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Classical erasure codes, e.g. Reed-Solomon codes, have been acknowledged as an efficient alternative to plain replication to reduce the storage overhead in reliable distributed storage systems. Yet, such codes experience high overhead during the maintenance process. In this paper we propose a novel erasure-coded framework especially tailored for networked storage systems. Our approach relies on the use of random codes coupled with a clustered placement strategy, enabling the maintenance of a failed machine at the granularity of multiple files. Our repair protocol leverages network coding techniques to reduce by half the amount of data transferred during maintenance, as several files can be repaired simultaneously. This approach, as formally proven and demonstrated by our evaluation on a public experimental testbed, enables to dramatically decrease the bandwidth overhead during the maintenance process, as well as the time to repair a failure. In addition, the implementation is made as simple as possible, aiming at a deployment into practical systems.
Distributed storage systems provide reliable access to data through redundancy spread over individually unreliable nodes. Application scenarios include data centers, peer-to-peer storage systems, and storage in wireless networks. Storing data using a
Erasure coding techniques are getting integrated in networked distributed storage systems as a way to provide fault-tolerance at the cost of less storage overhead than traditional replication. Redundancy is maintained over time through repair mechani
We present Kaleidoscope an innovative system that supports live forensics for application performance problems caused by either individual component failures or resource contention issues in large-scale distributed storage systems. The design of Kale
Erasure codes are increasingly being studied in the context of implementing atomic memory objects in large scale asynchronous distributed storage systems. When compared with the traditional replication based schemes, erasure codes have the potential
In todays enterprise storage systems, supported data services such as snapshot delete or drive rebuild can cause tremendous performance interference if executed inline along with heavy foreground IO, often leading to missing SLOs (Service Level Objec