Squirrel: A decentralized peer- to-peer web cache Paul Burstein 10/27/2003

Outline Overview Design Evaluation Discussion

Traditional Web Caching Goals Reduce browser latency Reduce aggregate bandwidth Reduce load on web servers Deployment Dedicated centralized machines Placed at local network boundaries

Squirrel Web Caching Decentralized caching Desktops cooperate in a peer-to-peer fashion Mutual sharing between hosts Hosts browse and cache

Pros Centralized Dedicated Hardware Cost Administration Handling load bursts Single point of failure Decentralized No additional hardware More users  more resources Automatic scaling Self organizing Easy deployment

Assumptions Cooperative hosts No security issues Link and node failures Nodes are in single geographic location Low internal network latencies

Outline Overview Design Evaluation Discussion

Design Goals Target environment: ,000 machines Goal: Achieve performance comparable to centralized cache

Design Overview Built on top of Pastry Objects have 128-bit objectIds SHA-1 hash of URL Mapped to home node with closest nodeId Requests: GET – new request cGET – conditional Two schemes Home-store Directory

Home-store Objects stored at client cache and home node External requests come through home node Cache replacement All objects are considered a. home node fresh b. home node stale

Directory Home node keeps a directory of pointers Randomly redirect to delegates a. no directory, add new delegate b. cGET not modified c. delegate fresh, get from delegate d. cGET and stale, update e. GET and stale, update

Outline Overview Design Evaluation Discussion

Evaluation Characteristics Compare two schemes and dedicated cache Performance Latency External bandwidth Hit ratio Overhead Load Storage Fault Tolerance

Trace Characteristics

Bandwidth and Hit ratio Bytes transferred to origin servers and back correlated with hit rate Centralized cache with infinite storage 100MB cache per node achieves optimal rates 10MB in-memory cache is reasonable Directory scheme Active nodes suffer from eviction Distributed LRU is worse than centralized Home-store More total storage required

Latency User-perceived time for a response With comparable hit ratios, only consider internal hops Many requests can be satisfied locally, with 0 hops Directory scheme latency is up to one hop greater Some requests can be satisfied by home node Squirrel Latency Based on Pastry hops on cache hit Overshadowed on cache miss

Load on Nodes(1/2) Bursty behavior observations Max objects served per second Up to 48 and 55 objects per second served for the two traces Directory scheme One delegate can get bombarded with requests from many home nodes Home-store scheme Replicate objects at request threshold

Load on Nodes(2/2) Sustained load measurements Max objects/minute Average load in any second or minute: 0.31 objects/minute Redmond trace, both models

Fault Tolerance Internet connection loss Internal partitioning Individual failure Desktop shutdown or reboot Graceful shutdown Pastry aided content transfer Directory scheme More vulnerable to failures

Results The home-store models seems to outperform the directory model Hit ratio Load balancing Internal network latency Compared to centralized cache?

Outline Overview Design Evaluation Discussion

Would this be deployed in a corporate network?