document: Improving web performance by client characterization driven server adaptation 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 .
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