Towards Operator-less Data Centers Through Data-Driven, Predictive, Proactive Autonomics

TL;DR

This paper demonstrates that data-driven predictive models can effectively forecast data center node failures, enabling proactive management and reducing operational costs in large-scale data centers.

Contribution

It introduces a practical approach using ensemble classifiers trained on live data to predict node failures, advancing autonomous data center management.

Findings

Predictive models achieve up to 88% true positive rate at 5% false positive rate.

High precision (50-72%) in failure prediction supports effective job rerouting.

Data-driven models are practical for large-scale, real-world data center management.

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-hour 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. [...]