Distributed storage allocation for multi-class data

Abstract

Distributed storage systems (DSSs) provide a scalable solution for reliably storing massive amounts of data coming from various sources. Heterogeneity of these data sources often means different data classes (types) exist in a DSS, each needing a different level of quality of service (QoS). As a result, efficient data storage and retrieval processes that satisfy various QoS requirements are needed. This paper studies storage allocation, meaning how the data of different classes is spread over storage nodes, for a multi-class DSS. More specifically, assuming a probabilistic access to the storage nodes, we aim at maximizing the weighted sum of the probability of successful data recovery of data classes, when for each class a minimum QoS (probability of successful recovery) is guaranteed. Solving this optimization problem for a general setup is intractable. Thus, we find the optimal storage allocation when the data of each class is spread minimally over the nodes, i.e. minimal spreading allocation (MSA). Then, by comparing the performance of the optimal MSA with the performance upper bound, we show that the optimal MSA is indeed the optimal storage allocation in many practical cases. Numerical examples are also presented for better illustration of the results.