Cost-Driven Data Replication with Predictions

TL;DR

This paper introduces a cost-efficient online data replication algorithm that leverages simple predictions to adaptively manage data copies across servers, balancing storage and network costs in dynamic environments.

Contribution

It develops a learning-augmented online algorithm with proven competitiveness bounds and analyzes the impact of prediction errors on performance.

Findings

Algorithm achieves ($rac{5+ ext{α}}{3}$)-competitiveness with perfect predictions.

Algorithm maintains bounded robustness under prediction errors.

Experimental results validate the effectiveness of the proposed approach.

Abstract

This paper studies an online replication problem for distributed data access. The goal is to dynamically create and delete data copies in a multi-server system as time passes to minimize the total storage and network cost of serving access requests. We study the problem in the emergent learning-augmented setting, assuming simple binary predictions about inter-request times at individual servers. We develop an online algorithm and prove that it is ($\frac{5+\alpha}{3}$)-consistent (competitiveness under perfect predictions) and ($1 + \frac{1}{\alpha}$)-robust (competitiveness under terrible predictions), where $\alpha \in (0, 1]$ is a hyper-parameter representing the level of distrust in the predictions. We also study the impact of mispredictions on the competitive ratio of the proposed algorithm and adapt it to achieve a bounded robustness while retaining its consistency. We further establish a lower bound of $\frac{3}{2}$ on the consistency of any deterministic learning-augmented algorithm. Experimental evaluations are carried out to evaluate our algorithms using real data access traces.