一個以內容為基礎的代理伺服器演算法

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白典正(Tien-Cheng Pai) 查詢紙本館藏

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資訊管理學系

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一個以內容為基礎的代理伺服器演算法
(An Effect and Efficient Content-Based Proxy Scheme)

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摘要(中)

根據Zona Research的研究報告指出，線上購物的使用者最不能忍受的事，就是花時間在等待網頁下載。研究中也同時指出，使用者若是等待超過八秒，就會因不耐久候而放棄消費；因此，如何利用代理伺服器減少延遲時間以爭取消費者的使用忠誠度，是電子商務中十分重要的研究課題。本研究嘗試深入探討代理伺服器之存取紀錄(Access log)內容，分析使用者瀏覽網頁的瀏覽序列(Clickstream)，以改進網頁快取演算法。首先，我們嘗試去除內容之雜訊，利用移動視窗熵值過濾法來區別使用者行為規則的可信度，對於可信度較高的，才去進行統計。而且，我們考慮每個網站受歡迎程度的不同，給予不同長度的熱門網頁紀錄。接著，我們依時間內容之不同，一天分上午和下午兩次去計算和更新熱門網頁紀錄，保持資訊的新鮮度。最後，我們提出on-demand prefetching的方法，當使用者有需求時，代理伺服器才去抓取網頁回來，但是給予熱門網頁較大的有效存活時間TTL值(Time-To-Live)，以免它們太快被置換出快取。實驗顯示所提出之演算法較原有的演算法可以提升更多的快取命中率，而且不會對網路造成額外的負擔，能有效地降低使用者的等待時間，提高整體服務品質。

摘要(英)

A report from Zona Research shows that customers hate waiting long time to load web pages in online shopping. They may lose patience and abandon shopping if the requested web page is not loaded within 8 seconds. In this paper, we try to improve the caching method by analyzing the content of the proxy’’s access log. Our contributions are listed as follows. First, we use the sliding window scheme and calculate the entropy of the window’’s access log to decide the habitual behavior (domain mode or exploratory mode) of each individual user. Then, improving the domain-top approach, we consider only the domain mode data in choosing the most popular domains. Next, a more precise statistic mode is applied to calculate the numbers of documents prefetched in different selected domains. Afterwards, analyzing the time variation of the content, we update the rank-list twice a day to further improve the hit rate. Finally, we propose an on-demand prefetching approach to avoid the un-necessary prefetching. It makes hot documents stay longer in the cache. Experiments show that the proposed approach can improve not only the hit rate but also the waiting time.

關鍵字(中)

★ 預先擷取
★ 快取命中率
★ 代理伺服器

關鍵字(英)

★ Prefetching
★ Hit Rate
★ Proxy Server

論文目次

第1章　緒論 1
1.1　研究背景 1
1.2　研究動機與目的 3
1.3　論文章節 5
第2章　文獻探討 6
2.1　代理伺服器的快取原理 6
2.2　代理伺服器的功能 8
2.3　代理伺服器的置換策略 10
2.4　代理伺服器Squid 10
2.5　預先擷取演算法之探討 11
第3章　Enhanced Domain-Top預先擷取機制 17
3.1　前處理階段 17
3.2　分析階段 20
3.3　預先擷取階段 24
3.4　系統模擬與效能評估 25
第4章　時間內容之on-Demand預先擷取機制 34
4.1　on-Demand預先擷取機制 34
4.2　系統模擬 35
第5章　結論與未來研究方向 41
5.1　結論 41
5.2　研究貢獻 41
5.3　未來研究方向 42
參考文獻 43

