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.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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