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ThinkMind // ADAPTIVE 2019, The Eleventh International Conference on Adaptive and Self-Adaptive Systems and Applications // View article adaptive_2019_6_10_50013


A Controller Architecture for Anomaly Detection, Root Cause Analysis and Self-Adaptation for Cluster Architectures

Authors:
Areeg Samir
Claus Pahl

Keywords: Cloud Computing; Container Clusters; Hidden Markov Model; Workload; Anomaly; Performance.

Abstract:
Service-based cloud computing allows applications to be deployed and managed through third-party provided services, making typically virtualised resources available. However, often there is no direct access to platform-level execution parameters of a provided service, and only some quality properties can be directly observed while others remain hidden from the service consumer. We introduce a controller architecture for autonomous, self-adaptive anomaly remediation in this semi-hidden setting. The controller determines the possible causes of consumer-observed anomalies 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.

Pages: 75 to 83

Copyright: Copyright (c) IARIA, 2019

Publication date: May 5, 2019

Published in: conference

ISSN: 2308-4146

ISBN: 978-1-61208-706-1

Location: Venice, Italy

Dates: from May 5, 2019 to May 9, 2019

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