1. 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

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 Synthetic Workloads
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 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

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