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Hot Systems, 18.12.2000

Hot Systems, 18.12.2000. Volkmar Uhlig volkmar@ira.uka.de. On the scale and performance of cooperative Web proxy caching. Alec Wolman, Geoffrey M. Voelker, Nitin Sharma, Neal Cardwell, Anna Karlin, and Henry M. Levy University of Washington (SOSP ‘99, Kiawah Island SC). Outline.

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Hot Systems, 18.12.2000

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  1. Hot Systems, 18.12.2000 Volkmar Uhlig volkmar@ira.uka.de

  2. On the scale and performance of cooperative Web proxy caching Alec Wolman, Geoffrey M. Voelker, Nitin Sharma, Neal Cardwell, Anna Karlin, and Henry M. Levy University of Washington (SOSP ‘99, Kiawah Island SC)

  3. Outline • Concepts of cooperative web caches • Cache simulation • Request analysis UW + Microsoft • Conclusion

  4. Web Proxy Caches http://l4ka.org/ Miss Hit Internet http://l4ka.org/

  5. Reasoning for Caches • Reduce download time • Improve responsiveness • Reduce internet bandwidth usage  Save money

  6. Idea:Cooperative Caches Overall Hit Rate?

  7. Hierarchical Caching

  8. Neighborhood Caches

  9. Hash based Caching

  10. Related Work – Proxies • V. Almeida, A. Bestavros, M. Crovella, and A. de-Oliveira. Characterizing reference locality in the WWW. Technical Report 96-011, Boston University, June 1996. • L. Breslau, P. Cao, L. Fan, G. Phillips, and S. Shenker. Web caching and Zipf-like distributions: Evidence and implications. In Proc. of IEEE INFOCOM ’99, pages 126–134, March 1999. • R. Caceres, F. Douglis, A. Feldmann, G. Glass, and M. Rabinovich. Web proxy caching: The devil is in the details. In Workshop on Internet Server Performance, pages 111–118, June 1998. • P. Cao. Characterization of Web proxy traffic and Wisconsin proxy benchmark 2.0. http://www.cs.wisc.edu/~cao/w3c-webchar-position, Nov. 1998. • M. E. Crovella and A. Bestavros. Self-similarity in World Wide Web traffic: Evidence and possible causes. In Proc. of the ACM SIGMETRICS ’96 Conf., pages 160–169, May 1996. • F. Douglis, A. Feldmann, B. Krishnamurthy, and J. Mogul. Rate of change and other metrics: a live study of the World Wide Web. In Proc. of the 1st USENIX Symp. on Internet Technologies and Systems, pages 147–158, Dec. 1997. • B. Duska, D. Marwood, and M. J. Feeley. The measured access characteristics of World Wide Web client proxy caches. In Proc. of the 1st USENIX Symp. on Internet Technologies and Systems, pages 23–36, Dec. 1997. • A. Feldmann, R. Caceres, F. Douglis, G. Glass, and M. Rabinovich. Performance of web proxy caching in heterogeneous bandwidth environments. In Proc. of IEEE INFOCOM ’99, March 1999. • S. D. Gribble and E. A. Brewer. System design issues for Internet middleware services: Deductions from a large client trace. In Proc. of the 1st USENIX Symp.on Internet Technologies and Systems, pages 207–218, Dec. 1997. • T. M. Kroeger, D. D. E. Long, and J. C. Mogul. Exploring the bounds of Web latency reduction from caching and prefetching. In Proc. of the 1st USENIX Symp. on Internet Technologies and Systems, pages 13–22, Dec.1997. • M. Rabinovich, J. Chase, and S. Gadde. Not all hits are created equal: Cooperative proxy caching over a wide area network. In Proc. of the 3rd Int. WWW Caching Workshop, June 1998.

  11. Related Work – Locality • V. Almeida, A. Bestavros, M. Crovella, and A. de-Oliveira. Characterizing reference locality in the WWW. Technical Report 96-011, Boston University, June 1996. • L. Breslau, P. Cao, L. Fan, G. Phillips, and S. Shenker. Web caching and Zipf-like distributions: Evidence and implications. In Proc. of IEEE INFOCOM ’99, pages 126–134, March 1999. • P. Cao and S. Irani. Cost-aware WWW proxy caching algorithms. In Proc. of the 1st USENIX Symp. on Internet Technologies and Systems, pages 193–206, Dec. 1997. • C. R. Cunha, A. Bestavros, and M. E. Crovella. Characteristics of WWW client-based traces. Technical Report BU-CS-95-010, Boston University, July 1995. • S. Glassman. A caching relay for the World Wide Web. In Proc. First Int. World Wide Web Conf., pages 60–76, May 1994. • T. M. Kroeger, J. C. Mogul, and C. Maltzahn. Digital’s Web proxy traces. ftp://ftp.digital.com/pub/DEC/traces/proxy/webtraces.html, August 1996.

  12. Scope of the paper • What is the best performance one could achieve with “perfect” caching? • For what range of client populations can cooperative caching work effectively? • Does the way in which clients are assigned to caches matter? • What cache hit rates are necessary to achieve worthwhile decreases in document access latency?

