The explosive growth in the use of the World Wide Web has resulted in increased load on its constituent networks and servers, and stresses the protocols that the Web is based on. Improving the performance of the web has been the subject of much recent research, addressing various aspects of the problem such as better Web caching, HTTP protocol enhancements, better HTTP servers and proxies and server OS implementations.
To date most work on measuring Web software performance has concentrated on accurately characterizing Web server workloads in terms of request file types, transfer sizes, locality of reference in URLs requested and other related statistics. Some researchers have tried to evaluate the performance of Web servers and proxies using real workloads directly. However, this approach suffers from the experimental difficulties involved in non-intrusive measurement of a live system and the inherent irreproducibility of live workloads.
Recently, there has been some effort towards Web server evaluation through generation of synthetic HTTP client traffic, based on invariants observed in real Web traffic. Unfortunately, there are pitfalls that arise in generating heavy and realistic Web traffic using a limited number of client machines. These problems can lead to significant deviation of benchmarking conditions from reality and fail to predict the performance of a givenWeb server.
In a Web server evaluation testbed consisting of a small number of clientmachines, it is difficult to simulate many independent clients. Typically, a load generating scheme is used that equates client load with the number of client processes in the test system. Adding client processes is thought to increase the total client request rate. Unfortunately, some peculiarities of the TCP protocol limit the traffic generating ability of such a naive scheme. Because of this, generating request rates that exceed the server's capacity is nontrivial, leaving the effect of request bursts on server performance unevaluated. In addition, a naive scheme generates client traffic that has little resemblance in its temporal characteristics to real-world Web traffic. Moreover, there are fundamental differences between the delay and loss characteristics of WANs and the LANs used in testbeds. Both of these factors may cause certain important aspects of Web server performance to remain unevaluated. Finally, care must be taken to ensure that limited resources in the simulated client systems do not distort the server performance results.