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Improving web performance by client characterization driven server adaptation
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Source International World Wide Web Conference archive
Proceedings of the 11th international conference on World Wide Web table of contents
Honolulu, Hawaii, USA
SESSION: Performance Workload Char. and Adaptation table of contents
Pages: 305 - 316  
Year of Publication: 2002
ISBN:1-58113-449-5
Authors
Balachander Krishnamurthy  AT&T Labs--Research, Florham Park, NJ
Craig E. Wills  WPI, Worcester, MA
Sponsors
ACM: Association for Computing Machinery
: WWW'02
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 3,   Downloads (12 Months): 32,   Citation Count: 7
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ABSTRACT

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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  7
 

Collaborative Colleagues:
Balachander Krishnamurthy: colleagues
Craig E. Wills: colleagues

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