Tree-structured data regeneration with network coding in distributed storage systems

Distributed storage systems, built on peer-to-peer networks, can provide large-scale data storage and high data reliability by redundant schemes, such as replica, erasure codes and linear network coding. Redundant data may get lost due to the instability of distributed systems, such as permanent node departures, hardware failures, and accidental deletions. In order to maintain data availability, it is necessary to regenerate new redundant data in another node, referred to as a newcomer. Regeneration is expected to be finished as soon as possible, because the regeneration time can influence the data reliability and availability of distributed storage systems. It has been acknowledged that linear network coding can regenerate redundant data with less network traffic than replica and erasure codes. However, previous regeneration schemes are all star-structured regeneration schemes, in which data are transferred directly from existing storage nodes, referred to as providers, to the newcomer, so the regeneration time is always limited by the path with the narrowest bandwidth between newcomer and provider, due to bandwidth heterogeneity. In this paper, we exploit the bandwidth between providers and propose a tree-structured regeneration scheme using linear network coding. In our scheme, data can be transferred from providers to the newcomer through a regeneration tree, defined as a spanning tree covering the newcomer and all the providers. In a regeneration tree, a provider can receive data from other providers, then encode the received data with the data this provider stores, and finally send the encoded data to another provider or to the newcomer. We prove that a maximum spanning tree is an optimal regeneration tree and analyze its performance. In a trace-based simulation, the results show the tree-structured scheme can reduce the regeneration time by 75%-82% and improve data availability by 73%-124%.