, and you often find yourself referring back to it, then the second edition is a very worthwhile upgrade. You can buy the second edition of Regular Expressions Cookbook from Amazon or wherever technical books are sold. Ask for ISBN 1449319432.

  Read the article

• Video Trip Report: If this is Tuesday, this must be Cairo

  - by Latest Microsoft Blogs

  This last week over a 7 day period, I went to Munich, Cairo and Dubai. I presented in three keynotes and did a total of 10 sessions. I crossed 12 time zones and missed my kids. I talked to/with/at about 3000 people. I'm utterly shattered. I took some Read More......(read more)

  Read the article

• Speaking at Microsoft's Duth DevDays

  - by gsusx

  Last week I had the pleasure of presenting two sessions at Microsoft's Dutch DevDays at Den Hague. On Tuesday I presented a sessions about how to implement real world RESTFul services patterns using WCF, WCF Data Services and ASP.NET MVC2. During that session I showed a total of 15 small demos that highlighted how to implement key aspects of RESTful solutions such as Security, LowREST clients, URI modeling, Validation, Error Handling, etc. As part of those demos I used the OAuth implementation created...(read more)

  Read the article

• Redirect network logs from syslog to another file

  - by w0rldart

  I keep logging way to much info (not needed, for now) in my syslog, and not daily or hourly... but instant. If I want to watch for something in my syslog I just can't because the network log keeps interfering. So, how can I redirect network logs to another file and/or stop logging it? Dec 10 17:01:33 user kernel: [ 8716.000587] MediaState is connected Dec 10 17:01:33 user kernel: [ 8716.000599] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:01:33 user kernel: [ 8716.000601] ==>rt_ioctl_giwfreq 11 Dec 10 17:01:33 user kernel: [ 8716.000612] rt28xx_get_wireless_stats ---> Dec 10 17:01:33 user kernel: [ 8716.000615] <--- rt28xx_get_wireless_stats Dec 10 17:01:39 user kernel: [ 8722.000714] MediaState is connected Dec 10 17:01:39 user kernel: [ 8722.000729] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:01:39 user kernel: [ 8722.000732] ==>rt_ioctl_giwfreq 11 Dec 10 17:01:39 user kernel: [ 8722.000747] rt28xx_get_wireless_stats ---> Dec 10 17:01:39 user kernel: [ 8722.000751] <--- rt28xx_get_wireless_stats Dec 10 17:01:44 user kernel: [ 8726.904025] QuickDRS: TxTotalCnt <= 15, train back to original rate Dec 10 17:01:45 user kernel: [ 8728.003138] MediaState is connected Dec 10 17:01:45 user kernel: [ 8728.003153] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:01:45 user kernel: [ 8728.003157] ==>rt_ioctl_giwfreq 11 Dec 10 17:01:45 user kernel: [ 8728.003171] rt28xx_get_wireless_stats ---> Dec 10 17:01:45 user kernel: [ 8728.003175] <--- rt28xx_get_wireless_stats Dec 10 17:01:51 user kernel: [ 8734.004066] MediaState is connected Dec 10 17:01:51 user kernel: [ 8734.004079] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:01:51 user kernel: [ 8734.004082] ==>rt_ioctl_giwfreq 11 Dec 10 17:01:51 user kernel: [ 8734.004096] rt28xx_get_wireless_stats ---> Dec 10 17:01:51 user kernel: [ 8734.004099] <--- rt28xx_get_wireless_stats Dec 10 17:01:57 user kernel: [ 8740.004108] MediaState is connected Dec 10 17:01:57 user kernel: [ 8740.004119] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:01:57 user kernel: [ 8740.004121] ==>rt_ioctl_giwfreq 11 Dec 10 17:01:57 user kernel: [ 8740.004132] rt28xx_get_wireless_stats ---> Dec 10 17:01:57 user kernel: [ 8740.004135] <--- rt28xx_get_wireless_stats Dec 10 17:01:57 user kernel: [ 8740.436021] QuickDRS: TxTotalCnt <= 15, train back to original rate Dec 10 17:02:03 user kernel: [ 8746.005280] MediaState is connected Dec 10 17:02:03 user kernel: [ 8746.005294] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:02:03 user kernel: [ 8746.005298] ==>rt_ioctl_giwfreq 11 Dec 10 17:02:03 user kernel: [ 8746.005312] rt28xx_get_wireless_stats ---> Dec 10 17:02:03 user kernel: [ 8746.005315] <--- rt28xx_get_wireless_stats Dec 10 17:02:09 user kernel: [ 8752.004790] MediaState is connected Dec 10 17:02:09 user kernel: [ 8752.004804] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:02:09 user kernel: [ 8752.004808] ==>rt_ioctl_giwfreq 11 Dec 10 17:02:09 user kernel: [ 8752.004821] rt28xx_get_wireless_stats ---> Dec 10 17:02:09 user kernel: [ 8752.004825] <--- rt28xx_get_wireless_stats Dec 10 17:02:15 user kernel: [ 8757.984031] QuickDRS: TxTotalCnt <= 15, train back to original rate Dec 10 17:02:15 user kernel: [ 8758.004078] MediaState is connected Dec 10 17:02:15 user kernel: [ 8758.004094] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:02:15 user kernel: [ 8758.004097] ==>rt_ioctl_giwfreq 11 Dec 10 17:02:15 user kernel: [ 8758.004112] rt28xx_get_wireless_stats ---> Dec 10 17:02:15 user kernel: [ 8758.004116] <--- rt28xx_get_wireless_stats Dec 10 17:02:16 user kernel: [ 8759.492017] QuickDRS: TxTotalCnt <= 15, train back to original rate Dec 10 17:02:19 user kernel: [ 8762.002179] SCANNING, suspend MSDU transmission ... Dec 10 17:02:19 user kernel: [ 8762.004291] MlmeScanReqAction -- Send PSM Data frame for off channel RM, SCAN_IN_PROGRESS=1! Dec 10 17:02:19 user kernel: [ 8762.025055] SYNC - BBP R4 to 20MHz.l Dec 10 17:02:19 user kernel: [ 8762.027249] RT35xx: SwitchChannel#1(RF=8, Pwr0=30, Pwr1=25, 2T), N=0xF1, K=0x02, R=0x02 Dec 10 17:02:19 user kernel: [ 8762.170206] RT35xx: SwitchChannel#2(RF=8, Pwr0=30, Pwr1=25, 2T), N=0xF1, K=0x07, R=0x02 Dec 10 17:02:19 user kernel: [ 8762.318211] RT35xx: SwitchChannel#3(RF=8, Pwr0=30, Pwr1=25, 2T), N=0xF2, K=0x02, R=0x02 Dec 10 17:02:19 user kernel: [ 8762.462269] RT35xx: SwitchChannel#4(RF=8, Pwr0=30, Pwr1=25, 2T), N=0xF2, K=0x07, R=0x02 Dec 10 17:02:19 user kernel: [ 