Decentralized Minimum-Cost Repair for Distributed Storage Systems

TL;DR

This paper proposes a decentralized approach to minimize repair costs in distributed storage systems by formulating the problem as convex optimization and applying primal-dual decomposition, enabling local solutions that optimize global transmission costs.

Contribution

It introduces a decentralized convex optimization framework for optimal-cost repair in distributed storage, with algorithms ensuring convergence and practical code construction.

Findings

Decentralized algorithms effectively minimize transmission costs.

The approach guarantees convergence to optimal solutions.

Feasible network codes are constructed with determined field sizes.

Abstract

There have been emerging lots of applications for distributed storage systems e.g., those in wireless sensor networks or cloud storage. Since storage nodes in wireless sensor networks have limited battery, it is valuable to find a repair scheme with optimal transmission costs (e.g., energy). The optimal-cost repair has been recently investigated in a centralized way. However a centralized control mechanism may not be available or is very expensive. For the scenarios, it is interesting to study optimal-cost repair in a decentralized setup. We formulate the optimal-cost repair as convex optimization problems for the network with convex transmission costs. Then we use primal and dual decomposition approaches to decouple the problem into subproblems to be solved locally. Thus, each surviving node, collaborating with other nodes, can minimize its transmission cost such that the global cost is minimized. We further study the optimality and convergence of the algorithms. Finally, we discuss the code construction and determine the field size for finding feasible network codes in our approaches.