Adaptive Caching Networks with Optimality Guarantees
Stratis Ioannidis, Edmund Yeh

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…
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Taxonomy
TopicsCaching and Content Delivery · Covalent Organic Framework Applications · Cooperative Communication and Network Coding
