IASO: A Fail-Slow Detection and Mitigation Framework for Distributed Storage Services

Authors: 

Biswaranjan Panda and Deepthi Srinivasan, Nutanix Inc.; Huan Ke, University of Chicago; Karan Gupta and Vinayak Khot, Nutanix Inc.; Haryadi S. Gunawi, University of Chicago

Abstract: 

We address the problem of “fail-slow” fault, a fault where a hardware or software component can still function (does not fail-stop) but in much lower performance than expected. To address this, we built IASO, a peer-based, non-intrusive fail-slow detection framework that has been deployed for more than 1.5 years across 39,000 nodes in our customer sites and helped our customers reduce major outages due to fail-slow incidents. IASO primarily works based on timeout signals (a negligible overhead of monitoring) and converts them into a stable and accurate fail-slow metric. IASO can quickly and accurately isolate a slow node within minutes. Within a 7-month period, IASO managed to catch 232 fail-slow incidents in our large deployment field. In this paper, we have also assembled a large dataset of 232 fail-slow incidents along with our analysis. We found that the fail-slow annual failure rate in our field is 1.02%.

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BibTeX
@inproceedings {234960,
author = {Biswaranjan Panda and Deepthi Srinivasan and Huan Ke and Karan Gupta and Vinayak Khot and Haryadi S. Gunawi},
title = {{IASO}: A Fail-Slow Detection and Mitigation Framework for Distributed Storage Services},
booktitle = {2019 {USENIX} Annual Technical Conference ({USENIX} {ATC} 19)},
year = {2019},
isbn = {978-1-939133-03-8},
address = {Renton, WA},
pages = {47--62},
url = {https://www.usenix.org/conference/atc19/presentation/panda},
publisher = {{USENIX} Association},
}