Huan Ke, The University of Chicago
Massive storage systems composed of tens of thousands of disks are increasingly common in high-performance computing data centers. With such an enormous number of components integrated within the storage system the probability for correlated failures across a large number of components becomes a critical concern in preventing data loss. To better protect against correlated failures we introduce Single-Overlap Declustered Parity (SODP), a novel declustered parity design that tolerates large numbers of disk failures and minimizes rebuild time. Our evaluation results show during large failure bursts SODP improves protection against data loss by 20x compared to traditional declustered parity and provides almost identical rebuild times compared to the current state of the art.
Huan Ke, The University of Chicago
Huan Ke is a PhD student in the Department of Computer Science at the University of Chicago. Her supervisor is Professor Haryadi S. Gunawi. Currently, she is doing her internship in Los Alamos National Laboratory, where she primarily works with Bradley Settlemyer. Her research interests lie in the system performance, reliability and scalability. Particularly, she works on modelling system behaviors, detecting concurrency bugs, optimizing tail-latency problems, and checking scalability issues. Now her research agenda explores how to develop new data layouts to tolerate massive failures in the large-scale storage systems.
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author = {Huan Ke},
title = {Surviving a Disk Apocalypse with {Single-Overlap} Declustered Parity},
year = {2020},
address = {Santa Clara, CA},
publisher = {USENIX Association},
month = feb
}