The following best practices for virtualizing Java can provide useful guidance for virtual CPU, virtual memory, networking and storage setup.

Learn more: Virtualizing Business Critical Applications Whitepaper [39-page PDF]

Whether a custom Java application or third-party vendor application, all virtualize relatively easily. Often our customers notice improvements in performance and scalability when moving to a virtualized platform. Java application clusters are known to contain many instances that require increased managment when on a physical infrastructure. Many of our customers look for consolidation opportunities while improving performance and scalability. The prime reason for virtualizing Java applications these days is the ability to reduce the JVM instance sprawl that many administrators dread—and have to consolidate through virtualization in order to keep the scale manageable and feasible. Many of our customers have virtualized IBM WebSphere, Oracle WebLogic, JBoss, and Tomcat. In the last three years, virtualization of Java applications has become mainstream, as seen with many of our customer accounts.

The results of the tests discussed in this paper show that enterprise-level Java applications can provide excellent performance when deployed on VMware vSphere 4.1. The application used in these tests was Olio, a multi-tier enterprise application that implements a complete social networking Web site. Olio was deployed on SpringSource tc Server, running both natively and virtualized on vSphere 4.1. Figure 5 shows the peak throughput for a single instance of Olio running on tc Server, both natively and in a VM, with two and four CPUs.

Customer Case in Point
“With our OrderExpress project we upgraded our WebSphere Commerce, Portal, WCM, Service Layer, DB2 Database; migrated from AIX to Linux; virtualized on VMware; moved the application into a three-tier DMZ; increased our transactions by over 150 percent; and added significant new capabilities that greatly improved the customer experience. Changing such a wide range of technology components at once was a huge challenge. However using VMware vSphere and additional architectural changes we were successful in improving performance by over 300 percent; lowered costs in the millions; improved security, availability, and scalability; and how we plan to continue evolving this application to maintain greater than 30 percent yearly growth.” – Jeff Battisti, Senior Enterprise Architect at Cardinal Health

Learn more: Virtualizing Business Critical Applications Whitepaper [39-page PDF]

Several customers have asked what vFabric SQLFire can do for their applications and how they can modify the architecture of a custom Java application accordingly. These customers typically run custom Java applications against RDBMS that have reached the limits of scalability and response time with their current architecture. They want to make the change only if they are not too invasive.

To answer these questions fairly, we simulated a customer scenario with no specific assumptions about specialized tuning. First, we took Spring Travel with RDBMs as-is and ran a load test against it. Then we converted the Spring Travel schema to run against vFabric SQLFire, also without tuning, and plotted the results side by side. 

We also wanted to demonstrate how quickly we could make this change without assumptions about any code intrusion/change, so we simulated how a developer might download the Spring Travel application, run the DDlUtils conversion utility to generate the SQLFire schema and the data load file, then quickly test to see the performance improvements. We felt this would answer the customers’ questions without bias.

This post summarizes the results. The details of the conversion of the RDBMS schema and data can be found in the vFabric SQLFire POC Jumpstart Service Kit, or vFabric SQLFire Accelerator Service Kit. (Contact your local VMware account team for details on these service offerings.). The conversion process took one day, and the total process—downloading the Spring Travel application, installing vFabric SQLFire, running the schema and data conversion utility, and running the load test—took 3 days. We iterated the results for another week for verification.

NOTE: You can download Spring Travel from: http://www.springsource.org/download. Navigate to the download link under Spring Web Flow 2.3.0.  For vFabric SQLFire, see: http://www.vmware.com/products/application-platform/vfabric-sqlfire/overview.html

Figure 1. Spring Travel on Traditional Disk-Based RDBMS versus vFabric SQLFire 

Performance Results

The results are plotted in Table 1. The legend for the columns is as follows:

• Threads: The number of concurrent Spring Travel application threads executed during the two load tests.
• SQLF R/T (ms): The response time of Spring Travel application in milliseconds.
• SQLF CPU %: The percentage of CPU utilization at peak for the SQLFire VMs.
• RDBMS R/T (ms): The Spring Travel application response time in milliseconds when running against the traditional disk-based RDBMS.
• RDBMS CPU %: The percentage of CPU utilization at peak on the RDBMS VM.

The results tested for a range of 18 to 7200 concurrent threads. ”Failed” indicates that Spring Travel running against the traditional disk-based RDBMS failed to respond. Since it was essentially frozen, we collected no data from it.

 Table 1. Spring Travel Results with RDBMS versus SQLFire

Spring Travel Response Time versus Concurrent Threads Test Results

Figure 2 shows response time along the vertical axis and concurrent number of threads along the horizontal axis. The red line represents Spring Travel running against a traditional disk-based RDBMS, and the blue line represents Spring Travel running against SQLFire. The data shows that as the number of concurrent threads increases along the horizontal axis, the Spring Travel response time increases in a linear fashion when running against the disk-based RDBMS but remains constant, as indicated by a fairly low and flat blue line, when running against SQLFire.

Figure 2 – Spring Travel Response Time versus Concurrent Threads

Scalability Test Results

This test demonstrates the extent of scalability of both configurations. When Spring Travel ran against the RDBMS, after reaching 1850 concurrent threads and getting close to a response time of 172 milliseconds, the system stopped responding, indicating that it had reached the scalability limit. This is indicated by the red line on Figure 3. On the other hand, Spring Travel running against SQLFire continued to function to the limit of 7200 concurrent threads and a response time of 984 milliseconds. This is indicated by the blue line.

