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Web Caching Simulator

Web Caching Simulator . Presented by Tashana Landray Molly Brown, Anna Karlin, Tashana Landray, Hank Levy, Felix Livni, Denise Pinnel, Nitin Sharma, Emin Gun Sirer, Geoff Voelker, Alec Wolman. Outline. Why we’re building a simulator What it simulates What results we want. Motivation.

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Web Caching Simulator

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  1. Web Caching Simulator Presented by Tashana Landray Molly Brown, Anna Karlin, Tashana Landray, Hank Levy, Felix Livni, Denise Pinnel, Nitin Sharma, Emin Gun Sirer, Geoff Voelker, Alec Wolman

  2. Outline • Why we’re building a simulator • What it simulates • What results we want

  3. Motivation • Several cooperative web caching algorithms have been proposed • Harvest/Squid • Tewari, Dahlin et. al • Zhang et. al • Us • Difficult to compare benefits and drawbacks

  4. Goal • Primary goal to evaluate and compare effectiveness of cooperative web caching algorithms • Allow comparison on a variety of metrics • hit rates, sharing, latency of requests, bandwidth consumed, etc. • Maintain high level of abstraction

  5. Features • Simulates a system of proxy caches • Implemented as modules that can be plugged in and interchanged • Caching algorithms • Traffic workloads • Network representation

  6. Traffic Workloads • Real Traces • Existing (e.g. Dec traces) • UW Trace (Alec’s talk) • Synthetic Workloads • Nitin’s talk

  7. Network Topology • Use Transit-Stub tool developed at Georgia Tech to create graphs representing arbitrary Internet-like topologies* • Precompute routing tables from graphs • We do not assign bandwidths or simulate network congestion * Ken Calvert, Matt Doar and Ellen W. Zegura. "Modeling Internet Topology." IEEE Communications Magazine, June 1997.

  8. What we simulate • We do (or will) simulate • Proxy load and server load at highly loaded servers • Bandwidth consumed over network links • We do NOT simulate • Queuing on network links • Underlying network protocols

  9. Algorithm Evaluation Results • Metrics to compare: • Hit rates (Sharing) • Average and worst-case latency of requests • Breakdown of what contributes to latency • Bandwidth consumed on links • Overhead of algorithms • Extra hops • Number of messages sent • Proxy and server load

  10. Performance Limitations • Scalability • Memory requirements biggest bottleneck • Can currently run one day of Dec trace through hierarchy of 68 proxies in about 120 MB • Representation of cached documents bottleneck • can currently store 2 million in 48 MB • can be optimized • May run with GMS

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