Presented by Tashana Landray

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Presentation transcript:

Presented by Tashana Landray 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 Several cooperative web caching algorithms have been proposed Harvest/Squid Tewari, Dahlin et. al Zhang et. al Us Difficult to compare benefits and drawbacks

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

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

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

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.

What we simulate We do (or will) simulate We do NOT 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

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

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