8762.606229] RT35xx: SwitchChannel#5(RF=8, Pwr0=30, Pwr1=25, 2T), N=0xF3, K=0x02, R=0x02 Dec 10 17:02:19 user kernel: [ 8762.750202] RT35xx: SwitchChannel#6(RF=8, Pwr0=30, Pwr1=25, 2T), N=0xF3, K=0x07, R=0x02 Dec 10 17:02:20 user kernel: [ 8762.894217] RT35xx: SwitchChannel#7(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF4, K=0x02, R=0x02 Dec 10 17:02:20 user kernel: [ 8763.038202] RT35xx: SwitchChannel#11(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF6, K=0x02, R=0x02 Dec 10 17:02:20 user kernel: [ 8763.040194] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:20 user kernel: [ 8763.040199] BAR(1) : Tid (0) - 03a3:037e Dec 10 17:02:20 user kernel: [ 8763.040387] SYNC - End of SCAN, restore to channel 11, Total BSS[03] Dec 10 17:02:20 user kernel: [ 8763.040400] ScanNextChannel -- Send PSM Data frame Dec 10 17:02:20 user kernel: [ 8763.040402] bFastRoamingScan ~~~~~~~~~~~~~ Get back to send data ~~~~~~~~~~~~~ Dec 10 17:02:20 user kernel: [ 8763.040405] SCAN done, resume MSDU transmission ... Dec 10 17:02:20 user kernel: [ 8763.047022] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:20 user kernel: [ 8763.047026] BAR(1) : Tid (0) - 03a3:03a5 Dec 10 17:02:21 user kernel: [ 8763.898130] bImprovedScan ............. Resume for bImprovedScan, SCAN_PENDING .............. Dec 10 17:02:21 user kernel: [ 8763.898143] SCANNING, suspend MSDU transmission ... Dec 10 17:02:21 user kernel: [ 8763.900245] MlmeScanReqAction -- Send PSM Data frame for off channel RM, SCAN_IN_PROGRESS=1! Dec 10 17:02:21 user kernel: [ 8763.921144] SYNC - BBP R4 to 20MHz.l Dec 10 17:02:21 user kernel: [ 8763.923339] RT35xx: SwitchChannel#8(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF4, K=0x07, R=0x02 Dec 10 17:02:21 user kernel: [ 8763.996019] QuickDRS: TxTotalCnt <= 15, train back to original rate Dec 10 17:02:21 user kernel: [ 8764.066221] RT35xx: SwitchChannel#9(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF5, K=0x02, R=0x02 Dec 10 17:02:21 user kernel: [ 8764.210212] RT35xx: SwitchChannel#10(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF5, K=0x07, R=0x02 Dec 10 17:02:21 user kernel: [ 8764.215536] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:21 user kernel: [ 8764.215542] BAR(1) : Tid (0) - 0457:0452 Dec 10 17:02:21 user kernel: [ 8764.244000] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:21 user kernel: [ 8764.244004] BAR(1) : Tid (0) - 0459:0456 Dec 10 17:02:21 user kernel: [ 8764.253019] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:21 user kernel: [ 8764.253023] BAR(1) : Tid (0) - 045c:0458 Dec 10 17:02:21 user kernel: [ 8764.256677] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:21 user kernel: [ 8764.256681] BAR(1) : Tid (0) - 045c:045b Dec 10 17:02:21 user kernel: [ 8764.259785] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:21 user kernel: [ 8764.259788] BAR(1) : Tid (0) - 045d:045b Dec 10 17:02:21 user kernel: [ 8764.280467] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:21 user kernel: [ 8764.280471] BAR(1) : Tid (0) - 045f:045c Dec 10 17:02:21 user kernel: [ 8764.282189] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:21 user kernel: [ 8764.282192] BAR(1) : Tid (0) - 045f:045e Dec 10 17:02:21 user kernel: [ 8764.354204] RT35xx: SwitchChannel#11(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF6, K=0x02, R=0x02 Dec 10 17:02:21 user kernel: [ 8764.356408] ScanNextChannel():Send PWA NullData frame to notify the associated AP! Dec 10 17:02:21 user kernel: [ 8764.498202] RT35xx: SwitchChannel#12(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF6, K=0x07, R=0x02 Dec 10 17:02:21 user kernel: [ 8764.642210] RT35xx: SwitchChannel#13(RF=8, Pwr0=30, Pwr1=28, 2T), N=0xF7, K=0x02, R=0x02 Dec 10 17:02:22 user kernel: [ 8764.790229] RT35xx: SwitchChannel#14(RF=8, Pwr0=30, Pwr1=28, 2T), N=0xF8, K=0x04, R=0x02 Dec 10 17:02:22 user kernel: [ 8764.934238] RT35xx: SwitchChannel#11(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF6, K=0x02, R=0x02 Dec 10 17:02:22 user kernel: [ 8764.935243] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:22 user kernel: [ 8764.935249] BAR(1) : Tid (0) - 048e:0485 Dec 10 17:02:22 user kernel: [ 8764.936423] SYNC - End of SCAN, restore to channel 11, Total BSS[05] Dec 10 17:02:22 user kernel: [ 8764.936436] ScanNextChannel -- Send PSM Data frame Dec 10 17:02:22 user kernel: [ 8764.936440] SCAN done, resume MSDU transmission ... Dec 10 17:02:22 user kernel: [ 8764.940529] RT35xx: SwitchChannel#11(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF6, K=0x02, R=0x02 Dec 10 17:02:22 user kernel: [ 8764.942178] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:22 user kernel: [ 8764.942182] BAR(1) : Tid (0) - 0493:048e Dec 10 17:02:22 user kernel: [ 8764.942715] CNTL - All roaming failed, restore to channel 11, Total BSS[05] Dec 10 17:02:22 user kernel: [ 8764.948016] MMCHK - No BEACON. restore R66 to the low bound(56) Dec 10 17:02:22 user kernel: [ 8764.948307] ===>rt_ioctl_giwscan. 5(5) BSS returned, data->length = 1111 Dec 10 17:02:23 user kernel: [ 8766.048073] QuickDRS: TxTotalCnt <= 15, train back to original rate Dec 10 17:02:23 user kernel: [ 8766.552034] QuickDRS: TxTotalCnt <= 15, train back to original rate Dec 10 17:02:27 user kernel: [ 8770.001180] MediaState is connected Dec 10 17:02:27 user kernel: [ 8770.001197] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:02:27 user kernel: [ 8770.001201] ==>rt_ioctl_giwfreq 11 Dec 10 17:02:27 user kernel: [ 8770.001219] rt28xx_get_wireless_stats ---> Dec 10 17:02:27 user kernel: [ 8770.001223] <--- rt28xx_get_wireless_stats Dec 10 17:02:28 user kernel: [ 8771.564020] QuickDRS: TxTotalCnt <= 15, train back to original rate Dec 10 17:02:29 user kernel: [ 8772.064031] QuickDRS: TxTotalCnt <= 15, train back to original rate

