A comparative evaluation of Web server systems: taxonomy and performance

Abstract

The Internet is an essential resource to an ever-increasing number of businesses and home users. Internet access is increasing dramatically and hence, the need for efficient and effective Web server systems is on the rise. These systems are information engines that are accessed through the Internet by a rapidly growing client base. These systems are expected to provide good performance and high availability to the end user. They are also resilient to failures at both the hardware and software levels. These characteristics make them suitable for servicing the present and future information demands of the end consumer.

In recent years, researchers have concentrated on taxonomies of scalable Web server system architectures, and routing and dispatching algorithms for request distribution. However, they have not focused on the classification of commercial products and prototypes, which would be of use to business professionals and software architects. Such a classification would help in selecting appropriate products from the market, based on product characteristics, and designing new products with different combinations of server architectures and dispatching algorithms.

Currently, dispatching algorithms are classified as content-blind, content-aware, and Domain Name Server (DNS) scheduling. These classifications are extended, and organized under one tree structure in this thesis. With the help of this extension, this thesis develops a unified product-based taxonomy that identifies product capabilities by relating them to a classification of scalable Web server systems and to the extended taxonomy of dispatching algorithms. As part of a detailed analysis of Web server systems, generic queuing models, which consist of a dispatcher unit and a Web server unit are built. Some performance metrics, such as throughput, server performance, mean queue size, mean waiting time, mean service time and mean response time of these generic queuing models are measured for evaluation. Finally, the correctness of generic queuing models are evaluated with the help of theoretical and simulation analysis.