Sizing and tuning guide for Rational DOORS Next Generation 5.0

Authors: BalakrishnaKolla
Build basis: Rational DOORS Next Generation (RDNG) 5.0

Introduction

In IBM® Rational® DOORS® Next Generation (RDNG), administrators can configure ther hardware and software to support user load on repositories of varied sizes. The Requirements Management (RM) team, who develop Rational DOORS Next Generation, know that a one size fits all configuration cannot satisfy the large variation in scalability requirements. The team tested the performance of the RM server on various repository sizes with various hardware and software configurations in order to create guidance for hardware and server tuning. You can use that guidance to determine which sizing and tuning settings most closely match your scalability requirements.

The guidance is based on the results of performance tests that the team ran on many hardware and software configurations. For details about the hardware to deploy and the server tuning settings for repositories, see the "RM server hardware configuration and tuning section."

Standard disclaimer

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 customer's ability to evaluate and integrate them into the customer's 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 Web sites are provided for convenience only and do not in any manner serve as an endorsement of these Web sites. 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.

Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multi-programming in the user's job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.

This testing was done as a way to compare and characterize the differences in performance between different versions of the product. The results shown here should thus be looked at as a comparison of the contrasting performance between different versions, and not as an absolute benchmark of performance.

Topology

The topology that was used in our testing is based on the standard (E1) Enterprise - Distributed / Linux / DB2 topology

Key:

• RM Server: Rational DOORS Next Generation Server
• JTS: Jazz™ Team Server

Tested hardware and software configurations

All of the hardware that was tested was 64 bit and supported hyperthreading. To determine the guidelines for repositories of varied sizes, the RM team used variations of physical memory, java heap and hard disk types. For details about hardware configurations and JVM settings for various repository sizes, see the "RM server hardware configuration and tuning" section. The following table shows the server hardware configurations that were used for all of the tests of all repository sizes.

Function	Number of machines	Machine type	Processor/machine	Total processor cores/threads	Memory/machine		Network interface	OS and version
Proxy Server (IBM HTTP Server and WebSphere Plugin)	1	IBM System x3550 M4	2 x Intel Xeon E5-2640 2.5 GHz (six-core) With Intel® Hyper-Threading Technology	12/24	32 GB	Gigabit Ethernet		Red Hat Enterprise Linux Server release 6.3 (Santiago)
Jazz Team Server WebSphere Application Server 8.5.5.1	1	IBM System x3550 M4	2 x Intel Xeon E5-2640 2.5 GHz (six-core) With Intel® Hyper-Threading Technology	12/24	32 GB	Gigabit Ethernet		Red Hat Enterprise Linux Server release 6.3 (Santiago)
RM server WebSphere Application Server 8.5.5.1	1	IBM System x3550 M4	2 x Intel Xeon E5-2640 2.5 GHz (six-core) With Intel® Hyper-Threading Technology	12/24	Varied, depending on the repository size and hard disk type. 32 GB to 128 GB	Gigabit Ethernet		Red Hat Enterprise Linux Server release 6.3 (Santiago)
Database server DB2 10.1.2	1	IBM System x3650 M4	2 x Intel Xeon E5-2640 2.5 GHz (six-core) With Intel® Hyper-Threading Technology	12/24	32 GB	Gigabit Ethernet		Red Hat Enterprise Linux Server release 6.3 (Santiago)
Network switches	N/A	Cisco 2960G-24TC-L	N/A	N/A		Gigabit Ethernet		24 Ethernet 10/100/1000 ports

Test data shape and volume

The test data that was used for these sizing guide tests represents extremely large repositories compared to most environments that are currently deployed. The RM team chose three repository sizes to test the limits of the RM application.
See Appendix C for a fourth data shape of 1M artifacts split into two equal projects.
You can choose the appropriate hardware and software configuration based on your scalability goals and requirements. The artifacts, modules, comments, links, and other elements were evenly distributed among the projects. Each project had data as shown in the following table.

Artifact type	Number
Large modules (10,000 artifacts)	2
Medium modules (1500 artifacts)	40
Small modules (500 artifacts)	10
Folders	119
Module artifacts	85000
Non-module artifacts	1181
Comments	260582
Links	304029
Collections	14
Public tags	300
Private tags	50
Views	200
Terms	238

Each repository that was tested contained a different number of projects. In each project, the data was distributed as shown in the previous table. The categories of RM artifacts in the repository were evenly distributed among all of the available projects in the repositories.

