research-article

PreFix: Switch Failure Prediction in Datacenter Networks

Authors:

Shenglin Zhang,

Lei SongAuthors Info & Claims

Proceedings of the ACM on Measurement and Analysis of Computing Systems, Volume 2, Issue 1

Article No.: 2, Pages 1 - 29

https://doi.org/10.1145/3179405

Published: 03 April 2018 Publication History

Abstract

In modern datacenter networks (DCNs), failures of network devices are the norm rather than the exception, and many research efforts have focused on dealing with failures after they happen. In this paper, we take a different approach by predicting failures, thus the operators can intervene and "fix" the potential failures before they happen. Specifically, in our proposed system, named PreFix, we aim to determine during runtime whether a switch failure will happen in the near future. The prediction is based on the measurements of the current switch system status and historical switch hardware failure cases that have been carefully labelled by network operators. Our key observation is that failures of the same switch model share some common syslog patterns before failures occur, and we can apply machine learning methods to extract the common patterns for predicting switch failures. Our novel set of features (message template sequence, frequency, seasonality and surge) for machine learning can efficiently deal with the challenges of noises, sample imbalance, and computation overhead. We evaluated PreFix on a data set collected from 9397 switches (3 different switch models) deployed in more than 20 datacenters owned by a top global search engine in a 2-year period. PreFix achieved an average of 61.81% recall and 1.84 * 10^-5 false positive ratio. It outperforms the other failure prediction methods for computers and ISP devices.

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Index Terms

PreFix: Switch Failure Prediction in Datacenter Networks
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Networks
  1. Network performance evaluation
    1. Network performance modeling

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cover image Proceedings of the ACM on Measurement and Analysis of Computing Systems

Proceedings of the ACM on Measurement and Analysis of Computing Systems Volume 2, Issue 1

March 2018

603 pages

EISSN:2476-1249

DOI:10.1145/3203302

Editors:
Augustin Chaintreau
Columbia University
,
Aditya Akella
University of Wisconsin-Madison
,
Adam Wierman
California Institute of Technology

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Published: 03 April 2018

Published in POMACS Volume 2, Issue 1

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https://dl.acm.org/doi/10.1145/3637528.3671810
Qiu HMao WWang CJha SFranke HNarayanaswami CKalbarczyk ZBaşar TIyer R(2024)When Green Computing Meets Performance and Resilience SLOs2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S)10.1109/DSN-S60304.2024.00015(17-22)Online publication date: 24-Jun-2024
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Zhuang HLi XChang JLiu X(2024)Paired 2-disjoint path covers of k-ary n-cubes under the partitioned edge fault modelJournal of Parallel and Distributed Computing10.1016/j.jpdc.2024.104887(104887)Online publication date: Mar-2024
https://doi.org/10.1016/j.jpdc.2024.104887
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Hadadi FDawes JShin DBianculli DBriand L(2024)Systematic Evaluation of Deep Learning Models for Log-based Failure PredictionEmpirical Software Engineering10.1007/s10664-024-10501-429:5Online publication date: 20-Jun-2024
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