  Read the article

• Monitor your Hard Drive’s Health with Acronis Drive Monitor

  - by Matthew Guay

  Are you worried that your computer’s hard drive could die without any warning?  Here’s how you can keep tabs on it and get the first warning signs of potential problems before you actually lose your critical data. Hard drive failures are one of the most common ways people lose important data from their computers.  As more of our memories and important documents are stored digitally, a hard drive failure can mean the loss of years of work.  Acronis Drive Monitor helps you avert these disasters by warning you at the first signs your hard drive may be having trouble.  It monitors many indicators, including heat, read/write errors, total lifespan, and more. It then notifies you via a taskbar popup or email that problems have been detected.  This early warning lets you know ahead of time that you may need to purchase a new hard drive and migrate your data before it’s too late. Getting Started Head over to the Acronis site to download Drive Monitor (link below).  You’ll need to enter your name and email, and then you can download this free tool. Also, note that the download page may ask if you want to include a trial of their for-pay backup program.  If you wish to simply install the Drive Monitor utility, click Continue without adding. Run the installer when the download is finished.  Follow the prompts and install as normal. Once it’s installed, you can quickly get an overview of your hard drives’ health.  Note that it shows 3 categories: Disk problems, Acronis backup, and Critical Events.  On our computer, we had Seagate DiskWizard, an image backup utility based on Acronis Backup, installed, and Acronis detected it. Drive Monitor stays running in your tray even when the application window is closed.  It will keep monitoring your hard drives, and will alert you if there’s a problem. Find Detailed Information About Your Hard Drives Acronis’ simple interface lets you quickly see an overview of how the drives on your computer are performing.  If you’d like more information, click the link under the description.  Here we see that one of our drives have overheated, so click Show disks to get more information. Now you can select each of your drives and see more information about them.  From the Disk overview tab that opens by default, we see that our drive is being monitored, has been running for a total of 368 days, and that it’s health is good.  However, it is running at 113F, which is over the recommended max of 107F.   The S.M.A.R.T. parameters tab gives us more detailed information about our drive.  Most users wouldn’t know what an accepted value would be, so it also shows the status.  If the value is within the accepted parameters, it will report OK; otherwise, it will show that has a problem in this area. One very interesting piece of information we can see is the total number of Power-On Hours, Start/Stop Count, and Power Cycle Count.  These could be useful indicators to check if you’re considering purchasing a second hand computer.  Simply load this program, and you’ll get a better view of how long it’s been in use. Finally, the Events tab shows each time the program gave a warning.  We can see that our drive, which had been acting flaky already, is routinely overheating even when our other hard drive was running in normal temperature ranges. Monitor Acronis Backups And Critical Errors In addition to monitoring critical stats of your hard drives, Acronis Drive Monitor also keeps up with the status of your backup software and critical events reported by Windows.  You can access these from the front page, or via the links on the left hand sidebar.  If you have any edition of any Acronis Backup product installed, it will show that it was detected.  Note that it can only monitor the backup status of the newest versions of Acronis Backup and True Image. If no Acronis backup software was installed, it will show a warning that the drive may be unprotected and will give you a link to download Acronis backup software.   If you have another backup utility installed that you wish to monitor yourself, click Configure backup monitoring, and then disable monitoring on the drives you’re monitoring yourself. Finally, you can view any detected Critical events from the Critical events tab on the left. Get Emailed When There’s a Problem One of Drive Monitor’s best features is the ability to send you an email whenever there’s a problem.  Since this program can run on any version of Windows, including the Server and Home Server editions, you can use this feature to stay on top of your hard drives’ health even when you’re not nearby.  To set this up, click Options in the top left corner. Select Alerts on the left, and then click the Change settings link to setup your email account. Enter the email address which you wish to receive alerts, and a name for the program.  Then, enter the outgoing mail server settings for your email.  If you have a Gmail account, enter the following information: Outgoing mail server (SMTP): smtp.gmail.com Port: 587 Username and Password: Your gmail address and password Check the Use encryption box, and then select TLS from the encryption options.   It will now send a test message to your email account, so check and make sure it sent ok. Now you can choose to have the program automatically email you when warnings and critical alerts appear, and also to have it send regular disk status reports.   Conclusion Whether you’ve got a brand new hard drive or one that’s seen better days, knowing the real health of your it is one of the best ways to be prepared before disaster strikes.  It’s no substitute for regular backups, but can help you avert problems.  Acronis Drive Monitor is a nice tool for this, and although we wish it wasn’t so centered around their backup offerings, we still found it a nice tool. Link Download Acronis Drive Monitor (registration required) Similar Articles Productive Geek Tips Quick Tip: Change Monitor Timeout From Command LineAnalyze and Manage Hard Drive Space with WinDirStatMonitor CPU, Memory, and Disk IO In Windows 7 with Taskbar MetersDefrag Multiple Hard Drives At Once In WindowsFind Your Missing USB Drive on Windows XP TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips HippoRemote Pro 2.2 Xobni Plus for Outlook All My Movies 5.9 CloudBerry Online Backup 1.5 for Windows Home Server Windows 7’s WordPad is Actually Good Greate Image Viewing and Management with Zoner Photo Studio Free Windows Media Player Plus! – Cool WMP Enhancer Get Your Team’s World Cup Schedule In Google Calendar Backup Drivers With Driver Magician TubeSort: YouTube Playlist Organizer

  Read the article

• Tom Cruise: Meet Fusion Apps UX and Feel the Speed

  - by ultan o'broin

  Unfortunately, I am old enough to remember, and now to admit that I really loved, the movie Top Gun. You know the one - Tom Cruise, US Navy F-14 ace pilot, Mr Maverick, crisis of confidence, meets woman, etc., etc. Anyway, one of more memorable lines (there were a few) was: "I feel the need, the need for speed." I was reminded of Tom Cruise recently. Paraphrasing a certain Senior Vice President talking about Oracle Fusion Applications and user experience at an all-hands meeting, I heard that: Applications can never be too easy to use. Performance can never be too fast. Developers, assume that your code is always "on". Perfect. You cannot overstate the user experience importance of application speed to users, or at least their perception of speed. We all want that super speed of execution and performance, and increasingly so as enterprise users bring the expectations of consumer IT into the work environment. Sten Vesterli (@stenvesterli), an Oracle Fusion Applications User Experience Advocate, also addressed the speed point artfully at an Oracle Usability Advisory Board meeting in Geneva. Sten asked us that when we next Googled something, to think about the message we see that Google has found hundreds of thousands or millions of results for us in a split second (for example, About 8,340,000 results (0.23 seconds)). Now, how many results can we see and how many can we use immediately? Yet, this simple message communicating the total results available to us works a special magic about speed, delight, and excitement that Google has made its own in the search space. And, guess what? The Oracle Application Development Framework table component relies on a similar "virtual performance boost", says Sten, when it displays the first 50 records in a table, and uses a scrollbar indicating the total size of the data record set. The user scrolls and the application automatically retrieves more records as needed. Application speed and its perception by users is worth bearing in mind the next time you're at a customer site and the IT Department demands that you retrieve every record from the database. Just think of... Dave Ensor: I'll give you all the rows you ask for in one second. If you promise to use them. (Again, hat tip to Sten.) And then maybe think of... Tom Cruise. And if you want to read about the speed of Oracle Fusion Applications, and what that really means in terms of user productivity for your entire business, then check out the Oracle Applications User Experience Oracle Fusion Applications white papers on the usable apps website.