Artifact type	500,000-artifact repository	1-million-artifact repository	2-million-artifact repository
Projects	6	12	24
Large modules (10,000 artifacts)	12	24	48
Medium modules (1500 artifacts)	240	480	960
Small modules (500 artifacts)	60	120	240
Folders	714	1428	2856
Module artifacts	510,000	1,020,000	2,040,000
Non-module artifacts	7086	14,172	28344
Comments	1,563,492	3,126,984	6,253,968
Links	1,824,174	3,648,348	7,296,696
Collections	84	168	336
Public tags	1800	3600	7200
Private tags	300	600	1200
Views	1200	2400	4800
Terms	1428	2856	5712
Index size on disk	39 GB	79 GB	157 GB

Workload characterization

IBM® Rational® Performance Tester was used to simulate the workload. A Rational Performance Tester script was created for each use case. The scripts are organized by pages; each page represents a user action. Users were distributed into many user groups and each user group repeatedly runs one script (use case). The RM team used the user load stages in Rational Performance Tester to capture the performance for various user loads. The user stages that were used to simulate the load were as follows:

• 100 Users
• 200 Users
• 300 Users
• 400 Users
• 425 Users
• 450 Users
• 475 Users
• 500 Users

In each stage, the users set up in 15 minutes. After they log in, they are allowed another 15 minutes to get settled. Then, tests are iterated for 60 minutes. During that time, tests were run as quickly as possible with a 1 minute “think time” between pages for each user.

This table shows the use cases and the number of users who were repeatedly running each script.

Use case	Description	Number of users
Copy/Paste/Move/Delete	Open a module that contains 1500 artifacts, select 25 artifacts, move them by using the copy and paste functions, and then delete the copied artifacts.	1%
Create an artifact	Create non-module artifacts.	3%
Create a collection	Create collections that contain 10 artifacts.	2%
Create a module artifact end-to-end scenario	Open a medium module that contains 1500 artifacts, create a module artifact, edit the new artifact, and delete the new artifact.	12%
Create a small module artifact end-to-end scenario	Open a small module that contains 500 artifacts, create a module artifact, edit that new artifact, and delete the new artifact.	3%
Create a comment in a module artifact	Open a medium module that contains 1500 artifacts, open an artifact that is in the module, expand the Comments section of the artifact, and create a comment.	17%
Create a module artifact link by dragging an artifact between modules	Open a medium module that contains 1500 artifacts in a new browser tab and change the column settings to show links of the LinkTo type. Return to the other browser tab and open another module that contains 1500 artifacts. Drag one requirement to the module in the new tab and drop it on one of the cells in the LinkTo column. When you are prompted, create a link.	6%
Display module history	Open a module that contains 1500 artifacts and show the module history and the module audit history.	1%
Hover over a module artifact and edit it	Open a module that contains 1500 artifacts and hover over an artifact. When the rich hover is displayed, edit the artifact text.	2%
Hover over and open a collection	Display all of the collections, hover over a collection, and then open it.	1%
Manage folders	Click “Show Artifacts” to display folder tree and then create a folder. Move the new folder into another folder and then delete the folder that you just created.	1%
Open the RM admin page	Open the rm/admin page.	1%
Open the project dashboard	Open a dashboard that displays the default dashboard.	4%
Search by ID and string	Open a project, select a folder, search for an artifact by its numeric ID, and click a search result to display an artifact. Search for artifacts by using a generic string search that produces about 50 results.	9%
Scroll 20 pages in a module	Open a module that contains 1500 artifacts and then scroll through 20 pages.	19%
Switch the module view	Open a module that contains 1500 artifacts and then change the view to add columns that display user-defined attributes.	14%
Upload a 4 MB file as a new artifact	Upload a file and create an artifact.	4%
Review a module	Open a module that contains 1500 artifacts and create a review of the module. This use case is unique and complex.	1 user, irrespective of how many users are in the stage
Import a ReqIF module that contains 1000 artifacts	Upload a ReqIF file and import it to create a module that contains 1000 artifacts.	1 user, irrespective of how many users are in the stage

Throughput of tests

As explained in the workload characterization, the test load was aggressive and it simulated the load of very active users. After the users were logged in, they continuously ran the scripts, waiting for only 1 minute before running each page. During the tests, transaction throughput statistics were gathered. Those statistics were the basis for the high-level overview of server activity that is shown in the next table.

