Rethinking erasure codes for cloud file systems: minimizing I/O for recovery and degraded reads

By Osama Khan, Randal C. Burns, James S. Plank, William Pierce, Cheng Huang. USENIX Conference on File and Storage Technologies, 2012

376 citations recorded by the source corpus.

How researchers see this paper

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Erasure Coding in Windows Azure Storage (2012, background): “However, practical solutions known so far [26, 27] achieve only around 20%-30% savings in terms of I/O and bandwidth, much less than LRC.”
A Solution to the Network Challenges of Data Recovery in Erasure-coded Distributed Storage Systems: A Study on the Facebook Warehouse Cluster (2013, methodology): “For the parameters where [14, 16, 17] exist, we have shown in [5] that the Piggybacked-RS codes perform at least as well.”
Pocket: Elastic Ephemeral Storage for Serverless Analytics (2018, background): “Hence, it is common for storage systems to use fault-tolerance techniques such as replication and erasure codes [42, 66, 46].”