Open-Source Web Servers: Performance on a Carrier-Class Linux Platform
The performance of web servers and client-server systems depends on many factors: the client platform, client software, server platform, server software, network and network protocols. Most of the performance analysis of the Web has concentrated on two main issues: the overall network performance and the performance of web server software and platforms.
Our benchmarks consist of a mechanism to generate a controlled stream of web requests with standard metrics to report results. We used 16 Intel Celeron 500MHz 1U rackmount units (see Figure 2) that come with 512MB of RAM and run Windows NT. These machines generate traffic using WebBench, a Freeware tool available from www.zdnet.com.
The basic benchmark scenario is a set of client programs (loaf generators) that emit a stream of web requests and measure the system response. The stream of requests is called the workload. WebBench provides a way to measure the performance of web servers. It consists of one controller and many clients (see Figure 3). The controller provides means to set up, start, stop and monitor the WebBench tests. It is also responsible for gathering and analyzing the data reported from the clients.
On the other hand, the clients execute the WebBench tests and send requests to the server. WebBench uses the client PCs to simulate web browsers. However, unlike actual browsers, the clients do not display the files that the server sends in response to their requests. Instead, when a client receives a response from the server, it records the information associated with the response and then immediately sends another request to the server.
There are several measurements of web servers. For our testing, we will report the number of connections or requests served per second and throughput, the number of served bytes per second (see Figure 4).
WebBench uses a standard workload tree to benchmark the server. The workload tree comes as a compressed file that we need to move to the server and expand in the HTML document root on the web server (this is where the web server looks for its HTML files). This will create a directory called WBTREE that contains 61MB of web documents that will be requested by the WebBench clients. Since some of our CPUs are diskless, we installed the workload tree on the NFS server and modified the web server configuration to use the NFS directory as its document root.
As part of WebBench configuration, we specified that the traffic generated by the benchmarking machines would be distributed equally among the targeted CPUs. Figure 5 shows how we specify each server node and the percentage of the traffic it will receive.
After setting our Linux cluster and the benchmarking environment, we were ready to define our test cases. We tested all of the three web servers (Apache both 1.3.14 and 2.08a, Tomcat 3.1 and Jigsaw 2.0.1) running on 1, 2, 4, 6, 8, 10 and 12 CPUs. For every test case, we would specify in the RAM disk loaded by the CPUs which web server to start when the RAM disk is loaded. As a result, we ran four types of tests, each with a different server and on multiple CPUs.
For the purpose of this article, we will only show three comparison cases: Apache 1.3.14 vs. Apache 2.08a on one CPU, Apache 1.3.14 vs. Apache 2.08a on eight CPUs and Jigsaw 2.0.1 vs. Tomcat 3.1 on one CPU.
The first benchmark we did was to test all the web servers on one CPU. In WebBench configuration, we specified that all the traffic generated by all the clients be directed to one CPU. Figure 6 shows the results of the benchmark for up to 64 simultaneous clients. On average, Apache 1.3.14 was able to serve 828 requests per second vs. 846 requests per second serviced by Apache 2.08a. The latest showed a performance improvement of 2.1%.
Figure 7 plots the results of the benchmarks of Apache 1.3.14 and Apache 2.08a. As we can see, both servers have almost identical performance.
As for the Java-based web servers, Tomcat and Jigsaw, Figures 8 and 9 show the resulting benchmarking data. The maximum number of requests per second Jigsaw was able to achieve was 39 vs. 60 for Tomcat. We were surprised by Jigsaw's performance; however, we need to remember that Jigsaw was designed to experiment new technologies rather than as a high-performance web server for industrial deployment.
When we scale the test over eight CPUs, Apache 2.08a was more consistent in its performance, servicing more requests per second as we increased the number of concurrent clients without any fluctuations in the number of serviced requests (see Figure 10).
Figure 11 clearly shows how consistent Apache 2.08a is compared to Apache 1.3.14. On eight CPUs, Apache 2.08a was able to maintain an average of 4,434 requests per second vs. 4,152 for Apache 1.3.14, a 6.8% performance improvement.
Fast/Flexible Linux OS Recovery
On Demand Now
In this live one-hour webinar, learn how to enhance your existing backup strategies for complete disaster recovery preparedness using Storix System Backup Administrator (SBAdmin), a highly flexible full-system recovery solution for UNIX and Linux systems.
Join Linux Journal's Shawn Powers and David Huffman, President/CEO, Storix, Inc.
Free to Linux Journal readers.Register Now!
- Sony Settles in Linux Battle
- Download "Linux Management with Red Hat Satellite: Measuring Business Impact and ROI"
- Libarchive Security Flaw Discovered
- Profiles and RC Files
- Maru OS Brings Debian to Your Phone
- Understanding Ceph and Its Place in the Market
- Snappy Moves to New Platforms
- The Giant Zero, Part 0.x
- Git 2.9 Released
- Astronomy for KDE
With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide