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ThinkMind // International Journal On Advances in Software, volume 12, numbers 3 and 4, 2019 // View article soft_v12_n34_2019_14


A Controller for Anomaly Detection, Analysis and Management for Self-Adaptive Container Clusters

Authors:
Areeg Samir
Nabil El Ioini
Ilenia Fronza
Hamid R. Barzegar
Van Thanh Le
Claus Pahl

Keywords: Cloud Computing; Container Technology, Distributed Clusters; Markov Model; Anomaly Detection; Anomaly Analysis; Workload; Performance

Abstract:
Service computing in the cloud allows applications to be deployed remotely. These are managed by third-party service providers that make virtualised resources available for these services. Self-adaptive features for load-balancing and auto-scaling are available here, but generally there is no direct access to the infrastructure or platform-level execution environment. Some quality parameters of a provided service can be directly observed while others remain hidden from the service consumer. Our solution is an autonomous self-adaptive controller for anomaly remediation in this semi-hidden setting. The objective of the controller is to, firstly, determine possible root causes of consumer-observed anomalies and, secondly, take appropriate action. This needs to happen in an underlying provider-controlled infrastructure. We use Hidden Markov Models to map observed performance anomalies into hidden resources, and to identify the root causes of the observed anomalies. We apply the model to a clustered computing resource environment that is based on three layers of aggregated resources. We discuss use cases to illustrate the utility of the proposed solution.

Pages: 356 to 371

Copyright: Copyright (c) to authors, 2019. Used with permission.

Publication date: December 30, 2019

Published in: journal

ISSN: 1942-2628

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