Server activity	100 users in 1 hour	100 users in 8 hours	400 users in 8 hours
Number of artifacts created	412 + 1000 = 1412	3296 + 8000 = 11296	3296 + 8000 = 21184
Number of artifacts opened	505	4040	16160
Number of artifacts edited or deleted	500	4000	16000
Display a list of modules	600	4800	19200
Comments created	204	1632	1632
Links created	72	572	2288
Modules opened	740	5920	23680
Search by ID and open the artifact	131	1048	4192
Search by string	135	1080	4320
Switch module view to filter by attribute	274	2192	8768
Number of module pages scrolled	1032	8256	33024
Review module	1 per hour, irrespective of the number of users	8	8

Test goals

The RM team ran many tests with varied hardware configurations on different repository sizes. The goal was to identify the hardware configuration that yields the best performance and scalability for a given repository. For each test, only the configuration of the RM server was changed. And the same hardware and software tuning were used for Jazz Team Server and the database server, irrespective of the repository size. For the results and the corresponding guidance, see the “RM server hardware configuration and tuning” section.

Jazz Team Server hardware and JVM configuration (for repositories of all sizes)

As mentioned, this article provides guidance about the hardware and software configurations for the RM server. However, it is also important to know about the hardware and software configurations for Jazz Team Server. When Jazz Team Server was configured as described in this section, its resources were underutilized and the server was not on a critical path to slow down the server performance.

Processor for Jazz Team Server

The team used IBM System x3550 M4 with 2 x Intel Xeon E5-2640 2.5 GHz (six-core). The total number of processor cores/machines was 24. For information about the performance benchmark of this server, see Appendix A.

Physical memory for Jazz Team Server

For all of the large repositories that were tested, 32 GB of RAM was used.

JVM heap and GC tuning

In general, the team used 50% of the available RAM as the JVM heap size and did not exceed the maximum heap size of 24 GB. Because the Jazz Team Server had 32 GB of RAM, the team used 16 GB for the maximum heap size. The JVM arguments that the team used to set the JVM heap sizes and the GC policy are as follows:

 -Xgcpolicy:gencon –Xmx16g –Xms16g –Xmn4G -Xcompressedrefs -Xgc:preferredHeapBase=0x100000000 

Database server hardware

The database server that was used for the testing was IBM DB2 enterprise version 10.1.2. The same hardware and database settings were used for all of the performance tests, irrespective of the repository size. In the team’s observation, the database server is not on a critical path; however, in certain cases, having a higher end server will improve performance.

Processor

The team used IBM System x3550 M4 with 2 x Intel Xeon E5-2640 2.5 GHz (six-core). The total number of processor cores/machines was 24. In the tests, one server was used for both the Jazz Team Server and RM databases. For the benchmark test results of the server performance, see Appendix A.

Physical memory

For repositories of all sizes, the database server used 32 GB of RAM.

Performance of RM server using various database vendors

As mentioned, the team used the DB2 server with same hardware configuration for repositories of all sizes. In tests that internal teams completed, those teams observed that the RM sever yields the same performance, irrespective of the database vendor. Follow the database tuning guidance that the corresponding database vendor recommends, and make sure that you have enough hard disk space to support large-scale deployments.

Hard disk capacity and configuration

The database servers should use the RAID 10 configuration with the RAID controller configured with "write back cache." In general, the database server does not slow the performance of the RM server. However, because large amounts of data are saved in the database, use a high capacity and high performing hard disk (10K RPM or better). For the hard disk performance benchmarks for the hard disks that were used on the DB2 server, see Appendix B.

Repository	Combined hard disk usage of the Jazz Team Server and RM databases	Hard disk size
Up to 500,000 artifacts	~100 GB	300 GB
Up to1 million artifacts	~150 GB	400 GB
Up to 2 million artifacts	~300 GB	700 GB

Proxy server hardware and configuration (for repositories of all sizes)

The team used the same server hardware to test repositories of all sizes. In general, the proxy server does not slow the performance of the RM server.

Processor

The team used IBM System x3550 M4 with 2 x Intel Xeon E5-2640 2.5 GHz (six-core). The total number of processor cores/machines was 24 for Jazz Team Server. For the performance benchmarks of the server, see Appendix A.

Physical memory

For all of the large repositories that were tested, 32 GB of RAM was used.

IHS tuning

For the IHS systems that were used during the tests, in the server pool regulation section (worker.c) of httpd.conf file, these parameters were configured:

ThreadLimit	25
ServerLimit	100
StartServers	2
MaxClients	2500
SpareThreads	25
MaxSpareThreads	500
ThreadsPerChild	25
MaxRequestsPerChild	0

RM server hardware configuration and tuning

The goal of the RM team was to test the performance of the RM server with various hardware and software configurations and keep the configurations of Jazz Team Server and the DB2 server constant irrespective of the RM repository size. The information in this section is applicable to the RM server only, but the hardware specifications of Jazz Team Server and the DB2 server are mentioned for reference. Based on the test results, the team created two categories of guidance. The first category applies to general server tuning for repositories of all sizes. The only guidance that varies based on the repository size is the guidance about the RM server. Otherwise, the guidance applies to repositories of all sizes.

