Improving web performance by client characterization driven server adaptation

• Authors: Balachander Krishnamurthy;Craig E. Wills
• Venue: Proceedings of the 11th international conference on World Wide Web
• Year: 2002

We categorize the set of clients communicating with a server on the Web based on information that can be determined by the server. The Web server uses the information to direct tailored actions. Users with poor connectivity may choose not to stay at a Web site if it takes a long time to receive a page, even if the Web server at the site is not the bottleneck. Retaining such clients may be of interest to a Web site. Better connected clients can receive enhanced representations of Web pages, such as with higher quality images.We explore a variety of considerations that could be used by a Web server in characterizing a client. Once a client is characterized as poor or rich, the server can deliver altered content, alter how content is delivered, alter policy and caching decisions, or decide when to redirect the client to a mirror site. We also use network-aware client clustering techniques to provide a coarser level of client categorization and use it to categorize subsequent clients from that cluster for which a client-specific categorization is not available.Our results for client characterization and applicable server actions are derived from real, recent, and diverse set of Web server logs. Our experiments demonstrate that a relatively simple characterization policy can classify poor clients such that these clients subsequently make the majority of badly performing requests to a Web server. This policy is also stable in terms of clients staying in the same class for a large portion of the analysis period. Client clustering can significantly help in initially classifying clients for which no previous information about the client is known. We also show that different server actions can be applied to a significant number of request sequences with poor performance.