  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.

Networking and Internet Architecture;Distributed, Parallel, and Cluster Computing;Computer Science and Game Theory;Performance