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Anomaly Detection and Analysis for Clustered Cloud Computing Reliability

Authors:
Areeg Samir
Claus Pahl

Keywords: Cloud Computing; Edge Computing; Container Cluster; Hidden Markov Model; Anomaly; Performance.

Abstract:
Cloud and edge computing allow applications to be deployed and managed through third-party provided services that typically make virtualised resources available. However, often there is no direct insight into execution parameters at resource level, and only some quality factors can be directly observed while others remain hidden from the consumer. We investigate a framework for autonomous anomaly analysis for clustered cloud or edge resources. The framework determines possible causes of consumer-observed anomalies in an underlying provider-controlled infrastructure. We use Hidden Markov Models to map observed performance anomalies to hidden resources, and to identify the root causes of the observed anomalies in order to improve reliability. We apply the model to clustered hierarchically organised cloud computing resources.

Pages: 110 to 119

Copyright: Copyright (c) IARIA, 2019

Publication date: May 5, 2019

Published in: conference

ISSN: 2308-4294

ISBN: 978-1-61208-703-0

Location: Venice, Italy

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