On Distributed Storage Allocations for Memory-Limited Systems

TL;DR

This paper introduces a tractable relaxation method for optimizing distributed storage allocations under memory constraints, applicable to large networks and multiple data objects, improving efficiency in memory-limited systems.

Contribution

It presents a new relaxation approach for symmetric storage allocations, enabling solutions for memory-limited distributed storage systems with arbitrary memory profiles.

Findings

The relaxation approximates the original problem well as network size increases.

The method effectively handles multiple data objects in memory-limited storage systems.

Abstract

In this paper we consider distributed allocation problems with memory constraint limits. Firstly, we propose a tractable relaxation to the problem of optimal symmetric allocations from [1]. The approximated problem is based on the Q-error function, and its solution approaches the solution of the initial problem, as the number of storage nodes in the network grows. Secondly, exploiting this relaxation, we are able to formulate and to solve the problem for storage allocations for memory-limited DSS storing and arbitrary memory profiles. Finally, we discuss the extension to the case of multiple data objects, stored in the DSS.