Open-Source Web Servers: Performance on a Carrier-Class Linux Platform
We collected graphs for systems with 1, 2, 4, 6, 8, 10 and 12 Linux processors. For each graph, we recorded the maximum number of requests per second that each configuration can service. When we divide this number by the number of Linux processors, we get the maximum number of requests that each processor can process per second in each configuration.
Figures 12 and 13 show the transaction capability per processor plotted against the cluster size for both versions of Apache. In both figures the line is not flat, which means that the scalability is not linear, i.e., not optimum.
If we collect the scalability data of Apache 1.3.14 and 2.08a (see Figure 14) and create the corresponding graph, Figure 15, we observe that both servers have similar scalability compared to each other.
On Linux systems both versions of the server have similar scalability. According to our results, Apache 2.08a is around 2% more scalable than the 1.3.14 version. In either case, we have a slow linear decrease. The more CPUs we add after we reach eight CPUs, the less performance we get per CPU.
As for the Java-based web server, although Tomcat showed a better performance (servicing more requests per second) than Jigsaw, it showed a slight scalability problem. Figure 16 shows a slight decrease in performance per processors as we add more processors.
Nonetheless, there are many possible explanations for the scalability degradation with the addition of more processors.
Several factors could have affected the results of the benchmarking tests:
We used NFS to store the workload tree of WebBench to make it available for all the CPUs. This could present a bottleneck at the NFS level when hundreds of clients per second are trying to access NFS-stored files.
Jigsaw and Tomcat are Java-based web servers, and thus their performance depends much on the performance of the Java Virtual Machine, which is also started from an NFS partition (since the CPUs are diskless and share I/O space through NFS).
To generate Web traffic, we were limited to only 16 Celeron rackmount units. The generated traffic may not have been enough to saturate the CPUs, especially in the case of Apache when we were testing more than six CPUs.
During our work on this activity, we faced many problems ranging from hardware problems and working on prototyped hardware to software problems, such as supported drivers and devices. In this section, we will focus only on the problems we faced while completing our benchmarks.
We suffered stability problems with the ZNYX Ethernet Linux drivers. The drivers were still under development; they were not production-level yet. After reaching a high number of transactions per second, the driver would simply crash. The following is a sample benchmark on one CPU running Apache 2.08a. Once the CPU reaches the level of servicing 1,053 requests per second (throughput of 6,044,916 bytes per second), the Ethernet driver would crash and we would lose connectivity to the ZNYX ports (see Figure 17).
We did much testing and debugging with the people from ZNYX and we were able to fix the driver problem and maintain a high level of throughput without any crash.
The second problem we faced when booting the cluster is related to inetd. The inetd dæmon acts as the operator for other system dæmons. It sits in the background and listens to network ports for incoming connections. When a connection is made, inetd spawns off a copy of the appropriate dæmon for that port. The problem we faced was that inetd was blocking for unknown reasons on UDP requests, and we needed to restart the dæmon every time it blocked. We are still having this problem even with the latest release of xinetd.
Another issue we faced was that we were not able to saturate the CPUs with enough traffic. That was obvious. We needed more power than what we were trying to benchmark. At the time we conducted this activity, we only had 17 machines deployed (one controller and 16 clients) for benchmarking purposes. It could be one reason why we were not able to scale up. However, we have increased the capacity of our benchmarking environment to 63 machines, and now we will be able to rerun some of the tests and verify our results.
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.
Join Linux Journal's Mike Diehl and Pat Cameron of Help Systems.
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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