An Overview of Analysis Methods and Evaluation Results for Caching Strategies

TL;DR

This paper surveys analytical methods and evaluation results for caching strategies, focusing on bounds, stochastic models, and performance tradeoffs in web caching systems.

Contribution

It provides a comprehensive overview of existing analytical approaches, including static and dynamic bounds, and compares strategies based on data properties and performance metrics.

Findings

Knapsack solutions offer static caching bounds.

Markov and TTL models capture request stream dynamics.

Performance varies with data properties and system tradeoffs.

Abstract

We survey analytical methods and evaluation results for the performance assessment of caching strategies. Knapsack solutions are derived, which provide static caching bounds for independent requests and general bounds for dynamic caching under arbitrary request pattern. We summarize Markov- and time-to-live-based solutions, which assume specific stochastic processes for capturing web request streams and timing. We compare the performance of caching strategies with different knowledge about the properties of data objects regarding a broad set of caching demands. The efficiency of web caching must regard benefits for network wide traffic load, energy consumption and quality-of-service aspects in a tradeoff with costs for updating and storage overheads.