NOTE: At approximately 3600 concurrent threads, SQLFire started to overflow to disk, and the response time increased. In a normal situation, you can use appropriate sizing of available RAM to contain this kind of overflow. 

Figure 3. Spring Travel Response Time versus Concurrent Threads – Scalability Test

CPU versus Concurrent Threads Test Results

Figure 4 shows the CPU %, measured for the duration of the test, of the RDBMS VM in red and the SQLFire VMs in blue. Using the RDBMS, Spring Travel peaked at approximately 80% CPU and 1850 concurrent threads. At this point, it completely failed to respond. The SQLFire-based Spring Travel configuration, on the other hand, continued to 98% CPU utilization at 7200 concurrent threads and was still responsive – this is at approximately 984 milliseconds f Spring Travel application response time. The red and blue lines crossed over at approximately 1000 concurrent threads, indicating that Spring Travel with SQLFire handled much higher loads at a steadier CPU utilization increase.

Figure 4 – Spring Travel Application CPU versus Concurrent Threads

Summary of Findings

This simulation shows that:

• Using the DDlUtils utility to convert the schema and data of the RDBMS associated with Spring Travel application was relatively straightforward.
• The installation of vFabric SQLFire was also straightforward.
• Spring Travel pointing to vFabric SQLFire scaled approximately 4x when compared to Spring Travel pointing to an RDBMS.
• The response times of SQLFire were 5x to 30x faster with vFabric SQLFire. Further, the response times on SQLFire were more stable and constant with increased load.
• The configuration of Spring Travel with an RDBMS has a response time that increases linearly with increased load.
• The break point for Spring Travel against an RDBM was at 80% CPU utilization for about 1850 concurrent threads, after which Spring Travel stopped responding. The SQLFire version of Spring Travel continued to pace ahead at 98% CPU utilization and achieved 7200 concurrent threads.

• NOTE: The assumption here in the test across the two configurations was that the total compute resource was the same, meaning a true apples-to-apples comparison.  The RDBMS VM was eight vCPU and 4GB of RAM, while the SQLFire VMs were of 2GB and two vCPU each.  In configuration cases VM memory reservation was set.

 Thank you for reading! Looking forward to seeing you at VMworld 2012, I will blog about my VMworld sessions shortly.

Emad Benjamin

Looking at a Sizing Example

Figure 4, shows the most commonly encountered JVM and virtual machine size. This may be a fairly busy JVM with 100 to 250 concurrent threads (actual thread count varies as it depends on the nature of the workload), 4GB of heap, approximately 4.5GB for the JVM process, and 0.5GB for the guest OS. This results in a total recommended memory reservation for the virtual machine of 5GB with 2 vCPU and 1 JVM process.

Figure 4. Most Commonly Encountered Configuration

Figure 5 takes a closer look at the sizing example within the Java process, the memory layout, and for various sizes..

Figure 5. 5GB RAM Virtual Machine with One JVM Process and Two CPUs

The general sizing equation can be summarized as follows:

Let’s assume that, through load testing, a JVM max heap (-Xmx) of 4096m has been determined as necessary. Proceed to size as follows:

• Set -Xmx4096m and set –Xms4096m.
• Set –XX:MaxPermSize=256m. This value is a common number and depends on the memory footprint of the class-level information within your Java application code base..
• The other segment of NumberOfConcurrentThreads*(-Xss) depends mostly on the NumberOfConcurrentThreads the JVM will process, and the –Xss value you have chosen. A common range of –Xss is 128k-192k. If, for example, NumberOfConcurrentThreads is 100, then 100*192k => 19200k (assuming you set –Xss to 192k).

Note: The stack -Xss is application dependent, i.e. if the stack is not sized correctly you will get a StackOverflow. The default value is sometimes quite large, but you can size it down to help save on memory consumption.

• Assume the OS has a requirement of about 500m to run as per the OS spec.
• Total JVM memory (java process memory) = 4096m (-Xmx) + 256m (–XX:MaxPermSize) + 100*192k (NumberOfConcurrentThreads*-Xss) + “other mem”.
• Therefore, JVM memory is approximately 4096m+256m+19.2m + “other mem” = 4371m + “other mem”.
• Typically “other mem” is not significant. However, it can be quite large if the application uses lots of NIO buffers and socket buffers. Otherwise, assuming about 5 percent of the total JVM process memory (i.e., 4 to 5 percent of 4371=> assume 217m), should be enough, although proper load testing should be used to verify.
• This implies that JVM process memory is 4371m+217m=4588m
• To determine the virtual machine memory, assume you are using Linux with only this single Java process and no other significant process running. The total configured memory for the virtual machine translates to: VM memory = 4588m + 500m = 5088m.
• Finally, you should set the virtual machine memory as the memory reservation. You can choose to set the memory reservation as 5088m. However, over time you should monitor the active memory used by the virtual machine that houses this JVM process and adjust the memory reservation to that active memory value, which could be less than 5088m.

Stay tuned. In Part 3 we will conclude with an overview of the recently released book, Enterprise Java Applications Architecture on VMware.