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Web Caching: Locality of References Revisited Foong, A.P.; Yu-Hen Hu; Heisey, D.M. Department of Electrical and computer Engineering, University of Wisconsin.

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Presentation on theme: "Web Caching: Locality of References Revisited Foong, A.P.; Yu-Hen Hu; Heisey, D.M. Department of Electrical and computer Engineering, University of Wisconsin."— Presentation transcript:

1 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, (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 W F 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.) Primary ObjectSecondary Object SINCE Time since last accessed Time since it was last accessed as a primary object BHIT W B accesses back hits The number of times it was accessed as a primary object SIZEIt is obtained via the header TYPE NUM_LINKS NUM_IMAGES NUM_KEYWORD Title of object Description of the hypertext link

9 Implementation(cont.) W F =100 & W B =100, N L =1000 & N P =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

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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