  Read the article

• Robotic Arm &ndash; Hardware

  - by Szymon Kobalczyk

  This is first in series of articles about project I've been building  in my spare time since last Summer. Actually it all began when I was researching a topic of modeling human motion kinematics in order to create gesture recognition library for Kinect. This ties heavily into motion theory of robotic manipulators so I also glanced at some designs of robotic arms. Somehow I stumbled upon this cool looking open source robotic arm: It was featured on Thingiverse and published by user jjshortcut (Jan-Jaap). Since for some time I got hooked on toying with microcontrollers, robots and other electronics, I decided to give it a try and build it myself. In this post I will describe the hardware build of the arm and in later posts I will be writing about the software to control it. Another reason to build the arm myself was the cost factor. Even small commercial robotic arms are quite expensive – products from Lynxmotion and Dagu look great but both cost around USD $300 (actually there is one cheap arm available but it looks more like a toy to me). In comparison this design is quite cheap. It uses seven hobby grade servos and even the cheapest ones should work fine. The structure is build from a set of laser cut parts connected with few metal spacers (15mm and 47mm) and lots of M3 screws. Other than that you’d only need a microcontroller board to drive the servos. So in total it comes a lot cheaper to build it yourself than buy an of the shelf robotic arm. Oh, and if you don’t like this one there are few more robotic arm projects at Thingiverse (including one by oomlout). Laser cut parts Some time ago I’ve build another robot using laser cut parts so I knew the process already. You can grab the design files in both DXF and EPS format from Thingiverse, and there are also 3D models of each part in STL. Actually the design is split into a second project for the mini servo gripper (there is also a standard servo version available but it won’t fit this arm).  I wanted to make some small adjustments, layout, and add measurements to the parts before sending it for cutting. I’ve looked at some free 2D CAD programs, and finally did all this work using QCad 3 Beta with worked great for me (I also tried LibreCAD but it didn’t work that well). All parts are cut from 4 mm thick material. Because I was worried that acrylic is too fragile and might break, I also ordered another set cut from plywood. In the end I build it from plywood because it was easier to glue (I was told acrylic requires a special glue). Btw. I found a great laser cutter service in Kraków and highly recommend it (www.ebbox.com.pl). It cost me only USD $26 for both sets ($16 acrylic + $10 plywood). Metal parts I bought all the M3 screws and nuts at local hardware store. Make sure to look for nylon lock (nyloc) nuts for the gripper because otherwise it unscrews and comes apart quickly. I couldn’t find local store with metal spacers and had to order them online (you’d need 11 x 47mm and 3 x 15mm). I think I paid less than USD $10 for all metal parts. Servos This arm uses five standards size servos to drive the arm itself, and two micro servos are used on the gripper. Author of the project used Modelcraft RS-2 Servo and Modelcraft ES-05 HT Servo. I had two Futaba S3001 servos laying around, and ordered additional TowerPro SG-5010 standard size servos and TowerPro SG90 micro servos. However it turned out that the SG90 won’t fit in the gripper so I had to replace it with a slightly smaller E-Sky EK2-0508 micro servo. Later it also turned out that Futaba servos make some strange noise while working so I swapped one with TowerPro SG-5010 which has higher torque (8kg / cm). I’ve also bought three servo extension cables. All servos cost me USD $45. Assembly The build process is not difficult but you need to think carefully about order of assembling it. You can do the base and upper arm first. Because two servos in the base are close together you need to put first with one piece of lower arm already connected before you put the second servo. Then you connect the upper arm and finally put the second piece of lower arm to hold it together. Gripper and base require some gluing so think it through too. Make sure to look closely at all the photos on Thingiverse (also other people copies) and read additional posts on jjshortcust’s blog: My mini servo grippers and completed robotic arm  Multiply the robotic arm and electronics Here is also Rob’s copy cut from aluminum My assembled arm looks like this – I think it turned out really nice: Servo controller board The last piece of hardware I needed was an electronic board that would take command from PC and drive all seven servos. I could probably use Arduino for this task, and in fact there are several Arduino servo shields available (for example from Adafruit or Renbotics).  However one problem is that most support only up to six servos, and second that their accuracy is limited by Arduino’s timer frequency. So instead I looked for dedicated servo controller and found a series of Maestro boards from Pololu. I picked the Pololu Mini Maestro 12-Channel USB Servo Controller. It has many nice features including native USB connection, high resolution pulses (0.25µs) with no jitter, built-in speed and acceleration control, and even scripting capability. Another cool feature is that besides servo control, each channel can be configured as either general input or output. So far I’m using seven channels so I still have five available to connect some sensors (for example distance sensor mounted on gripper might be useful). And last but important factor was that they have SDK in .NET – what more I could wish for! The board itself is very small – half of the size of Tic-Tac box. I picked one for about USD $35 in this store. Perhaps another good alternative would be the Phidgets Advanced Servo 8-Motor – but it is significantly more expensive at USD $87.30. The Maestro Controller Driver and Software package includes Maestro Control Center program with lets you immediately configure the board. For each servo I first figured out their move range and set the min/max limits. I played with setting the speed an acceleration values as well. Big issue for me was that there are two servos that control position of lower arm (shoulder joint), and both have to be moved at the same time. This is where the scripting feature of Pololu board turned out very helpful. I wrote a script that synchronizes position of second servo with first one – so now I only need to move one servo and other will follow automatically. This turned out tricky because I couldn’t find simple offset mapping of the move range for each servo – I had to divide it into several sub-ranges and map each individually. The scripting language is bit assembler-like but gets the job done. And there is even a runtime debugging and stack view available. Altogether I’m very happy with the Pololu Mini Maestro Servo Controller, and with this final piece I completed the build and was able to move my arm from the Meastro Control program.   The total cost of my robotic arm was: $10 laser cut parts $10 metal parts $45 servos $35 servo controller ----------------------- $100 total So here you have all the information about the hardware. In next post I’ll start talking about the software that I wrote in Microsoft Robotics Developer Studio 4. Stay tuned!

  Read the article

• Enterprise Cloud Computing: Risk and Economics

  Cloud computing can help optimize a company's capital investments by reducing its costs for hardware, software and real estate, resulting in a much lower total cost of ownership and, ultimately, a whole new way of looking at the economics of operational IT.