General configuration and tuning for all deployments

Follow this tuning guidance for all deployments, irrespective of the repository shape and size.

Available TCP/IP ports

The default number of TCP/IP ports that are available on AIX and Linux operating systems is too low and must be increased. Windows operating systems have a higher default limit, but that limit might still be too low for large concurrent user loads. Use the following instructions to increase the port range.

• On AIX and Linux systems, set the limit as follows: ulimit -n 65000
• On Windows 2003 Server, follow these steps:
  • Open the registry.
  • Navigate to HKEY_LOCAL_MACHINE/SYSTEM/CurrentControlSet/Services/Tcpip/Parameters and create a dWord named MaxUserPort. Set its value to 65000.
  • Restart the computer.
• On Windows 2008 Server, follow the instructions in this Microsoft support article to change the dynamic port range. For the start port setting, use 2000. Set the number of ports to 63535.

Thread pool size

The WebSphere Application Server thread pool size must be increased from the default of 50 to 0.75 times the expected user load. For example, if you have 400 concurrent users, the thread pool maximum is 300.

Database optimization

To ensure optimal database performance, ensure that the database is fully optimized. With both DB2 and Oracle, you can run statistics to allow the databases to analyze the shape of a schema and optimize the contents. Databases generally manage statistics automatically; for example, in a scheduled overnight operation. However, to ensure that the database is fully optimized, you can manually run the statistics as follows. DB2

DB2 REORGCHK UPDATE STATISTICS ON TABLE ALL 

To reduce the startup time of large deployments, adjust the buffer pool size on the DB2 database. Use 300000 pages (16 K page size). Oracle

EXEC DBMS_STATS.gather_schema_stats(' JAZZDBUSER' );

More suggestions

In a 3-server topology, where Jazz Team Server and the RM server are on separate hardware, adjust two settings in the fronting.properties file for loads higher than 200 concurrent users.

• Ensure that com.ibm.rdm.fronting.server.connection.route.max equals the number of users, which in this test was 500.
• Ensure that com.ibm.rdm.fronting.server.connection.max is twice the value of number of users, which in this test was 1000.

When you use a proxy or reverse proxy server, you might need to increase the maximum allowed connections on the proxy server, depending on the concurrent user load. For more information, see Tuning IBM HTTP Server to maximize the number of client connections to WebSphere Application Server .

Repository-specific configuration and tuning

Repositories with up to 200,000 artifacts: RM server hardware and JVM tuning

The team tested the RM server on fairly large deployments (500,000 to 2 million artifacts). To support the large repositories, higher end servers and a high availability of RAM on the servers are required. But for small-scale deployments (up to 200,000 RM artifacts), the hardware and JVM heap configurations that are described in this section are suitable.

Processor

As mentioned earlier, the team tested the large-scale deployments by using IBM System x3550 M4 with 2 x Intel Xeon E5-2640 2.5 GHz (six-core) with a total number of 24 processor cores/machines. For small-scale deployments of repositories with up to 200,000 RM artifacts, the same processor performance is not required. However, use a higher end server that closely matches the performance of these servers used for testing larger repositories. See Appendix A, for the performance benchmarks of the server used for testing the larger repositories.

Physical memory

For repositories that contain up to 200,000 RM artifacts, use 16 GB of RAM.

JVM heap and GC tuning

Set the maximum heap size to 8 GB. To set the GC policy and arguments and the max and init heap sizes, use the following JVM parameters (specified in generic JVM arguments).

-Xgcpolicy:gencon –Xmx8g –Xms8g –Xmn2700M -Xcompressedrefs -Xgc:preferredHeapBase=0x100000000

Repositories with up to 500,000 artifacts: RM server hardware and JVM tuning

Performance test results for the 500,000-artifact repository

As shown in the following chart, the 500,000-artifact repository was tested with three variations in the hardware. The goal was to find the amount of RAM that is required on the RM server to yield the maximum number of users supported. Tests were completed with same servers but with varied sizes of available RAM on the RM sever. In addition, the heap was adjusted to not exceed 50% of the RAM and not to exceed 24 GB of the maximum heap size.

The following table shows the various hardware configurations with RAM and heap sizes and the maximum number of concurrent users that are supported in each hardware configuration.