參考文獻

【1】許毅嘉，民90，關連法則應用於代理伺服器上之快取置換機制，國立中興大學資訊科學研究所碩士論文。
【2】徐碧玉，民90，利用熵作為網際網路文件搜尋之排名方法，國立中興大學資訊科學研究所碩士論文。
【3】黃毓莉，民90，行動商務中快取機制之研究，國立交通大學資訊管理研究所碩士論文。
【4】王敏傑，民91，一個針對快取以使用者行為為基礎之預先擷取機制，國立交通大學資訊管理研究所碩士論文。
【5】楊才旻，民91，瀏覽行為之HMM分析與參數估計，東吳大學資訊科學研究所碩士論文。
【6】中央大學電子計算機中心：　http://www.cc.ncu.edu.tw/
【7】 "The Need for Speed," Zona Research, Issue: 32, June 1999
【8】 Seung Won Shin, Byeong Hag Seong, and Daeyeon Park, "Improving World-Wide-Web Performance Using Domain-Top approach to Prefetching," the Fourth International Conference/Exhibition on High Performance Computing in the Asia-Pacific Region, Vol. 2, pp.738-746, 2000.
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【12】 L. Brunie, J. Pierson, and D. Coquil, "Semantic collaborative web caching," the Third International Conference on Web Information Systems Engineering(WISE), pp.30-39, 2002
【13】 The Squid Web Proxy Cache: http://www.squid-cache.org/
【14】 Harvest Project: http://webharvest.sourceforge.net/ng/
【15】 NLANR IRCache Project: http://www.ircache.net/
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【18】 Zheng Wang and Jon Crowcroft, "Prefetching in World-Wide Web," In Proceedings of IEEE Globecom, December 1996.
【19】 Carlos R. Cunha and Carlos F. B. Jaccoud, "Determining WWW user's next access and its application to prefetching," In Proceedings of Second IEEE Symposium on Computers and Communications, July 1997.
【20】 John H. Hine, Craig E. Wills, Anja Martel, and Joel Sommers, "Combining Client Knowledge and Resource Dependencies for Improved World Wide Web Performance," New Applications of INET'98, 1998.
【21】 C. Aggarwal, J. Wolf, and P. Yu, "Caching on the World Wide Web," IEEE Transactions on Knowledge and Data Engineering, Vol. 11, No.1, pp.94-107, January/February 1999.
【22】 Junho Shim, Peter Scheuermann, and Radek Vingralek, "Proxy cache design: Algorithms, implementation and performance," IEEE Transactions on Knowledge and Data Engineering, pp.549-562, July/August 1999.
【23】 J. Wang, "A survey of web caching schemes for the internet," ACM Computer Communication Review, vol. 29(5), pp. 36-46, October 1999.
【24】 Jean-Marc Menaud, Valerie Issarny, and Michel Banatre, "Improving effectiveness of Web caching," In Recent Advances in Distributed Systems, Vol. 1752 of LNCS. Springer Verlag, 2000.
【25】 J. Srivastava, R. Cooley, M. Deshpande, and P. N. Tan, "Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data," ACM SIGKDD Explorations, Vol. 1, Issue: 2, 2000
【26】 Qiang Yang, Haining Henry Zhang, and Ian Tianyi Li, "Mining Web logs for prediction models in WWW caching and prefetching," The Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2001.
【27】 Juhnyoung Lee, Mark Podlaseck, Edith Schonberg, and Robert Hoch, "Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising," Journal of Data Mining and Knowledge Discovery, Vol. 5, pp.59-84, 2001
【28】 Paolo Giudici and Robert Castelo, "Association Models for Web Mining," Journal of Data Mining and Knowledge Discovery, Vol. 5, pp.183-196, 2001
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【30】 Bamshad Mobasher, Honghua Dai, Tao Luo, and Miki Nakagawa, "Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization," Journal of Data Mining and Knowledge Discovery, Vol. 6, pp.61-82, 2002
【31】 Rituparna Sen and Mark H. Hansen, "Predicting a Web user's next request based on log data," The Journal of Computational and Graphical Statistics, 2002.

指導教授

張瑞益(Ray-I Chang)

審核日期

2003-7-8

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