RAFI: Risk-Aware Failure Identification to Improve the RAS in Erasure-coded Data Centers

Authors: 

Juntao Fang, Wuhan National Laboratory for Optoelectronics, Huazhong University of Sci. and Tech.; Shenggang Wan, School of Computer Science and Technology, Huazhong University of Sci. and Tech.; Xubin He, Department of Computer and Information Sciences, Temple University

Abstract: 

Data reliability and availability, and serviceability (RAS) of erasure-coded data centers are highly affected by data repair induced by node failures. Compared to the recovery phase of the data repair, which is widely studied and well optimized, the failure identification phase of the data repair is less investigated. Moreover, in a traditional failure identification scheme, all chunks share the same identification time threshold, thus losing opportunities to further improve the RAS.

To solve this problem, we propose RAFI, a novel risk-aware failure identification scheme. In RAFI, chunk failures in stripes experiencing different numbers of failed chunks are identified using different time thresholds. For those chunks in a high risk stripe (a stripe with many failed chunks), a shorter identification time is adopted, thus improving the overall data reliability and availability. For those chunks in a low risk stripe (one with only a few failed chunks), a longer identification time is adopted, thus reducing the repair network traffic. Therefore, the RAS can be improved simultaneously.

We use both simulations and prototyping implementation to evaluate RAFI. Results collected from extensive simulations demonstrate the effectiveness and efficiency of RAFI on improving the RAS. We implement a prototype on HDFS to verify the correctness and evaluate the computational cost of RAFI.

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BibTeX
@inproceedings {215969,
author = {Juntao Fang and Shenggang Wan and Xubin He},
title = {{RAFI}: Risk-Aware Failure Identification to Improve the {RAS} in Erasure-coded Data Centers},
booktitle = {2018 {USENIX} Annual Technical Conference ({USENIX} {ATC} 18)},
year = {2018},
isbn = {978-1-939133-01-4},
address = {Boston, MA},
pages = {495--506},
url = {https://www.usenix.org/conference/atc18/presentation/fang},
publisher = {{USENIX} Association},
month = jul,
}

Presentation Audio