  13. Cache Simulations – How? • Collect traces (i.e. packet sniffer) • Model cache behavior • Play traces against cache model • Analyze

  14. Cache Traces 977131631.070 11 sec 1.2.3.52 TCP_MISS 1465 GET <URL> 977131631.070 11 1.2.3.52 TCP_MISS/200 1465 GET http://i30www.ira.uka.de/ - DIRECT/i30www.ira.uka.de text/html 977131631.369 13 1.2.3.52 TCP_MISS/200 3488 GET http://i30www.ira.uka.de/header.shtml - DIRECT/i30www.ira.uka.de text/html 977131631.379 30 1.2.3.52 TCP_MISS/200 11585 GET http://i30www.ira.uka.de/main.html - DIRECT/i30www.ira.uka.de text/html 977131631.663 67 1.2.3.52 TCP_REFRESH_HIT/200 1898 GET http://i30www.ira.uka.de/sysarch_header.css - DIRECT/i30www.ira.uka.de text/css 977131631.665 10 1.2.3.52 TCP_REFRESH_HIT/200 2119 GET http://i30www.ira.uka.de/sysarch3.css - DIRECT/i30www.ira.uka.de text/css 977131631.862 64 1.2.3.52 TCP_REFRESH_HIT/200 3215 GET http://i30www.ira.uka.de/images/bg_lgrey.jpg - DIRECT/i30www.ira.uka.de image/jpeg 977131631.867 31 1.2.3.52 TCP_REFRESH_HIT/200 11755 GET http://i30www.ira.uka.de/images/infblg.jpg - DIRECT/i30www.ira.uka.de image/jpeg 977131632.257 19 1.2.3.52 TCP_REFRESH_HIT/200 2569 GET http://i30www.ira.uka.de/images/sag.gif - DIRECT/i30www.ira.uka.de image/gif 977131632.393 45 1.2.3.52 TCP_REFRESH_HIT/200 3016 GET http://i30www.ira.uka.de/images/bg_white.jpg - DIRECT/i30www.ira.uka.de image/jpeg 977131637.860 542 1.2.3.52 TCP_CLIENT_REFRESH_MISS/200 445 GET http://www.aftenposten.no/grafikk/pixel-blank.gif - DIRECT/www.aftenposten.no image/gif 977131637.980 693 1.2.3.52 TCP_CLIENT_REFRESH_MISS/200 4271 GET http://www.aftenposten.no/grafikk/finn_samtlige.gif - DIRECT/www.aftenposten.no image/gif 977131638.146 309 1.2.3.52 TCP_CLIENT_REFRESH_MISS/200 2295 GET http://aftenposten.no/grafikk/aftenpostenhode1.gif - DIRECT/aftenposten.no image/gif 977133332.271 13 1.2.3.52 TCP_MEM_HIT/200 446 GET http://ad.no.doubleclick.net/ad/www.aftenposten.no/Innenriks;sz=468x60;ord= - NONE/- image/gif DIRECT/i30www.ira.uka.de text/html

  15. Simulation Methodology • Infinite sized caches • No expiration for objects • No compulsory misses (cold start) • Ideal vs. Practical Cache (cacheability)

  16. Simulation ofCooperative Caching • Optimistic simulation model: • Working set of all combined caches • No inter-proxy communication latency  One HUGE cache server

  17. Collect Traces Microsoft University of Washington Traces of same period of time

  18. University of Washington • 82.8 million HTTP requests • 18.4 million HTTP objects • 677 GB total requested bytes • 137 requests/second • 22,984 clients • 244,211 servers • 7 days

  19. Microsoft Cooperation • 107.7 million HTTP requests • 15.3 million HTTP objects • total requested bytes not available • 199 requests/second • 60,233 clients • 306,586 servers • 6 days 6 hours

  20. Experiment Analysis • Hit rate (object, byte) • Request latency • Bandwidth • Locality

  21. Request Hit-Rate / # Clients Caches with more than 2500 clients do not increase hit rates significantly!

  22. Byte Hit-Rate / # Clients (UW)

  23. Object Request Latency More clients do not reduce object latency significantly.

  24. Bandwidth / # Clients There is no relation between number of clients and bandwidth utilization!

  25. Locality:Proxies and Organizations • University of Washington • Museum of Art and Natural History • Music Department • Schools of Nursing and Dentistry • Scandinavian Languages • Computer Science comparable to cooperating businesses

  26. Local and Global Proxy Hit rates

  27. Randomly populated vs. UW organizations Locality is minimal(about 4%)

  28. Impact of larger populations

  29. Large-scale Experiment Microsoft University of Washington 60K Clients 23K Clients

  30. Cooperative CachingMicrosoft + UW

  31. Further Aspects • Analytic model of Web accesses • Popularity • Expiration of documents • Rate of change

  32. Summary and Conclusions Further research should focus on improving cacheability! • Cooperative caching with small population is effective (< 2500) • Can be handled by single server • Locality not significant • Limitations due to cacheability

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