On Storage Allocation for Maximum Service Rate in Distributed Storage Systems

TL;DR

This paper analyzes how storage allocation strategies in distributed systems impact overall service rate, considering access failures and data recovery success, and finds that minimal spreading or replication maximizes service rate under certain conditions.

Contribution

It introduces a comprehensive service rate analysis incorporating access models and recovery success for quasi-uniform storage allocations in distributed systems.

Findings

Minimal spreading allocation maximizes service rate with exponential node waiting times.

Replication outperforms coded storage in terms of service rate for given storage budgets.

Incorporating access failure models provides more accurate performance insights.

Abstract

Storage allocation affects important performance measures of distributed storage systems. Most previous studies on the storage allocation consider its effect separately either on the success of the data recovery or on the service rate (time) where it is assumed that no access failure happens in the system. In this paper, we go one step further and incorporate the access model and the success of data recovery into the service rate analysis. In particular, we focus on quasi-uniform storage allocation and provide a service rate analysis for both fixed-size and probabilistic access models at the nodes. Using this analysis, we then show that for the case of exponential waiting time distribution at individuals storage nodes, minimal spreading allocation results in the highest system service rate for both access models. This means that for a given storage budget, replication provides a better service rate than a coded storage solution.