RM server hardware and JVM configuration	Graph	Jazz Team Server	DB2 server	Number of users supported
32 GB RAM with 16 GB JVM heap		32 GB RAM with 16 GB JVM heap	32 GB RAM	200
64 GB RAM with 24 GB JVM heap		32 GB RAM with 16 GB JVM heap	32 GB RAM	450
128 GB RAM with 24 GB JVM heap		32 GB RAM with 16 GB JVM heap	32 GB RAM	475

RM server hardware and JVM tuning guidance for the 500,000-artifact repository

The following guidance is based on the test results.

Processor

Use a server that yields performance that is as close as possible to the performance of the server that was used for testing. The team used IBM System x3550 M4 with 2 x Intel Xeon E5-2640 2.5 GHz (six-core). The total number of processor cores/machines was 24. For the performance benchmarks of the server, see Appendix A. You can use those performance benchmarks as reference and compare them with the performance of your server. Be sure to use a server that matches or exceeds the performance benchmarks in this article.

Physical memory and hard disk

Use a high performing hard disk drive (HDD). For the specifications and performance benchmarks of the hard disks that were used during testing, see Appendix B. In your deployment, try to match the performance of those hard disks.

• To support up to 200 users, have at least 32 GB of RAM.
• To support up to 450 users, have at least 64 GB of RAM.
• To support up to 475 users, have at least 128 GB of RAM.

JVM heap and GC tuning

For 32 GB of RAM, set the maximum heap size to 16 GB. For RAM configurations that are higher than 32 GB, set the maximum heap size to 24 GB. For testing purposes, the following JVM parameters (specified in generic JVM arguments) were used to set the GC policy and the max and init heap size arguments.

• Server with 32 GB RAM

-Xgcpolicy:gencon –Xmx16g –Xms16g –Xmn5700M -Xcompressedrefs -Xgc:preferredHeapBase=0x100000000

• Server with 64 GB or higher RAM

-Xgcpolicy:gencon –Xmx24g –Xms24g –Xmn8G -Xcompressedrefs -Xgc:preferredHeapBase=0x100000000

Repositories with up to 1 million artifacts: RM server hardware and JVM tuning

Performance test results for the 1-million-artifact repository

As shown in the following chart, the RM server was tested with 1 million artifacts and six different variations in the hardware and JVM configurations of the server. The goal was to benchmark the RM server performance on an HDD, a SAN, and an SSD with varied RAM and heap sizes.

The following table outlines the different hardware and JVM heap configurations and the maximum number of users that are supported in each configuration.

RM server hardware and JVM configuration	Graph	Jazz Team Server	DB2 server	Number of users supported
HDD – 32 GB RAM with 16 GB JVM heap		32 GB RAM with 16 GB JVM heap	32 GB RAM	50
SSD – 32 GB RAM with 16 GB JVM heap		32 GB RAM with 16 GB JVM heap	32 GB RAM	150
SAN – 64 GB RAM with 24 GB JVM heap		32 GB RAM with 16 GB JVM heap	32 GB RAM	200
HDD – 64 GB RAM with 24 GB JVM heap		32 GB RAM with 16 GB JVM heap	32 GB RAM	425
SSD – 64 GB RAM with 24 GB JVM heap		32 GB RAM with 16 GB JVM heap	32 GB RAM	425
HDD – 128 GB RAM with 24 GB JVM heap		32 GB RAM with 16 GB JVM heap	32 GB RAM	425

Observations on the test results

• An HDD with 32 GB RAM can hardly support 100 users. Although it can sustain the user load of up to 200 users, the performance is very poor and the users will experience long waits for page loads and timeouts for queries.
• Increasing RAM beyond 64 GB did not yield additional user scalability or improvement in performance.
• Using an SSD with 64 GB RAM did not yield additional user scalability compared to using an HDD with 64 GB of RAM.
• When testers used a SAN with 64 GB RAM, it did not match the response time or user scalability of 64 GB RAM with a regular HDD.

RM server hardware and JVM tuning guidance for the 1-million-artifact repository

The following guidance is based on the test results.

Processor

Use a server that yields performance that is as close as possible to the performance of the server that was used for testing. For testing, the RM team used IBM System x3550 M4 with 2 x Intel Xeon E5-2640 2.5 GHz (six-core)” with a total of 24 processor cores/machines. For the performance benchmarks of the server, see Appendix A. Be sure to use a server that matches or exceeds the performance benchmarks.

