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Web Caching: Locality of References Revisited

Web Caching: Locality of References Revisited. Foong, A.P.; Yu-Hen Hu; Heisey, D.M. Department of Electrical and computer Engineering, University of Wisconsin Conference on IEEE International Networks, 2000. (ICON 2000). Proceedings., 2000 Page(s): 81 – 86 Kun-Ming Tien. Outline.

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Web Caching: Locality of References Revisited

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  1. Web Caching: Locality of References Revisited Foong, A.P.; Yu-Hen Hu; Heisey, D.M. Department of Electrical and computer Engineering, University of WisconsinConference on IEEE International Networks, 2000. (ICON 2000).Proceedings., 2000 Page(s): 81 –86 Kun-Ming Tien

  2. Outline • 1.Introduction • 2.Locality of reference in web accesses • 3.The Logistic Regression Model • 4.Implementation • 5.Applying to Web Caching • 6.Future Work & Conclusion

  3. 1.Introduction • The effort in this paper: --Determine what constitutes web locality --Find a good method for studying locality (Logistic Regression model) --Propose cache strategies based on the results • Effective web cache strategies are based on more than one feature

  4. 2.Locality of reference in web accesses

  5. 3.The Logistic Regression Model • It is widely used by the medical community

  6. The Logistic Regression Model(cont.) • Coefficients can be estimated by a suitable method( learning phase) • LR probability can be calculated for other objects( predication phase) • Y=1 if the web object re-accessed at least once , in the WF accesses

  7. 4.Implementation • Temporal Locality --X1=SINCE --X2=BHITS • Functional Locality --X3=SIZE --X4=TYPE • Topical/Contextual Locality --X5=NUM_LINKS --X6=NUM_IMAGES --X7=NUM_KEYWORD • Spatial Locality(dependency graph) --Primary & Secondary Objects

  8. Implementation(cont.)

  9. Implementation(cont.) • WF=100 & WB=100, NL=1000 & NP=10000 • Some Observation --multiple dimensions of locality exits --different sites exhibit different types of locality --primary objects show strong temporal & spatial locality --secondary objects have less temporal locality but strong topical locality

  10. 5.Applying to Web Caching • Lifespan • LR-LIFESPAN(cost=lifespan) • LR-LSIZE(cost=lifespan*size)

  11. Applying to Web Caching(cont.)

  12. Applying to Web Caching(cont.)

  13. 6.Future Work & Conclusion • Prefetch (Dependency graph) --We can predict accesses of secondary objects based on their features • Topical localityfull page parsing & content classification (XML,XHTML,optional tag) • Complex relationships among Cooperating caches

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