  Read the article

• Roughly, what percentage of “business” users have .NET 2.0, 3.0, 3.5, 4.0 installed?

  - by Dan W

  Home use has already (somewhat) been established, but I'm curious about business users. Approximately, what percentage of business users worldwide have .NET 3.5 runtime installed? Client profile will do, since that's what I compile my app to (though others may be interested in the full, so maybe answer that too). I'm only looking for rough estimates, but I'd like to hear separate percentage figures for: 2.0, 3.0, 3.5, 3.5 CP, 4.0, 4.0 CP, 4.5 (note: percentages won't total 100%, since many users can have two or more .NET versions simultaneously).

  Read the article

• Mix metrics for April 5, 2010

  - by tim.bonnemann

  Our latest numbers... Registered Mix users (weekly growth) 61,374 (+0.6%) Active users (percent of total) Last 30 days: 4,317 (7.0%) Last 60 days: 8,638 (14.1%) Last 90 days: 12,481 (20.3%) Traffic (30-day) Visits: 11,893 Page views: 65,880 Twitter Followers: 3,169 List mentions: 146 User-generated content (30-day) New ideas: 36 New questions: 57 New comments: 394 Groups There are currently 1,402 Mix groups (requires login).

  Read the article

• Mix metrics for March 22, 2010

  - by tim.bonnemann

  Mix hit another major milestone this past week, surpassing 60,000 registered members. Registered Mix users (weekly growth) 60,662 (+0.8%) Active users (percent of total) Last 30 days: 4,571 (7.5%) Last 60 days: 8,945 (14.7%) Last 90 days: 11,479 (18.9%) Traffic (30-day) Visits: 12,371 Page views: 70,896 Twitter Followers: 3,117 List mentions: 146 User-generated content (30-day) New ideas: 32 New questions: 74 New comments: 378 Groups There are currently 1,394 Mix groups (requires login).

  Read the article

• DundeeWealth Selects Oracle CRM On Demand as Core Platform

  - by andrea.mulder

  "Oracle CRM On Demand enhances our existing Oracle platform, providing an integrated solution with incredible flexibility, mobility, agility and lowered total cost of ownership," said To Anh Tran, Senior Vice President of Business Transformation and Technology at DundeeWealth Inc. "Using Oracle as a partner in the expansion of DundeeWealth's CRM processes reinforces our client-centric approach to customer service and we believe it gives us a competitive advantage. As we begin our deployment, we are confident that Oracle is with us every step of the way." Click here to read more about more about DundeeWealth's plans.

  Read the article

• Exam 71-513: TS: Windows Communication Foundation Development with Microsoft .NET Framework 4

  - by Ricardo Peres

  Just took the 70-513 exam. Here are some thoughts: 84 questions 240 minutes total time Lots of questions on transactions Lots of questions on sessions and callbacks Some questions on scripting (jQuery) Some questions on throttling Absolutely no questions on interception As for the other exams, I still don't know the result, but I think I may have passed...

  Read the article

• 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 

  Read the article

• Kinect va entrer au Guiness Book des Records, il est « l'appareil électronique de grande consommation qui s'est vendu le plus rapidement »

  Kinect va entrer au Guiness Book des Records Il est l'appareil électronique de grande consommation qui s'est vendu le plus rapidement Mise à jour du 10/03/11 Le Guinness World Records, l'autorité mondiale en matière de records, a confirmé aujourd'hui que Kinect, le capteur de mouvement de Microsoft pour la Xbox 360, était « l'appareil électronique de grande consommation qui s'est vendu le plus rapidement ».*Le périphérique s'est en effet vendu à une moyenne de 133.333 unités par jour, pour un total de 8 millions d'unités dans ses 60 premiers jours (lire ci-avant).*« Les chiffres de vente dépassent ceux de l'iPhone et de l'iPad pour leurs périodes ...

