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On the scale and performance of cooperative Web proxy caching
Alec Wolman, Geoffrey M. Voelker, Nitin Sharma, Neal Cardwell,
Anna Karlin, and Henry M. LevyUniversity of Washington
(SOSP ‘99, Kiawah Island SC)
Outline Concepts of cooperative web
caches Cache simulation Request analysis UW + Microsoft Conclusion
Web Proxy Caches
Internet
Internet
http://l4ka.org/
Miss
http://l4ka.org/
Hit
Reasoning for Caches Reduce download time Improve responsiveness Reduce internet bandwidth usage
Save money
Idea:Cooperative Caches
Overall Hit
Rate?
Hierarchical Caching
Neighborhood Caches
Hash based Caching
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.
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.
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?
Cache Simulations – How? Collect traces (i.e. packet sniffer) Model cache behavior Play traces against cache model Analyze
Cache Traces977131631.070 11 1.2.3.52 TCP_MISS/200 1465 GET http://i30www.ira.uka.de/ -
DIRECT/i30www.ira.uka.de text/html977131631.369 13 1.2.3.52 TCP_MISS/200 3488 GET http://i30www.ira.uka.de/header.shtml -
DIRECT/i30www.ira.uka.de text/html977131631.379 30 1.2.3.52 TCP_MISS/200 11585 GET http://i30www.ira.uka.de/main.html -
DIRECT/i30www.ira.uka.de text/html977131631.663 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/css977131631.665 10 1.2.3.52 TCP_REFRESH_HIT/200 2119 GET http://i30www.ira.uka.de/sysarch3.css -
DIRECT/i30www.ira.uka.de text/css977131631.862 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/jpeg977131631.867 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/jpeg977131632.257 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/gif977131632.393 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/jpeg977131637.860 542 1.2.3.52 TCP_CLIENT_REFRESH_MISS/200 445 GET
http://www.aftenposten.no/grafikk/pixel-blank.gif - DIRECT/www.aftenposten.no image/gif977131637.980 693 1.2.3.52 TCP_CLIENT_REFRESH_MISS/200 4271 GET
http://www.aftenposten.no/grafikk/finn_samtlige.gif - DIRECT/www.aftenposten.no image/gif977131638.146 309 1.2.3.52 TCP_CLIENT_REFRESH_MISS/200 2295 GET
http://aftenposten.no/grafikk/aftenpostenhode1.gif - DIRECT/aftenposten.no image/gif977133332.271 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
977131631.07011 sec1.2.3.52TCP_MISS1465 GET <URL>DIRECT/i30www.ira.uka.detext/html
Simulation Methodology Infinite sized caches No expiration for objects No compulsory misses (cold start) Ideal vs. Practical Cache
(cacheability)
Simulation ofCooperative Caching Optimistic simulation model:
Working set of all combined caches No inter-proxy communication latency
One HUGE cache server
Collect Traces
MicrosoftUniversity of Washington
Traces of same period of time
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
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
Experiment Analysis Hit rate (object, byte) Request latency Bandwidth Locality
Request Hit-Rate / # Clients
Caches with more than 2500 clients do not increase hit
rates significantly!
Byte Hit-Rate / # Clients (UW)
Object Request Latency
More clients do not reduce object
latency significantly.
Bandwidth / # Clients
There is no relation between number of clients
and bandwidth utilization!
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
Local and Global Proxy Hit rates
Randomly populated vs. UW organizations
Locality is minimal(about 4%)
Impact of larger populations
Large-scale Experiment
MicrosoftUniversity of Washington
23K Clients 60K Clients
Cooperative CachingMicrosoft + UW
Further Aspects Analytic model of Web accesses
Popularity Expiration of documents Rate of change
Summary and Conclusions Cooperative caching with small
population is effective (< 2500) Can be handled by single server Locality not significant Limitations due to cacheability
Further research should focus on
improving cacheability!
