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Towards operator-less data centers through data-driven, predictive, proactive autonomics

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Abstract

Continued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, and at that point, the intervention of humans will be limited to setting high-level goals and policies rather than performing low-level operations. Data-driven autonomics, where management and control are based on holistic predictive models that are built and updated using live data, opens one possible path towards limiting the role of operators in data centers. In this paper, we present a data-science study of a public Google dataset collected in a 12K-node cluster with the goal of building and evaluating predictive models for node failures. Our results support the practicality of a data-driven approach by showing the effectiveness of predictive models based on data found in typical data center logs. We use BigQuery, the big data SQL platform from the Google Cloud suite, to process massive amounts of data and generate a rich feature set characterizing node state over time. We describe how an ensemble classifier can be built out of many Random Forest classifiers each trained on these features, to predict if nodes will fail in a future 24-h window. Our evaluation reveals that if we limit false positive rates to 5 %, we can achieve true positive rates between 27 and 88 % with precision varying between 50 and 72 %. This level of performance allows us to recover large fraction of jobs’ executions (by redirecting them to other nodes when a failure of the present node is predicted) that would otherwise have been wasted due to failures. We discuss the feasibility of including our predictive model as the central component of a data-driven autonomic manager and operating it on-line with live data streams (rather than off-line on data logs). All of the scripts used for BigQuery and classification analyses are publicly available on GitHub.

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Notes

  1. Delfina Eberly, Director of Data Center Operations at Facebook, speaking on “Operations at Scale” at the 7 \(\times \) 24 Exchange 2013 Fall Conference.

  2. Based on current Google BigQuery pricing.

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Acknowledgments

BigQuery analysis was carried out through a generous Cloud Credits grant from Google. We are grateful to John Wilkes of Google for helpful discussions regarding the cluster trace data.

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Authors and Affiliations

  1. Department of Computer Science, University of Pisa, Largo Bruno Pontecorvo 3, 56127, Pisa, Italy

    Alina Sîrbu

  2. Department of Computer Science and Engineering, University of Bologna, Mura Anteo Zamboni 7, 40126, Bologna, Italy

    Alina Sîrbu & Ozalp Babaoglu

Authors
  1. Alina Sîrbu
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  2. Ozalp Babaoglu
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Correspondence to Alina Sîrbu.

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Sîrbu, A., Babaoglu, O. Towards operator-less data centers through data-driven, predictive, proactive autonomics. Cluster Comput 19, 865–878 (2016). https://doi.org/10.1007/s10586-016-0564-y

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  • DOI: https://doi.org/10.1007/s10586-016-0564-y

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