  Read the article

• Of C# Iterators and Performance

  - by James Michael Hare

  Some of you reading this will be wondering, "what is an iterator" and think I'm locked in the world of C++.  Nope, I'm talking C# iterators.  No, not enumerators, iterators.   So, for those of you who do not know what iterators are in C#, I will explain it in summary, and for those of you who know what iterators are but are curious of the performance impacts, I will explore that as well.   Iterators have been around for a bit now, and there are still a bunch of people who don't know what they are or what they do.  I don't know how many times at work I've had a code review on my code and have someone ask me, "what's that yield word do?"   Basically, this post came to me as I was writing some extension methods to extend IEnumerable<T> -- I'll post some of the fun ones in a later post.  Since I was filtering the resulting list down, I was using the standard C# iterator concept; but that got me wondering: what are the performance implications of using an iterator versus returning a new enumeration?   So, to begin, let's look at a couple of methods.  This is a new (albeit contrived) method called Every(...).  The goal of this method is to access and enumeration and return every nth item in the enumeration (including the first).  So Every(2) would return items 0, 2, 4, 6, etc.   Now, if you wanted to write this in the traditional way, you may come up with something like this:       public static IEnumerable<T> Every<T>(this IEnumerable<T> list, int interval)     {         List<T> newList = new List<T>();         int count = 0;           foreach (var i in list)         {             if ((count++ % interval) == 0)             {                 newList.Add(i);             }         }           return newList;     }     So basically this method takes any IEnumerable<T> and returns a new IEnumerable<T> that contains every nth item.  Pretty straight forward.   The problem?  Well, Every<T>(...) will construct a list containing every nth item whether or not you care.  What happens if you were searching this result for a certain item and find that item after five tries?  You would have generated the rest of the list for nothing.   Enter iterators.  This C# construct uses the yield keyword to effectively defer evaluation of the next item until it is asked for.  This can be very handy if the evaluation itself is expensive or if there's a fair chance you'll never want to fully evaluate a list.   We see this all the time in Linq, where many expressions are chained together to do complex processing on a list.  This would be very expensive if each of these expressions evaluated their entire possible result set on call.    Let's look at the same example function, this time using an iterator:       public static IEnumerable<T> Every<T>(this IEnumerable<T> list, int interval)     {         int count = 0;         foreach (var i in list)         {             if ((count++ % interval) == 0)             {                 yield return i;             }         }     }   Notice it does not create a new return value explicitly, the only evidence of a return is the "yield return" statement.  What this means is that when an item is requested from the enumeration, it will enter this method and evaluate until it either hits a yield return (in which case that item is returned) or until it exits the method or hits a yield break (in which case the iteration ends.   Behind the scenes, this is all done with a class that the CLR creates behind the scenes that keeps track of the state of the iteration, so that every time the next item is asked for, it finds that item and then updates the current position so it knows where to start at next time.   It doesn't seem like a big deal, does it?  But keep in mind the key point here: it only returns items as they are requested. Thus if there's a good chance you will only process a portion of the return list and/or if the evaluation of each item is expensive, an iterator may be of benefit.   This is especially true if you intend your methods to be chainable similar to the way Linq methods can be chained.    For example, perhaps you have a List<int> and you want to take every tenth one until you find one greater than 10.  We could write that as:       List<int> someList = new List<int>();         // fill list here         someList.Every(10).TakeWhile(i => i <= 10);     Now is the difference more apparent?  If we use the first form of Every that makes a copy of the list.  It's going to copy the entire list whether we will need those items or not, that can be costly!    With the iterator version, however, it will only take items from the list until it finds one that is > 10, at which point no further items in the list are evaluated.   So, sounds neat eh?  But what's the cost is what you're probably wondering.  So I ran some tests using the two forms of Every above on lists varying from 5 to 500,000 integers and tried various things.    Now, iteration isn't free.  If you are more likely than not to iterate the entire collection every time, iterator has some very slight overhead:   Copy vs Iterator on 100% of Collection (10,000 iterations) Collection Size Num Iterated Type Total ms 5 5 Copy 5 5 5 Iterator 5 50 50 Copy 28 50 50 Iterator 27 500 500 Copy 227 500 500 Iterator 247 5000 5000 Copy 2266 5000 5000 Iterator 2444 50,000 50,000 Copy 24,443 50,000 50,000 Iterator 24,719 500,000 500,000 Copy 250,024 500,000 500,000 Iterator 251,521   Notice that when iterating over the entire produced list, the times for the iterator are a little better for smaller lists, then getting just a slight bit worse for larger lists.  In reality, given the number of items and iterations, the result is near negligible, but just to show that iterators come at a price.  However, it should also be noted that the form of Every that returns a copy will have a left-over collection to garbage collect.   However, if we only partially evaluate less and less through the list, the savings start to show and make it well worth the overhead.  Let's look at what happens if you stop looking after 80% of the list:   Copy vs Iterator on 80% of Collection (10,000 iterations) Collection Size Num Iterated Type Total ms 5 4 Copy 5 5 4 Iterator 5 50 40 Copy 27 50 40 Iterator 23 500 400 Copy 215 500 400 Iterator 200 5000 4000 Copy 2099 5000 4000 Iterator 1962 50,000 40,000 Copy 22,385 50,000 40,000 Iterator 19,599 500,000 400,000 Copy 236,427 500,000 400,000 Iterator 196,010       Notice that the iterator form is now operating quite a bit faster.  But the savings really add up if you stop on average at 50% (which most searches would typically do):     Copy vs Iterator on 50% of Collection (10,000 iterations) Collection Size Num Iterated Type Total ms 5 2 Copy 5 5 2 Iterator 4 50 25 Copy 25 50 25 Iterator 16 500 250 Copy 188 500 250 Iterator 126 5000 2500 Copy 1854 5000 2500 Iterator 1226 50,000 25,000 Copy 19,839 50,000 25,000 Iterator 12,233 500,000 250,000 Copy 208,667 500,000 250,000 Iterator 122,336   Now we see that if we only expect to go on average 50% into the results, we tend to shave off around 40% of the time.  And this is only for one level deep.  If we are using this in a chain of query expressions it only adds to the savings.   So my recommendation?  If you have a resonable expectation that someone may only want to partially consume your enumerable result, I would always tend to favor an iterator.  The cost if they iterate the whole thing does not add much at all -- and if they consume only partially, you reap some really good performance gains.   Next time I'll discuss some of my favorite extensions I've created to make development life a little easier and maintainability a little better.

  Read the article

• C#: LINQ vs foreach - Round 1.

