Adaptive Caching Networks with Optimality Guarantees

TL;DR

This paper introduces an adaptive, distributed content placement algorithm for caching networks that guarantees near-optimal performance, outperforming traditional methods like path replication combined with standard eviction policies.

Contribution

It presents a novel stochastic gradient-based algorithm with provable approximation guarantees and a new eviction policy, improving content placement efficiency in caching networks.

Findings

The proposed algorithm achieves within 1-1/e of the optimal expected caching gain.

Numerical evaluations show significant performance improvements over traditional path replication.

The new eviction policy enhances caching efficiency across various network topologies.

Abstract

We study the problem of optimal content placement over a network of caches, a problem naturally arising in several networking applications, including ICNs, CDNs, and P2P systems. Given a demand of content request rates and paths followed, we wish to determine the content placement that maximizes the expected caching gain, i.e., the reduction of routing costs due to intermediate caching. The offline version of this problem is NP-hard and, in general, the demand and topology may be a priori unknown. Hence, a distributed, adaptive, constant approximation content placement algorithm is desired. We show that path replication, a simple algorithm frequently encountered in literature, can be arbitrarily suboptimal when combined with traditional eviction policies, like LRU, LFU, or FIFO. We propose a distributed, adaptive algorithm that performs stochastic gradient ascent on a concave relaxation of the expected caching gain, and constructs a probabilistic content placement within 1-1/e factor from the optimal, in expectation. Motivated by our analysis, we also propose a novel greedy eviction policy to be used with path replication, and show through numerical evaluations that both algorithms significantly outperform path replication with traditional eviction policies over a broad array of network topologies.