Physical memory and hard disk

Use a high-performing hard disk drive (HDD) with high available RAM. For the specifications and performance benchmarks of the hard disks that were used during testing, see Appendix B.

• Use an SSD with 32 GB of RAM to support up to 150 user load with reasonable response time.
• Use an HDD with 64 GB of RAM to support up to 425 user load with reasonable response time.

JVM heap and GC tuning

For 32 GB of RAM, set the maximum heap size to 16 GB. For RAM configurations that are higher than 32 GB, set the maximum heap size to and 24GB. For testing purposes, the following JVM parameters (specified in generic JVM arguments) were used to set the GC policy and the max and init heap size arguments.

• Server with 32 GB RAM

-Xgcpolicy:gencon –Xmx16g –Xms16g –Xmn5700M -Xcompressedrefs -Xgc:preferredHeapBase=0x100000000

• Server with 64 GB or higher RAM

-Xgcpolicy:gencon –Xmx24g –Xms24g –Xmn8G -Xcompressedrefs -Xgc:preferredHeapBase=0x100000000

Repositories with up to 2 million artifacts

Performance test results for the 2-million-artifact repository

As shown in the following chart, the RM server was tested with 2 million artifacts and three variations in the hardware and JVM configurations of the server. The goal was to benchmark the RM server performance on an HDD, SSD, and a SAN with varied RAM and heap sizes.

The following table outlines the different hardware and JVM heap configurations and the maximum number of users that are supported in each configuration.

RM server hardware and JVM configuration	Graph	Jazz Team Server	DB2 server	Number of users supported
SAN – 64 GB RAM with 24 GB JVM heap		32 GB RAM with 16 GB JVM heap	32 GB RAM	<100
HDD – 128 GB RAM with 24 GB JVM heap		32 GB RAM with 16 GB JVM heap	32 GB RAM	200
SSD – 64 GB RAM with 24 GB JVM heap		32 GB RAM with 16 GB JVM heap	32 GB RAM	300

Observations on the test results

• An SSD with 64 GB RAM yielded the maximum user scalability with reasonable performance.
• An HDD with 128 GB RAM also yielded good performance until the user load reached 200.

RM server hardware and JVM tuning guidance for the 2-million-artifact repository

The following guidance is based on the test results.

Processor

Use a server that yields performance that is as close as possible to the performance of the server that was used for testing. The RM team used IBM System x3550 M4 with 2 x Intel Xeon E5-2640 2.5 GHz (six-core) with 24 processor cores/machines. For the performance benchmarks of that server, see Appendix A. Use a server that matches or exceeds those performance benchmarks.

Physical memory and hard disk

Use a solid state hard disk drive (SSD). Use the high performing hard disk. For the specifications and performance benchmarks of the hard disks that were used during testing, see Appendix B.

• Use an HDD with 128 GB of RAM to support up to a 200 user load with reasonable response time.
• Use an SSD with 64 GB of RAM to support up to a 300 user load with reasonable response time.

JVM heap and GC tuning

Set the maximum heap size to 24 GB. For testing purposes, the following JVM parameters (specified in generic JVM arguments) were used to set the GC policy and the max and init heap size arguments.

-Xgcpolicy:gencon –Xmx24g –Xms24g –Xmn8G -Xcompressedrefs -Xgc:preferredHeapBase=0x100000000

Summary

The following hardware configuration for the RM server is based on the results of the performance tests on various hardware configurations and JVM tunings. This guidance is based on the assumption that the server hardware matches or exceeds the performance benchmarks that are specified in Appendix A and Appendix B.

Repository size	200 users	400 users
200,000 requirements	16 GB RAM, HDD 8 GB JVM “max heap size”	32 GB RAM, HDD  16 GB JVM “max heap size”
500,000 requirements	32 GB RAM, HDD  16 GB JVM “max heap size”	64 GB RAM, HDD  24 GB JVM “max heap size”
1 million requirements	64 GB RAM, SAN  24 GB JVM “max heap size”	64 GB RAM, HDD  24 GB JVM “max heap size”
2 million requirements	128 GB RAM, HDD 24 GB JVM “max heap size”	64 GB RAM, SSD  24 GB JVM “max heap size”

Appendix A: Server performance benchmarks

Various server performance benchmarks were captured by using Sysbench. The performance benchmarks were captured on the RM server.

Using the CPU workload: sysbench --test=cpu --cpu-max-prime=20000 --num-threads=2 run

sysbench 0.4.12:  multi-threaded system evaluation benchmark

Running the test with following options:
Number of threads: 2

Doing CPU performance benchmark

Threads started!
Done.

