Distributed Storage Allocations for Neighborhood-based Data Access

TL;DR

This paper proposes a neighborhood-based distributed data storage allocation model that minimizes storage costs while guaranteeing data recovery, using a novel distributed algorithm based on dual decomposition.

Contribution

It introduces a new neighborhood-based data access model and develops a distributed algorithm with provable approximation guarantees for optimal storage allocation.

Findings

The algorithm achieves a (1+ε)-approximation ratio.

The algorithm converges in O(d_max^{3/2}/ε) iterations.

Simulations confirm the algorithm's effectiveness.

Abstract

We introduce a neighborhood-based data access model for distributed coded storage allocation. Storage nodes are connected in a generic network and data is accessed locally: a user accesses a randomly chosen storage node, which subsequently queries its neighborhood to recover the data object. We aim at finding an optimal allocation that minimizes the overall storage budget while ensuring recovery with probability one. We show that the problem reduces to finding the fractional dominating set of the underlying network. Furthermore, we develop a fully distributed algorithm where each storage node communicates only with its neighborhood in order to find its optimal storage allocation. The proposed algorithm is based upon the recently proposed proximal center method--an efficient dual decomposition based on accelerated dual gradient method. We show that our algorithm achieves a $(1+\epsilon)$-approximation ratio in $O(d_{\mathrm{max}}^{3/2}/\epsilon)$ iterations and per-node communications, where $d_{\mathrm{max}}$ is the maximal degree across nodes. Simulations demonstrate the effectiveness of the algorithm.