Intelligent prefetch in WWW using client behavior characterization

Improving the latency perceived by a Web client has drawn a lot of attention right from the genesis of the World Wide Web. Hierarchical caching is found to improve the latency in addition to reducing the Web traffic in the Internet backbone. Recently, Uniform Resource Locators (URL) have been found to be dynamic, i.e. the contents of the URLs keep changing every few minutes or during every visit. This proliferation of dynamic URLs renders static caching inefficient. We present a prefetching technique by characterizing the Web client browsing behavior and show an improvement in the Web client cache hit ratio in the presence of dynamic URLs. The features we considered are the trend in visit counts and the order of visits of URLs by the Web client. The proposed technique shows an average 13% client cache hit ratio even when all the visited URLs are dynamic.

[1]  Jon Crowcroft,et al.  Prefetching in World Wide Web , 1996, Proceedings of GLOBECOM'96. 1996 IEEE Global Telecommunications Conference.

[2]  Darrell D. E. Long,et al.  Exploring the Bounds of Web Latency Reduction from Caching and Prefetching , 1997, USENIX Symposium on Internet Technologies and Systems.

[3]  Jeffrey C. Mogul,et al.  Using predictive prefetching to improve World Wide Web latency , 1996, CCRV.

[4]  Alan Jay Smith,et al.  Branch Prediction Strategies and Branch Target Buffer Design , 1995, Computer.

[5]  Azer Bestavros,et al.  Server-Initated Document Dissemination for the WWW , 1996, IEEE Data Eng. Bull..

[6]  Thomas Alexander,et al.  Design and Evaluation of a Distributed Cache Architecture with Prediction , 1994 .

[7]  Anja Feldmann,et al.  Potential benefits of delta encoding and data compression for HTTP , 1997, SIGCOMM '97.

[8]  Linwei,et al.  Web prefetching between low-bandwidth clients and proxies , 1999 .

[9]  Carlos R. Cunha,et al.  Determining WWW user's next access and its application to pre-fetching , 1997, Proceedings Second IEEE Symposium on Computer and Communications.

[10]  Venkata N. Padmanabhan.  Improving World Wide Web Latency , 1995 .

[11]  Alan Jay Smith,et al.  Cache Memories , 1982, CSUR.

[12]  Wei Lin,et al.  Web prefetching between low-bandwidth clients and proxies: potential and performance , 1999, SIGMETRICS '99.

[13]  P. Krishnan,et al.  Practical prefetching via data compression , 1993 .

[14]  Margo I. Seltzer,et al.  The case for geographical push-caching , 1995, Proceedings 5th Workshop on Hot Topics in Operating Systems (HotOS-V).

[15]  Peter B. Danzig,et al.  A Hierarchical Internet Object Cache , 1996, USENIX ATC.

[16]  Stanley B. Zdonik,et al.  Fido: A Cache That Learns to Fetch , 1991, VLDB.

[17]  Vaduvur Bharghavan,et al.  Alleviating the Latency and Bandwidth Problems in WWW Browsing , 1997, USENIX Symposium on Internet Technologies and Systems.

[18]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[19]  R. Wilder,et al.  Wide-area Internet traffic patterns and characteristics , 1997, IEEE Netw..

[20]  Joseph D. Touch,et al.  An experiment in latency reduction , 1994, Proceedings of INFOCOM '94 Conference on Computer Communications.

[21]  S. V. Raghavan,et al.  On the classification of interactive user behaviour indices , 1985, SIGMETRICS 1985.

[22]  Jacob R. Lorch,et al.  Making World Wide Web Caching Servers Cooperate , 1996, World Wide Web J..

[23]  Azer Bestavros,et al.  Using speculation to reduce server load and service time on the WWW , 1995, CIKM '95.

[24]  Evangelos P. Markatos,et al.  Main Memory Caching of Web Documents , 1996, Comput. Networks.