Anomaly Detection in a Large-scale Cloud Platform

• URL: http://arxiv.org/abs/2010.10966v2
• Date: Thu, 11 Feb 2021 00:55:55 GMT
• Title: Anomaly Detection in a Large-scale Cloud Platform
• Authors: Mohammad Saiful Islam, William Pourmajidi, Lei Zhang, John Steinbacher, Tony Erwin, Andriy Miranskyy
• Abstract summary: Cloud computing is ubiquitous: more and more companies are moving the workloads into the Cloud. Service providers need to monitor the quality of their ever-growing offerings effectively. We designed and implemented an automated monitoring system for the IBM Cloud Platform.
• Score: 9.283888139549067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cloud computing is ubiquitous: more and more companies are moving the workloads into the Cloud. However, this rise in popularity challenges Cloud service providers, as they need to monitor the quality of their ever-growing offerings effectively. To address the challenge, we designed and implemented an automated monitoring system for the IBM Cloud Platform. This monitoring system utilizes deep learning neural networks to detect anomalies in near-real-time in multiple Platform components simultaneously. After running the system for a year, we observed that the proposed solution frees the DevOps team's time and human resources from manually monitoring thousands of Cloud components. Moreover, it increases customer satisfaction by reducing the risk of Cloud outages. In this paper, we share our solutions' architecture, implementation notes, and best practices that emerged while evolving the monitoring system. They can be leveraged by other researchers and practitioners to build anomaly detectors for complex systems.

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