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Distributed storage systems provide large-scale reliable data storageservices by spreading redundancy across a large group of storage nodes. In sucha large system, node failures take place on a regular basis. When a storagenode breaks down, a replacement node is expected to regenerate the redundantdata as soon as possible in order to maintain the same level of redundancy.Previous results have been mainly focused on the minimization of networktraffic in regeneration. However, in practical networks, where link capacitiesvary in a wide range, minimizing network traffic does not always yield theminimum regeneration time. In this paper, we investigate two approaches to theproblem of minimizing regeneration time in networks with heterogeneous linkcapacities. The first approach is to download different amounts of repair datafrom the helping nodes according to the link capacities. The second approachgeneralizes the conventional star-structured regeneration topology totree-structured topologies so that we can utilize the links between helpingnodes with bypassing low-capacity links. Simulation results show that theflexible tree-structured regeneration scheme that combines the advantages ofboth approaches can achieve a substantial reduction in the regeneration time.

Accelerating Data Regeneration for Distributed Storage Systems with Heterogeneous Link Capacities