  - by James Michael Hare

  So I was reading Peter Kellner's blog entry on Resharper 5.0 and its LINQ refactoring and thought that was very cool.  But that raised a point I had always been curious about in my head -- which is a better choice: manual foreach loops or LINQ?    The answer is not really clear-cut.  There are two sides to any code cost arguments: performance and maintainability.  The first of these is obvious and quantifiable.  Given any two pieces of code that perform the same function, you can run them side-by-side and see which piece of code performs better.   Unfortunately, this is not always a good measure.  Well written assembly language outperforms well written C++ code, but you lose a lot in maintainability which creates a big techncial debt load that is hard to offset as the application ages.  In contrast, higher level constructs make the code more brief and easier to understand, hence reducing technical cost.   Now, obviously in this case we're not talking two separate languages, we're comparing doing something manually in the language versus using a higher-order set of IEnumerable extensions that are in the System.Linq library.   Well, before we discuss any further, let's look at some sample code and the numbers.  First, let's take a look at the for loop and the LINQ expression.  This is just a simple find comparison:       // find implemented via LINQ     public static bool FindViaLinq(IEnumerable<int> list, int target)     {         return list.Any(item => item == target);     }         // find implemented via standard iteration     public static bool FindViaIteration(IEnumerable<int> list, int target)     {         foreach (var i in list)         {             if (i == target)             {                 return true;             }         }           return false;     }   Okay, looking at this from a maintainability point of view, the Linq expression is definitely more concise (8 lines down to 1) and is very readable in intention.  You don't have to actually analyze the behavior of the loop to determine what it's doing.   So let's take a look at performance metrics from 100,000 iterations of these methods on a List<int> of varying sizes filled with random data.  For this test, we fill a target array with 100,000 random integers and then run the exact same pseudo-random targets through both searches.                       List<T> On 100,000 Iterations     Method      Size     Total (ms)  Per Iteration (ms)  % Slower     Any         10       26          0.00046             30.00%     Iteration   10       20          0.00023             -     Any         100      116         0.00201             18.37%     Iteration   100      98          0.00118             -     Any         1000     1058        0.01853             16.78%     Iteration   1000     906         0.01155             -     Any         10,000   10,383      0.18189             17.41%     Iteration   10,000   8843        0.11362             -     Any         100,000  104,004     1.8297              18.27%     Iteration   100,000  87,941      1.13163             -   The LINQ expression is running about 17% slower for average size collections and worse for smaller collections.  Presumably, this is due to the overhead of the state machine used to track the iterators for the yield returns in the LINQ expressions, which seems about right in a tight loop such as this.   So what about other LINQ expressions?  After all, Any() is one of the more trivial ones.  I decided to try the TakeWhile() algorithm using a Count() to get the position stopped like the sample Pete was using in his blog that Resharper refactored for him into LINQ:       // Linq form     public static int GetTargetPosition1(IEnumerable<int> list, int target)     {         return list.TakeWhile(item => item != target).Count();     }       // traditionally iterative form     public static int GetTargetPosition2(IEnumerable<int> list, int target)     {         int count = 0;           foreach (var i in list)         {             if(i == target)             {                 break;             }               ++count;         }           return count;     }   Once again, the LINQ expression is much shorter, easier to read, and should be easier to maintain over time, reducing the cost of technical debt.  So I ran these through the same test data:                       List<T> On 100,000 Iterations     Method      Size     Total (ms)  Per Iteration (ms)  % Slower     TakeWhile   10       41          0.00041             128%     Iteration   10       18          0.00018             -     TakeWhile   100      171         0.00171             88%     Iteration   100      91          0.00091             -     TakeWhile   1000     1604        0.01604             94%     Iteration   1000     825         0.00825             -     TakeWhile   10,000   15765       0.15765             92%     Iteration   10,000   8204        0.08204             -     TakeWhile   100,000  156950      1.5695              92%     Iteration   100,000  81635       0.81635             -     Wow!  I expected some overhead due to the state machines iterators produce, but 90% slower?  That seems a little heavy to me.  So then I thought, well, what if TakeWhile() is not the right tool for the job?  The problem is TakeWhile returns each item for processing using yield return, whereas our for-loop really doesn't care about the item beyond using it as a stop condition to evaluate. So what if that back and forth with the iterator state machine is the problem?  Well, we can quickly create an (albeit ugly) lambda that uses the Any() along with a count in a closure (if a LINQ guru knows a better way PLEASE let me know!), after all , this is more consistent with what we're trying to do, we're trying to find the first occurence of an item and halt once we find it, we just happen to be counting on the way.  This mostly matches Any().       // a new method that uses linq but evaluates the count in a closure.     public static int TakeWhileViaLinq2(IEnumerable<int> list, int target)     {         int count = 0;         list.Any(item =>             {                 if(item == target)                 {                     return true;                 }                   ++count;                 return false;             });         return count;     }     Now how does this one compare?                         List<T> On 100,000 Iterations     Method         Size     Total (ms)  Per Iteration (ms)  % Slower     TakeWhile      10       41          0.00041             128%     Any w/Closure  10       23          0.00023             28%     Iteration      10       18          0.00018             -     TakeWhile      100      171         0.00171             88%     Any w/Closure  100      116         0.00116             27%     Iteration      100      91          0.00091             -     TakeWhile      1000     1604        0.01604             94%     Any w/Closure  1000     1101        0.01101             33%     Iteration      1000     825         0.00825             -     TakeWhile      10,000   15765       0.15765             92%     Any w/Closure  10,000   10802       0.10802             32%     Iteration      10,000   8204        0.08204             -     TakeWhile      100,000  156950      1.5695              92%     Any w/Closure  100,000  108378      1.08378             33%     Iteration      100,000  81635       0.81635             -     Much better!  It seems that the overhead of TakeAny() returning each item and updating the state in the state machine is drastically reduced by using Any() since Any() iterates forward until it finds the value we're looking for -- for the task we're attempting to do.   So the lesson there is, make sure when you use a LINQ expression you're choosing the best expression for the job, because if you're doing more work than you really need, you'll have a slower algorithm.  But this is true of any choice of algorithm or collection in general.     Even with the Any() with the count in the closure it is still about 30% slower, but let's consider that angle carefully.  For a list of 100,000 items, it was the difference between 1.01 ms and 0.82 ms roughly in a List<T>.  That's really not that bad at all in the grand scheme of things.  Even running at 90% slower with TakeWhile(), for the vast majority of my projects, an extra millisecond to save potential errors in the long term and improve maintainability is a small price to pay.  And if your typical list is 1000 items or less we're talking only microseconds worth of difference.   It's like they say: 90% of your performance bottlenecks are in 2% of your code, so over-optimizing almost never pays off.  So personally, I'll take the LINQ expression wherever I can because they will be easier to read and maintain (thus reducing technical debt) and I can rely on Microsoft's development to have coded and unit tested those algorithm fully for me instead of relying on a developer to code the loop logic correctly.   If something's 90% slower, yes, it's worth keeping in mind, but it's really not until you start get magnitudes-of-order slower (10x, 100x, 1000x) that alarm bells should really go off.  And if I ever do need that last millisecond of performance?  Well then I'll optimize JUST THAT problem spot.  To me it's worth it for the readability, speed-to-market, and maintainability.

  Read the article

• 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.                            

  Read the article

• Webcast: The ART of Migrating and Modernizing IBM Mainframe Applications

  - by todd.little

  Tuxedo provides an excellent platform to migrate mainframe applications to distributed systems. As the only distributed transaction processing monitor that offers quality of service comparable or better than mainframe systems, Tuxedo allows customers to migrate their existing mainframe based applications to a platform with a much lower total cost of ownership. Please join us on Thursday April 29 at 10:00am Pacific Time for this exciting webcast covering the new Oracle Tuxedo Application Runtime for CICS and Batch 11g. Find out how easy it is to migrate your CICS and mainframe batch applications to Tuxedo.

  Read the article

• How to repair an external harddrive?

  - by dodohjk

  I would like to reformat my hard disk, and if possible recover the (somewhat unimportant) contents if possible. I have a Western Digital 1TB hard drive which had a NTFS partition. I unplugged the drive without safely removing it first. At first a pop up was asking me to use a Windows OS to run the chkdsk /f command, however, in the effort to keep using a Linux OS I used the ntfsfix command on the ubuntu terminal Now, when I try to access the hard drive, it doesn't show up anymore in Nautilus. I tried reformatting it using Disk Utility, but it gives me an error message, and Gparted would hang on the "Scanning devices" step infinitely. Please comment any output that you would like to see and I will add it to my question. EDIT disk utility tells me is on /dev/sdb the command sudo fdisk -l gives dodohjk@DodosPC:~$ sudo fdisk -l [sudo] password for dodohjk: Disk /dev/sda: 250.1 GB, 250059350016 bytes 255 heads, 63 sectors/track, 30401 cylinders, total 488397168 sectors Units = sectors of 1 * 512 = 512 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0x0006fa8c Device Boot Start End Blocks Id System /dev/sda1 * 4094 482344959 241170433 5 Extended /dev/sda2 482344960 488396799 3025920 82 Linux swap / Solaris /dev/sda5 4096 31461127 15728516 83 Linux /dev/sda6 31463424 52434943 10485760 83 Linux /dev/sda7 52436992 62923320 5243164+ 83 Linux /dev/sda8 62924800 482344959 209710080 83 Linux Disk /dev/sdb: 1000.2 GB, 1000202043392 bytes 255 heads, 63 sectors/track, 121600 cylinders, total 1953519616 sectors Units = sectors of 1 * 512 = 512 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0x6e697373 This doesn't look like a partition table Probably you selected the wrong device. Device Boot Start End Blocks Id System /dev/sdb1 ? 1936269394 3772285809 918008208 4f QNX4.x 3rd part /dev/sdb2 ? 1917848077 2462285169 272218546+ 73 Unknown /dev/sdb3 ? 1818575915 2362751050 272087568 2b Unknown /dev/sdb4 ? 2844524554 2844579527 27487 61 SpeedStor Partition table entries are not in disk order I wrote something wrong here, however here the output of fsck /dev/sbd is dodohjk@DodosPC:~$ sudo fsck /dev/sdb fsck from util-linux 2.20.1 e2fsck 1.42.5 (29-Jul-2012) ext2fs_open2: Bad magic number in super-block fsck.ext2: Superblock invalid, trying backup blocks... fsck.ext2: Bad magic number in super-block while trying to open /dev/sdb The superblock could not be read or does not describe a correct ext2 filesystem. If the device is valid and it really contains an ext2 filesystem (and not swap or ufs or something else), then the superblock is corrupt, and you might try running e2fsck with an alternate superblock: e2fsck -b 8193 <device&gt;

