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.