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Design, implementation, and evaluation of a client characterization driven web server
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Source International World Wide Web Conference archive
Proceedings of the 12th international conference on World Wide Web table of contents
Budapest, Hungary
SESSION: Provisioning table of contents
Pages: 138 - 147  
Year of Publication: 2003
ISBN:1-58113-680-3
Authors
Balachander Krishnamurthy  AT&T Labs-Research, Florham Park, NJ
Yin Zhang  AT&T Labs-Research, Florham Park, NJ, USA
Craig E. Wills  Worcester Polytechnic Institute, Worcester, MA
Kashi Vishwanath  Duke University, Durham, NC
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

In earlier work we proposed a way for a Web server to detect connectivity information about clients accessing it in order to take tailored actions for a client request. This paper describes the design, implementation, and evaluation of such a working system. A Web site has a strong incentive to reduce the 'time-to-glass' to retain users who may otherwise lose interest and leave the site. We have performed a measurement study from multiple client sites around the world with various levels of connectivity to the Internet communicating with modified Apache Web servers under our control. The results show that clients can be classified in a correct and stable manner and that user-perceived latency can be reduced via tailored actions. Our measurements show that classification and determination of server actions are done without significant overhead on the Web server. We explore a variety of modified actions ranging from selecting a lower quality version of the resource to altering the manner of content delivery. By studying numerous performance related factors in a single unified framework and examining both individual actions as well as combination of actions, our modified Web server implementation shows the efficacy of various server actions.


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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Collaborative Colleagues:
Balachander Krishnamurthy: colleagues
Yin Zhang: colleagues
Craig E. Wills: colleagues
Kashi Vishwanath: colleagues