  Read the article

• Improved Performance on PeopleSoft Combined Benchmark using SPARC T4-4

  - by Brian

  Oracle's SPARC T4-4 server running Oracle's PeopleSoft HCM 9.1 combined online and batch benchmark achieved a world record 18,000 concurrent users experiencing subsecond response time while executing a PeopleSoft Payroll batch job of 500,000 employees in 32.4 minutes. This result was obtained with a SPARC T4-4 server running Oracle Database 11g Release 2, a SPARC T4-4 server running PeopleSoft HCM 9.1 application server and a SPARC T4-2 server running Oracle WebLogic Server in the web tier. The SPARC T4-4 server running the application tier used Oracle Solaris Zones which provide a flexible, scalable and manageable virtualization environment. The average CPU utilization on the SPARC T4-2 server in the web tier was 17%, on the SPARC T4-4 server in the application tier it was 59%, and on the SPARC T4-4 server in the database tier was 47% (online and batch) leaving significant headroom for additional processing across the three tiers. The SPARC T4-4 server used for the database tier hosted Oracle Database 11g Release 2 using Oracle Automatic Storage Management (ASM) for database files management with I/O performance equivalent to raw devices. Performance Landscape Results are presented for the PeopleSoft HRMS Self-Service and Payroll combined benchmark. The new result with 128 streams shows significant improvement in the payroll batch processing time with little impact on the self-service component response time. PeopleSoft HRMS Self-Service and Payroll Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-4 (db) 18,000 0.988 0.539 32.4 128 SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-4 (db) 18,000 0.944 0.503 43.3 64 The following results are for the PeopleSoft HRMS Self-Service benchmark that was previous run. The results are not directly comparable with the combined results because they do not include the payroll component. PeopleSoft HRMS Self-Service 9.1 Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) 2x SPARC T4-2 (db) 18,000 1.048 0.742 N/A N/A The following results are for the PeopleSoft Payroll benchmark that was previous run. The results are not directly comparable with the combined results because they do not include the self-service component. PeopleSoft Payroll (N.A.) 9.1 - 500K Employees (7 Million SQL PayCalc, Unicode) Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-4 (db) N/A N/A N/A 30.84 96 Configuration Summary Application Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 512 GB memory Oracle Solaris 11 11/11 PeopleTools 8.52 PeopleSoft HCM 9.1 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Java Platform, Standard Edition Development Kit 6 Update 32 Database Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 256 GB memory Oracle Solaris 11 11/11 Oracle Database 11g Release 2 PeopleTools 8.52 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Micro Focus Server Express (COBOL v 5.1.00) Web Tier Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory Oracle Solaris 11 11/11 PeopleTools 8.52 Oracle WebLogic Server 10.3.4 Java Platform, Standard Edition Development Kit 6 Update 32 Storage Configuration: 1 x Sun Server X2-4 as a COMSTAR head for data 4 x Intel Xeon X7550, 2.0 GHz 128 GB memory 1 x Sun Storage F5100 Flash Array (80 flash modules) 1 x Sun Storage F5100 Flash Array (40 flash modules) 1 x Sun Fire X4275 as a COMSTAR head for redo logs 12 x 2 TB SAS disks with Niwot Raid controller Benchmark Description This benchmark combines PeopleSoft HCM 9.1 HR Self Service online and PeopleSoft Payroll batch workloads to run on a unified database deployed on Oracle Database 11g Release 2. The PeopleSoft HRSS benchmark kit is a Oracle standard benchmark kit run by all platform vendors to measure the performance. It's an OLTP benchmark where DB SQLs are moderately complex. The results are certified by Oracle and a white paper is published. PeopleSoft HR SS defines a business transaction as a series of HTML pages that guide a user through a particular scenario. Users are defined as corporate Employees, Managers and HR administrators. The benchmark consist of 14 scenarios which emulate users performing typical HCM transactions such as viewing paycheck, promoting and hiring employees, updating employee profile and other typical HCM application transactions. All these transactions are well-defined in the PeopleSoft HR Self-Service 9.1 benchmark kit. This benchmark metric is the weighted average response search/save time for all the transactions. The PeopleSoft 9.1 Payroll (North America) benchmark demonstrates system performance for a range of processing volumes in a specific configuration. This workload represents large batch runs typical of a ERP environment during a mass update. The benchmark measures five application business process run times for a database representing large organization. They are Paysheet Creation, Payroll Calculation, Payroll Confirmation, Print Advice forms, and Create Direct Deposit File. The benchmark metric is the cumulative elapsed time taken to complete the Paysheet Creation, Payroll Calculation and Payroll Confirmation business application processes. The benchmark metrics are taken for each respective benchmark while running simultaneously on the same database back-end. Specifically, the payroll batch processes are started when the online workload reaches steady state (the maximum number of online users) and overlap with online transactions for the duration of the steady state. Key Points and Best Practices Two PeopleSoft Domain sets with 200 application servers each on a SPARC T4-4 server were hosted in 2 separate Oracle Solaris Zones to demonstrate consolidation of multiple application servers, ease of administration and performance tuning. Each Oracle Solaris Zone was bound to a separate processor set, each containing 15 cores (total 120 threads). The default set (1 core from first and third processor socket, total 16 threads) was used for network and disk interrupt handling. This was done to improve performance by reducing memory access latency by using the physical memory closest to the processors and offload I/O interrupt handling to default set threads, freeing up cpu resources for Application Servers threads and balancing application workload across 240 threads. A total of 128 PeopleSoft streams server processes where used on the database node to complete payroll batch job of 500,000 employees in 32.4 minutes. See Also Oracle PeopleSoft Benchmark White Papers oracle.com SPARC T4-2 Server oracle.com OTN SPARC T4-4 Server oracle.com OTN PeopleSoft Enterprise Human Capital Managementoracle.com OTN PeopleSoft Enterprise Human Capital Management (Payroll) oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 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 8 November 2012.

  Read the article

• Some Benefits of Cash Registers

  A cash register is one of the devices that are used within a sales establishment and which are being operated by a sales clerk. This is usually used to calculate the total number of sale a customer w... [Author: Alan Wisdom - Computers and Internet - April 05, 2010]

  Read the article

< Previous Page | 76 77 78 79 80 81 82 83 84 85 86 87  | Next Page >