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