Anomaly Detection in Cloud Components

• URL: http://arxiv.org/abs/2005.08739v2
• Date: Fri, 7 Aug 2020 14:34:41 GMT
• Title: Anomaly Detection in Cloud Components
• Authors: Mohammad Saiful Islam and Andriy Miranskyy
• Abstract summary: Gated-Recurrent-Unit-based autoencoder was tested with a likelihood function to detect anomalies in various time series and achieved high performance.
• Score: 0.8883733362171035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cloud platforms, under the hood, consist of a complex inter-connected stack of hardware and software components. Each of these components can fail which may lead to an outage. Our goal is to improve the quality of Cloud services through early detection of such failures by analyzing resource utilization metrics. We tested Gated-Recurrent-Unit-based autoencoder with a likelihood function to detect anomalies in various multi-dimensional time series and achieved high performance.

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