Efficient routing for cooperative data regeneration in heterogeneous storage networks

Large-scale storage systems often face node failures that lead to data loss. Cooperative regeneration has been extensively studied to minimize the repair traffic of simultaneously reconstructing the lost data of multiple failed nodes. However, existing cooperative regeneration schemes assume that nodes are homogeneous. They do not consider how to minimize the general regenerating cost when taking into account node heterogeneity. This paper presents the first systematic study on enhancing conventional cooperation regeneration (CCR) schemes in a heterogeneous environment. We formulate cooperative regeneration as a cost-based routing optimization model, and propose a new cost-based heterogeneity-aware cooperative regeneration (HCR) framework. The main novelty of HCR is to decompose CCR schemes into two stages (i.e., expansion and aggregation) that can be opportunistically carried out by different nodes depending on their costs. To efficiently select the nodes for expansion execution without exhaustive enumeration, we design two greedy algorithms based on the hill-climbing technique. We also formulate the routing problem in the aggregation stage as a Steiner Tree Problem. Finally, we conduct extensive trace-driven simulations and show that HCR can reduce up to 75.4% transmission time of CCR. Also, we demonstrate that HCR remains robust even when the heterogeneity information is not accurately measured.