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Studying the Impact of More Complete Server Information on Web Caching

Studying the Impact of More Complete Server Information on Web Caching. Craig E. Wills and Mikhail Mikhailov Worcester Polytechnic Institute {cew,mikhail}@cs.wpi.edu http://www.cs.wpi.edu/~{cew,mikhail}. Presented by Mikhail Mikhailov May 23, 2000. Outline of Talk. Observations

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Studying the Impact of More Complete Server Information on Web Caching

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  1. Studying the Impact of More Complete Server Information on Web Caching Craig E. Wills and Mikhail Mikhailov Worcester Polytechnic Institute {cew,mikhail}@cs.wpi.edu http://www.cs.wpi.edu/~{cew,mikhail} Presented by Mikhail Mikhailov May 23, 2000

  2. Outline of Talk • Observations • Proposed approach • Experiments • Methodology • Test sets • Results • Conclusions • Future work

  3. Observations • Heterogeneous dynamic content • Monolithic pages, loss of information • Changes are predictable, can be localized • Heuristic approaches to caching (many validations)

  4. Proposed Approach • Object classification by type and change characteristics • Preserve object identities • Object Composition (vs. monolithic approach) • Object Relationships • Piggybacking

  5. Exp1: Methodology (content reuse) • Popular sites (100hot.com) and popular URLs (NLANR proxy logs) • Unconditionally GET HTML and embedded images each day at the same time for 11 days • Catalogue resources, compute MD5 • Analyze changes with Chunking Tool

  6. Exp1: Test Sets (content reuse) • Cnt300 (7 NLANR logs) • Top50 (50 most popular sites, 100hot.com) • ECom (50 largest b-2-c shopping sites, 100hot.com) • Srcheng (11 top search engines) • EComQ (2 queries, top 10 EComm set) • SrchengQ (2 queries, Srcheng set)

  7. Exp1: Results (content reuse)

  8. Exp2: Methodology(eliminating validation requests) • NLANR proxy logs • For each 304 response look for a 200 response from the same server within a given window (10 sec on each side) • Focus on 304 responses for images

  9. Exp2: Results (eliminating validation requests)

  10. Exp3.1: Methodology / Results(object change characteristics) • Dynamic, Access Dependent objects (Top50, R,R,15min,R) • most of short-term changes occur immediately

  11. Exp3.2: Methodology / Results(object change characteristics) • Dependency-based objects (SrchengQ, EComQ, same query, retrieved daily) • some changes may be attributed to dynamic/access dependent objects; further study needed

  12. Exp3.3.1: Methodology / Results(object change characteristics) • Input Dependent objects (SrchengQ, EComQ, different queries, retrieved daily)

  13. Exp3.3.2: Methodology / Results(object change characteristics) • Input Dependent objects (objects with cookies from Cnt300, Top50, ECom, obtain 2 cookies for each object, R-cookie1,R-cookie2)

  14. Conclusions • Proposed techniques have potential to: • increase content reuse • reduce number of validation requests

  15. Future Work • Combine object types and change characteristics with object relationships • Extend web server and proxy caching software to support proposed techniques

  16. Object classification by change characteristics • Periodic (changes at regular intervals: hour, day, etc) • Dependency-based (depends on a file or DB changing) • Dynamic (different on every access, can’t be prefetched) • Access Dependent (different on every access, can be prefetched) • Input Dependent (query, cookies) • Relatively Dynamic (changes frequently) • Static (never changes) • Relatively Static (changes infrequently)

  17. Figure 1. Current News Composite Object

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