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Analysis of a Least Recently Used Cache Management Policy for Web Browsers

Author

Listed:
  • Vijay S. Mookerjee

    (University of Texas at Dallas, JO4.624, School of Management, Richardson, Texas 75083-0688)

  • Yong Tan

    (University of Washington, School of Business, Box 353200, Seattle, Washington 98195-3200)

Abstract

Experience shows that document caching by a web browser is a cheap and effective way to improve the performance of the World Wide Web. This study analyzes a LRU (Least Recently Used) policy for cache management in a web browser. In this policy, the cache is filled with documents based upon a document's “age,” defined as the time elapsed since the document was last accessed. The user's preference for a document is modeled as a general function that declines with the document's age. Two popular measures---the expected delay per document access, and the hit-ratio---are used to evaluate the LRU policy. Unlike many previous studies that evaluate caching policies using simulation methods, this study derives analytical expressions to evaluate performance. The study also presents an approximate, easy-to-compute method to evaluate performance. Numerical tests show this approximation to be extremely accurate. A variety of other numerical results are presented that help describe the behavior ofthe LRU policy under different situations (e.g., when the documents need to be updated periodically). We also compare the LRU policy with other caching policies (both static and dynamic) for small problems. Our comparison suggests that finding a good caching policy that is conscious of document size and delay may be difficult.

Suggested Citation

  • Vijay S. Mookerjee & Yong Tan, 2002. "Analysis of a Least Recently Used Cache Management Policy for Web Browsers," Operations Research, INFORMS, vol. 50(2), pages 345-357, April.
  • Handle: RePEc:inm:oropre:v:50:y:2002:i:2:p:345-357
    DOI: 10.1287/opre.50.2.345.430
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    References listed on IDEAS

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    1. Ferdinand F. Leimkuhler, 1968. "A literature search and file organization model," American Documentation, Wiley Blackwell, vol. 19(2), pages 131-136, April.
    2. Moinzadeh, Kamran & Berk, Emre, 2000. "An archiving model for a hierarchical information storage environment," European Journal of Operational Research, Elsevier, vol. 123(1), pages 206-225, May.
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    Citations

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    Cited by:

    1. Yong Tan & Yonghua Ji & Vijay S. Mookerjee, 2006. "Analyzing Document-Duplication Effects on Policies for Browser and Proxy Caching," INFORMS Journal on Computing, INFORMS, vol. 18(4), pages 506-522, November.
    2. Mohit Tawarmalani & Karthik Kannan & Prabuddha De, 2009. "Allocating Objects in a Network of Caches: Centralized and Decentralized Analyses," Management Science, INFORMS, vol. 55(1), pages 132-147, January.
    3. Kartik Hosanagar & Ramayya Krishnan & John Chuang & Vidyanand Choudhary, 2005. "Pricing and Resource Allocation in Caching Services with Multiple Levels of Quality of Service," Management Science, INFORMS, vol. 51(12), pages 1844-1859, December.
    4. Kartik Hosanagar & Yong Tan, 2012. "Cooperative Cashing? An Economic Analysis of Document Duplication in Cooperative Web Caching," Information Systems Research, INFORMS, vol. 23(2), pages 356-375, June.
    5. Anindya Datta & Kaushik Dutta & Qianhui Liang & Debra VanderMeer, 2012. "SOA Performance Enhancement Through XML Fragment Caching," Information Systems Research, INFORMS, vol. 23(2), pages 505-535, June.
    6. Yonghua Ji & Subodha Kumar & Vijay Mookerjee, 2016. "When Being Hot Is Not Cool: Monitoring Hot Lists for Information Security," Information Systems Research, INFORMS, vol. 27(4), pages 897-918, December.
    7. Anindya Datta & Kaushik Dutta & Helen Thomas & Debra VanderMeer, 2003. "World Wide Wait: A Study of Internet Scalability and Cache-Based Approaches to Alleviate It," Management Science, INFORMS, vol. 49(10), pages 1425-1444, October.

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