Maximum prime number checked in CPU test: 20000

Test execution summary:
    total time:                          14.1921s
    total number of events:              10000
    total time taken by event execution: 28.3806
    per-request statistics:
         min:                                  2.81ms
         avg:                                  2.84ms
         max:                                  6.16ms
         approx.  95 percentile:               2.89ms

Threads fairness:
    events (avg/stddev):           5000.0000/5.00
    execution time (avg/stddev):   14.1903/0.00

Using the threads workload: sysbench --test=threads --thread-locks=1 --max-time=20s run

sysbench 0.4.12:  multi-threaded system evaluation benchmark

Running the test with following options:
Number of threads: 1

Doing thread subsystem performance test
Thread yields per test: 1000 Locks used: 1
Threads started!
Done.

Test execution summary:
    total time:                          2.4548s
    total number of events:              10000
    total time taken by event execution: 2.4534
    per-request statistics:
         min:                                  0.24ms
         avg:                                  0.25ms
         max:                                  0.64ms
         approx.  95 percentile:               0.25ms

Threads fairness:
    events (avg/stddev):           10000.0000/0.00
    execution time (avg/stddev):   2.4534/0.00

Using the mutex workload: sysbench --test=mutex --num-threads=64 run

sysbench 0.4.12:  multi-threaded system evaluation benchmark

Running the test with following options:
Number of threads: 64

Doing mutex performance test
Threads started!
Done.

Test execution summary:
    total time:                          1.8363s
    total number of events:              64
    total time taken by event execution: 113.1430
    per-request statistics:
         min:                               1526.83ms
         avg:                               1767.86ms
         max:                               1834.26ms
         approx.  95 percentile:            1831.69ms

Threads fairness:
    events (avg/stddev):           1.0000/0.00
    execution time (avg/stddev):   1.7679/0.07 

Using the memory workload: sysbench --test=memory --num-threads=4 run

sysbench 0.4.12:  multi-threaded system evaluation benchmark

Running the test with following options:
Number of threads: 4

Doing memory operations speed test
Memory block size: 1K

Memory transfer size: 102400M

Memory operations type: write
Memory scope type: global
Threads started!
Done.

Operations performed: 104857600 (1513850.88 ops/sec)

102400.00 MB transferred (1478.37 MB/sec)


Test execution summary:
    total time:                          69.2655s
    total number of events:              104857600
    total time taken by event execution: 135.7675
    per-request statistics:
         min:                                  0.00ms
         avg:                                  0.00ms
         max:                                  0.72ms
         approx.  95 percentile:               0.00ms

Threads fairness:
    events (avg/stddev):           26214400.0000/2003181.97
    execution time (avg/stddev):   33.9419/0.65 

Appendix B: Hard disk performance benchmarks

HDD performance of RM server measured using the fileio workload

sysbench --test=fileio --file-total-size=150G --file-test-mode=rndrw --max-time=300 --max-requests=0 run
sysbench 0.4.12:  multi-threaded system evaluation benchmark

Running the test with following options:
Number of threads: 1

Extra file open flags: 0
128 files, 1.1719Gb each
150Gb total file size
Block size 16Kb
Number of random requests for random IO: 0
Read/Write ratio for combined random IO test: 1.50
Periodic FSYNC enabled, calling fsync() each 100 requests.
Calling fsync() at the end of test, Enabled.
Using synchronous I/O mode
Doing random r/w test
Threads started!
Time limit exceeded, exiting...
Done.

Operations performed:  64501 Read, 43000 Write, 137600 Other = 245101 Total
Read 1007.8Mb  Written 671.88Mb  Total transferred 1.6403Gb  (5.5981Mb/sec)
  358.28 Requests/sec executed

Test execution summary:
    total time:                          300.0506s
    total number of events:              107501
    total time taken by event execution: 292.2219
    per-request statistics:
         min:                                  0.00ms
         avg:                                  2.72ms
         max:                                298.61ms
         approx.  95 percentile:               6.50ms

Threads fairness:
    events (avg/stddev):           107501.0000/0.00
    execution time (avg/stddev):   292.2219/0.00

SSD performance of RM server measured using the fileio workload

sysbench --test=fileio --file-total-size=150G --file-te      st-mode=rndrw --max-time=300 --max-requests=0 run
sysbench 0.4.12:  multi-threaded system evaluation benchmark

Running the test with following options:
Number of threads: 1

Extra file open flags: 0
128 files, 1.1719Gb each
150Gb total file size
Block size 16Kb
Number of random requests for random IO: 0
Read/Write ratio for combined random IO test: 1.50
Periodic FSYNC enabled, calling fsync() each 100 requests.
Calling fsync() at the end of test, Enabled.
Using synchronous I/O mode
Doing random r/w test
Threads started!
Time limit exceeded, exiting...
Done.

