Cost-Driven Data Replication with Predictions
<|reference_start|>Cost-Driven Data Replication with Predictions: 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.<|reference_end|>
@article{zuo2024cost-driven,
    title={Cost-Driven Data Replication with Predictions},
    author={Tianyu Zuo, Xueyan Tang, Bu Sung Lee},
    journal={arXiv preprint arXiv:2404.16489},
    year={2024},
    doi={10.1145/3626183.3659964},
    archivePrefix={arXiv},
    eprint={2404.16489},
    primaryClass={cs.DS}
}