Evaluating Content Management Technique for Web Proxy Cache M. Arlitt, L. Cherkasova, J. Dilley, R. Friedrich and T. Jin MinSu Shin

Contents Introduction Data collection and Reduction Key Workload Characteristics Experimental Design Simulation Results Virtual caches Conclusion and Critique

Introduction How effective are current proxy cache replacement policies for real workload? What new replacement policies? Performance metrics Hit rate : how many times hit? Byte hit rate : how many bytes hit?

Data Collection Access logs from ISP Jan 3 rd, 1997 ~ May 31 st,1997 Backbone ISP Proxy Server

Workload Characteristics Cacheable Object 92% of all requests were for cacheable objects

Workload Characteristics Object Set Size 16 million unique cacheable objects 389GB : too much? Object Sizes Most of the requested objects are small Largest : 148MB video Large number of small object vs. small number of large object

Workload Characteristics Recency of Reference 1/3 : within one hour 2/3 : within 24 hour Frequency of Reference 60% of the distinct objects were requested only single time Discriminate against one-timers Turnover Objects that were once popular are no longer requested

Cache Replacement policies Least-Recently-Used (LRU) Consider only single workload characteristic SIZE – replace the largest object Some of the small object that never be accessed again – how? LFU (least frequently used) LFU-Aging

Cache Replacement policies GreedyDual-Size (GD-Size) Replace the object with the lowest utility GD-Size(1) : maximize hit rate GD-Size(packet) : best byte-hit rate C : cost associated with bringing object i to cache S : object size L : running age factor, replace object f, L = K f

Suggested Policies GDSF (GreedyDual-Size with Frequency) GD-Size not consider frequency LFU-Dynamic Aging LFU with inflation factor L

Performance

Virtual Cache Logical partition into N virtual caches Virtual caches have different management policies LFU-DAGDSF-Hits New access evicted replacement High byte hit rate + High hit rate reinserted

Performance of Virtual cache

VC1 has more impact on total performance

Conclusion and Critique Good policy Investigate various policies Suggest some effective method Virtual server is key idea? Not Good Not counting operation complexity Not original idea about workload characteristic