FairCache: Introducing Fairness to ICN Caching - Technical Report

TL;DR

FairCache introduces a fair, game-theoretic caching algorithm for ICN that balances efficiency and collaboration overhead, achieving significant performance improvements with provable fairness guarantees.

Contribution

It models in-network caching as a Nash bargaining game, deriving a distributed algorithm with theoretical analysis of collaboration overhead and fairness guarantees.

Findings

Achieves at least 16% performance gain over competitors.

Collaboration overhead grows exponentially with network size.

Algorithm guarantees convergence, Pareto efficiency, and proportional fairness.

Abstract

Information-centric networking extensively uses universal in-network caching. However, developing an efficient and fair collaborative caching algorithm for selfish caches is still an open question. In addition, the communication overhead induced by collaboration is especially poorly understood in a general network setting such as realistic ISP and Autonomous System networks. In this paper, we address these two problems by modeling the in-network caching problem as a Nash bargaining game. We show that the game is a convex optimization problem and further derive the corresponding distributed algorithm. We analytically investigate the collaboration overhead on general graph topologies, and theoretically show that collaboration has to be constrained within a small neighborhood due to its cost growing exponentially. Our proposed algorithm achieves at least 16% performance gain over its competitors on different network topologies in the evaluation, and guarantees provable convergence, Pareto efficiency and proportional fairness.