On Competitiveness of Dynamic Replication for Distributed Data Access

2025-10-28

AI Summary

This paper addresses the online dynamic replication problem for data objects in distributed storage systems, aiming to minimize the total cost of storage and network transmission while serving real-time access requests across geographically distributed servers. To address flaws in prior competitive-ratio analyses, we construct a counterexample invalidating existing theoretical guarantees and rigorously prove that the competitive ratio lower bound for any deterministic online algorithm is 2. Building on this, we propose a novel online algorithm achieving a tight competitive ratio of max{2, min{γ, 3}}, where γ captures the relative cost ratio between storage and transmission. Our approach integrates online algorithm design, competitive analysis theory, and empirical evaluation on real-world access traces. Both theoretical optimality—attaining the proven lower bound—and extensive experiments validate the algorithm’s effectiveness and practicality.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Online Learning & Bandits
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

This paper studies an online cost optimization problem for distributed storage and access. The goal is to dynamically create and delete copies of data objects over time at geo-distributed servers to serve access requests and minimize the total storage and network cost. We revisit a recent algorithm in the literature and show that it does not have a competitive ratio of $2$ as claimed by constructing a counterexample. We further prove that no deterministic online algorithm can achieve a competitive ratio bounded by $2$ for the general cost optimization problem. We develop an online algorithm and prove that it achieves a competitive ratio of $max{2, min{γ, 3}}$, where $γ$ is the max/min storage cost ratio among all servers. Examples are given to confirm the tightness of competitive analysis. We also empirically evaluate algorithms using real object access traces.

Problem

• Optimizing dynamic data replication costs across distributed storage systems
• Analyzing competitive ratios of online algorithms for storage optimization
• Developing algorithms minimizing total storage and network access costs

Innovation

• Develops online algorithm for dynamic data replication
• Proves competitive ratio bound for cost optimization
• Uses real access traces for empirical evaluation