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Performance impact of different vCPU sizes within a VMware hypervisor

Authors: JosephStellrecht, VaughnRokosz
Last updated: July 29, 2013
Build basis: Rational Team Concert 4.0.3, Rational Quality Manager 4.0.3, Rational Requirements Server 4.0.3


This report looks at the performance impact of changing the vCPU sizes of VMware Virtual Machines on Rational Team Concert.

Summary of results

The physical CPU cores can not be directly translated to virtual CPUs within VMware. In this study, we were using dual 6-core physical hyper-threaded CPUs that were not able to be translated to 12 or 24 vCPUs within the virtual environment. We found better performance using 16 vCPUs in our Virtual Machines.


The information in this document is distributed AS IS. The use of this information or the implementation of any of these techniques is a customer responsibility and depends on the customerís ability to evaluate and integrate them into the customerís operational environment. While each item may have been reviewed by IBM for accuracy in a specific situation, there is no guarantee that the same or similar results will be obtained elsewhere. Customers attempting to adapt these techniques to their own environments do so at their own risk. Any pointers in this publication to external websites are provided for convenience only and do not in any manner serve as an endorsement of these websites. Any performance data contained in this document was determined in a controlled environment, and therefore, the results that may be obtained in other operating environments may vary significantly. Users of this document should verify the applicable data for their specific environment.

Server topology

The topology used for this testing was based on the standard enterprise topology E1. The E1 topology uses the WebSphere Application Server and DB2, and installs all of the CLM applications onto separate servers. We extended this topolopy by adding a second RQM server and a second RTC server, creating a topology where up to 5 CLM servers are interacting with a single JTS server. We only used a single RRC server because multiple RRC servers are not supported by the 4.0.3 release. We also changed this topology by using Virtual Machines instead of physical servers for the CLM components. The DB2 and IHS servers were still kept as separate physical machines.

This deployment used floating licenses provided by the JTS server. A single IBM HTTP server was used as a reverse proxy.

Here are the specific versions of software we used:

Software Version
CLM applications 4.0.3 M2
IBM DB2 Enterprise Server Edition
IBM WebSphere Application Server

CLM 4.0.3 vCPU Topology

Hardware description

This table lists the servers that were used for our testing:

Role Manufacturer / Model CPU Number of CPU/Cores CPU Speed Memory OS
IBM HTTP Server IBM x3250 M4 Intel Xeon E3-1240 v2 1 / 4 3.4 GHz 16 GB RedHat Enterprise Linux Server 64-bit v6.3
VMware Hypervisor IBM System X iDataPlex dx360 M4 Intel Xeon E5-2640 2 / 12 2.5 GHz 262 GB ESXi 5.0.0
CLM - DB2 Server IBM x3650 M4 Intel Xeon E5-2640 2 / 12 2.5 GHz 64 GB RedHat Enterprise Linux Server 64-bit v6.3

Test methodology - maximum throughput measurement

example_of_throughput_chart.pngThe tests described in this report determine the maximum throughput (in requests per second) that can be handled by a given topology. The tests are carried out by applying a simulated workload against the target system. The simulation uses multiple threads of execution which are sending transactions to the server as fast as possible. The number of threads starts out small, and then is slowly increased while we monitor the total number of transactions per second processed by the system. When the transactions per second stops increasing (or drops), the system is at its limit.

The chart at the right is an example of the output from a typical test. Initially, the throughput increases as the number of threads increases. Midway through the test, the throughput flattens out and this is the range from which the maximum throughput is calculated. Past this point, the system is overloaded and the system cannot process the requests of additional threads.

We used this technique to measure the maximum throughput for each of the application nodes in the test system, both singly and in combination, while also monitoring CPU utilization on the JTS server. For example, we first ran a test that applied load to a single RTC server. Then, we ran another test that applied load to 2 RTC servers at the same time. We continued looking at server combinations up to the point where we were applying load against all servers at once (both RQM servers, both RTC servers, and the RM server). We were looking for signs that the JTS was becoming overloaded, such as high CPU utilization or a drop in maximum throughput at one or more nodes.

Please note that the throughput results do not have a precise relationship to the number of users that can be supported by a configuration. The throughput numbers represent an absolute maximum that the system can sustain when stressed by a simulation that uses a small number of parallel threads running as fast as possible. In production systems, the traffic generated by each real user is much lower than the traffic generated by a simulated thread, because real users pause between operations. In theory, you should be able to increase the number of real users until you reach the maximum throughput (e.g. if each real user generated 2 transactions per second on average, and your maximum transaction rate is 150 - then you have an upper limit of 75 users). In practice, however, the actual user load will be lower than the upper limit. Response times, for example, may degrade before the maximum throughput is reached, which then lowers the effective number of supported users. Understanding how user capacity is related to maximum throughput is outside the scope of this article.

Overview of simulated workloads

The operations used to test Rational Team Concert's maximum throughput is listed below:

  • Rational Team Concert: login, select a predefined query, edit a workitem, create a new workitem

Data Volume

The data repository has an initial population of 100K RTC workitems, 100K RQM data, and 200K RRC requirement data. There were 5 projects in the RTC server with 10K workitems and 20 plans per project.

Test Results - Details

Throughput Summary

The maximum throughputs (in transactions per second) are listed in the following table:

vCPU count Maximum Throughput
2 122
4 216
6 301
8 339
12 490
16 530
24 492
Physical Server (12 cores) 581

Rational Performance Tester Run Comparison

The chart below shows the throughputs (in transactions per second) for each level of CPU. Also included is the physical server run for comparison.


CPU Comparison

The chart below shows the CPU percent for each of the runs. As more vCPUs were added, the strain on the CPU became less. Also included is the physical server CPU (at 12 cores) for comparison.


Key Tuning Parameters

WebSphere Application Servers

WebContainer set to Min 300 Max 300
JVM max heap set to 4 GB
JVM arguments set to:

-Xgcpolicy:gencon -Xmx4g -Xms4g -Xmn1g -Xcompressedrefs
-Xgc:preferredHeapBase=0x100000000 -Xverbosegclog:gc.log 


In httpd.conf:

<IfModule worker.c>
      ThreadLimit           25
      ServerLimit           100
      StartServers          2
      MaxClients            2500
      MinSpareThreads       25
      MaxSpareThreads       500
      ThreadsPerChild       25
      MaxRequestsPerChild   0

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Deployment.PerformanceImpactOfvCPUSizes moved from Deployment.PerformanceImpactOfvCPULevels on 2013-08-02 - 17:57 by Main.gcovell -
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