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Collaborative Web caching based on proxy affinities
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Source Joint International Conference on Measurement and Modeling of Computer Systems archive
Proceedings of the 2000 ACM SIGMETRICS international conference on Measurement and modeling of computer systems table of contents
Santa Clara, California, United States
Pages: 78 - 89  
Year of Publication: 2000
ISBN:1-58113-194-1
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Authors
Jiong Yang  T.J. Watson Research Center, IBM
Wei Wang  T.J. Watson Research Center, IBM
Richard Muntz  Computer Science Department, UCLA
Sponsor
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
Publisher
ACM  New York, NY, USA
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ABSTRACT

With the exponential growth of hosts and traffic workloads on the Internet, collaborative web caching has been recognized as an efficient solution to alleviate web page server bottlenecks and reduce traffic. However, cache discovery, i.e., locating where a page is cached, is a challenging problem, especially in the fast growing World Wide Web environment, where the number of participating proxies can be very large. In this paper, we propose a new scheme which employs proxy affinities to maintain a dynamic distributed collaborative caching infrastructure. Web pages are partitioned into clusters according to proxy reference patterns. All proxies which frequently access some page(s) in the same web page cluster form an “information group”. When web pages belonging to a web page cluster are deleted from or added into a proxy's cache, only proxies in the associated information group are notified. This scheme can be shown to greatly reduce the number of messages and other overhead on individual proxies while maintaining a high cache hit rate. Finally, we employ trace driven simulation to evaluate our web caching scheme using three web access trace logs to verify that our caching structure can provide significant benefits on real workloads.


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:
Jiong Yang: colleagues
Wei Wang: colleagues
Richard Muntz: colleagues