Operations performed:  922042 Read, 614694 Write, 1966976 Other = 3503712 Total
Read 14.069Gb  Written 9.3795Gb  Total transferred 23.449Gb  (80.038Mb/sec)
 5122.45 Requests/sec executed

Test execution summary:
    total time:                          300.0002s
    total number of events:              1536736
    total time taken by event execution: 171.4439
    per-request statistics:
         min:                                  0.00ms
         avg:                                  0.11ms
         max:                                  3.46ms
         approx.  95 percentile:               0.25ms

Threads fairness:
    events (avg/stddev):           1536736.0000/0.00
    execution time (avg/stddev):   171.4439/0.00

Appendix C: Large project performance

Test data shape and volume

The large project tests were conducted using a 1 million artifact repository devided into two equal projects, with the data in the shape of the following table:

Artifact type	Number
Large modules (10,000 artifacts)	12
Medium modules (1500 artifacts)	240
Small modules (500 artifacts)	60
Folders	370
Module artifacts	510,000
Non-module artifacts	7,086
Comments	1,563,492
Links	1,824,174
Collections	84
Public tags	1800
Private tags	300
Views	1200
Terms	1,428

Performance test results for 1 million 'Large Projects'

As shown in the following chart, the RM server was tested with 1 million artifacts split between two identical 500,000 requirement projects. Three hardware configurations were used, with the goal to demonstrate the impact of having fewer and larger projects with the total artifact count being constant.
Test results from 1 million artifacts across 12 projects on SSD and 64GB of RAM are included in this chart for a visual comparison.

The following table outlines the different hardware and JVM heap configurations and the maximum number of users that are supported in each configuration.

RM server hardware and JVM configuration	Graph	Jazz Team Server	DB2 server	Number of users supported
HDD – 32 GB RAM with 16 GB JVM heap		32 GB RAM with 16 GB JVM heap	32 GB RAM	100
SSD – 32 GB RAM with 16 GB JVM heap		32 GB RAM with 16 GB JVM heap	32 GB RAM	300
SSD – 64 GB RAM with 24 GB JVM heap		64 GB RAM with 24 GB JVM heap	64 GB RAM	300
'Standard' 1M SSD – 64 GB RAM with 24 GB JVM heap		64 GB RAM with 24 GB JVM heap	64 GB RAM	425

Observations on the test results

• All hardware configurations yield acceptable performance at 100 users.
• SSD-based hardware configurations can handle between 200 and 300 concurrent users with acceptable performance.
• An HDD with 32 GB RAM can support 100 users, but degrades beyond this user load. Although up to 200 users can be sustained, the performance is very poor and the users will experience long waits for page loads and timeouts for queries.
• Using an SSD with 64 GB RAM resulted in additional user scalability compared to using an SSD with 32 GB of RAM.
• This repository shape cannot sustain beyond 300 users while maintaining acceptable performance with any hardware configurations tested.

Appendix D: IBM Flash storage

The performance tests were conducted using a 2 million artifact repository deployed on IBM Flash Storage Solution.

Performance test results for 2 million repository on Flash Storage Vs HDD

As shown in the following chart, the RDNG server was tested with 2 million artifacts using Flash Storage and HDD, with the goal to demonstrate the benefits of deploying the RDNG server on "IBM Flash Storage" when compared to regular HDD.

Observations on the test results

• When compared with HDD, Flash Storage Solution yielded up to 6 times better average response time, which results in a much better user experience as the wait time between the page loads will go down signifficantly with Flash Storage.
• Also the user scalability is atleast 3 times better with Flash Storage Solution when compared to the HDD. With HDD the server peaked its capacity at 100 user load and resulted in higher failure rates during and after 100 user load. Where as with the Flash Storage Solution the server was able to sustain the 300 user load with reasonable response time.
• Although there is a spike in average response time from 200 user load to 300 user load with flash storage, that ART is still within the limits and wouldn't cause signifficant delays in page loading.

Additional contributors: Devang Parikh, Knut Radloff, Erik Craig, Joe Stellrecht, Sentella Cystrunk, Gustaf Svensson, Laura W. Hinson

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Questions and comments:

• What other performance information would you like to see here?
• Do you have performance scenarios to share?
• Do you have scenarios that are not addressed in documentation?
• Where are you having problems in performance?