Capacity of dynamical storage systems

TL;DR

This paper models the capacity of distributed storage systems with dynamically failing nodes, showing that adaptive repair strategies can increase capacity compared to static models by leveraging failure sequence information.

Contribution

It introduces a Markov model for node failures and demonstrates how dynamic repair policies can enhance storage capacity over static approaches.

Findings

Capacity can be increased with dynamic repair strategies.

Knowledge of failure sequences further improves capacity.

The model quantifies capacity gains over static models.

Abstract

We introduce a dynamical model of node repair in distributed storage systems wherein the storage nodes are subjected to failures according to independent Poisson processes. The main parameter that we study is the time-average capacity of the network in the scenario where a fixed subset of the nodes support a higher repair bandwidth than the other nodes. The sequence of node failures generates random permutations of the nodes in the encoded block, and we model the state of the network as a Markov random walk on permutations of $n$ elements. As our main result we show that the capacity of the network can be increased compared to the static (worst-case) model of the storage system, while maintaining the same (average) repair bandwidth, and we derive estimates of the increase. We also quantify the capacity increase in the case that the repair center has information about the sequence of the recently failed storage nodes.