Joint Data Compression and Caching: Approaching Optimality with Guarantees

TL;DR

This paper addresses the complex problem of jointly optimizing data compression and caching in networks to minimize latency, proposing an approximation algorithm with strong theoretical guarantees and demonstrating near-optimal performance.

Contribution

It introduces a polynomial-time approximation algorithm for joint compression and caching optimization with provable guarantees, extending to general network topologies.

Findings

The problem is NP-hard due to caching decisions.

The compression subproblem is polynomial-time solvable.

The proposed algorithm achieves a (1-1/e)-approximation in strongly polynomial time.

Abstract

We consider the problem of optimally compressing and caching data across a communication network. Given the data generated at edge nodes and a routing path, our goal is to determine the optimal data compression ratios and caching decisions across the network in order to minimize average latency, which can be shown to be equivalent to maximizing the compression and caching gain under an energy consumption constraint. We show that this problem is NP-hard in general and the hardness is caused by the caching decision subproblem, while the compression sub-problem is polynomial-time solvable. We then propose an approximation algorithm that achieves a $(1-1/e)$-approximation solution to the optimum in strongly polynomial time. We show that our proposed algorithm achieve the near-optimal performance in synthetic-based evaluations. In this paper, we consider a tree-structured network as an illustrative example, but our results easily extend to general network topology at the expense of more complicated notations.