Erasure Coding in Windows Azure Storage

By Cheng Huang, Huseyin Simitci, Yikang Xu, Aaron Ogus, Brad Calder, Parikshit Gopalan, Jin Li, Sergey Yekhanin. USENIX Annual Technical Conference, 2012

1,141 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/35cb2b9febada179689724c78dfe31d9fa3f74c4

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Citations in the corpus

Congestion Control for Large-Scale RDMA Deployments (2015, background): “We vary the degree of incast from 2 to 10, to model different erasure coding patterns [16].”
Locally Repairable Codes (2012, background): “The main properties that we exploit here are the following: entropy is oblivious to the encoding process (linear or nonlinear), it can accommodate different input or output symbol sizes, and different symbol representations (scalar or vector).”
TOFEC: Achieving optimal throughput-delay trade-off of cloud storage using erasure codes (2013, background): “…high degree of availability, scalability, and data durability, each object is replicated several times within the internal distributed ﬁle system and sometimes also further protected by erasure codes to more efﬁciently use the storage capacity while attaining very high durability guarantees [1].”
Analyzing the download time of availability codes (2015, methodology): “It is worth noting that MTTDL of a 3-replication is typically considered as the performance objective while choosing an erasure code for a storage scheme [24, 48].”
Speeding Up Distributed Machine Learning Using Codes (2015, background): “Recently, codes have begun to transform the storage layer of distributed systems in modern data centers under the umbrella of regenerating and locally repairable codes for distributed storage [6]–[21] which are also having a major impact on industry [22]–[25].”
A Solution to the Network Challenges of Data Recovery in Erasure-coded Distributed Storage Systems: A Study on the Facebook Warehouse Cluster (2013, background): “In [18,19], a new class of codes called LRCs are proposed that reduce the disk and network bandwidth requirement during recovery.”
When do redundant requests reduce latency ? (2013, methodology): “[6] consider a distributed stoage system where the data is stored using an (n = 16, k = 12) Reed-Solomon code.”
f4: Facebook's Warm BLOB Storage System (2014, methodology): “Windows Azure storage [33] uses new Local Reconstruction Codes for efficient recovery with local and global parity information, but is not a Maximum Distance Separable (MDS) code.”
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].”
Locality and Availability in Distributed Storage (2014, background): “The authors would like to thank Arya Mazumdar and Natalia Silberstein for valuable discussions. developed, each optimized for a different repair cost metric.”