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Authors: David O'Shea 1 ; Vincent C. Emeakaroha 1 ; John Pendlebury 1 ; Neil Cafferkey 1 ; John P. Morrison 1 and Theo Lynn 2

Affiliations: 1 University College Cork, Ireland ; 2 Dublin City University, Ireland

ISBN: 978-989-758-182-3

Keyword(s): Anomaly Detection, Wavelet Transformation, Cloud Monitoring, Data Analysis, Cloud Computing.

Related Ontology Subjects/Areas/Topics: Cloud Applications Performance and Monitoring ; Cloud Computing ; Fundamentals ; Platforms and Applications ; QoS for Applications on Clouds ; Service Monitoring and Control ; Services Science

Abstract: Anomaly detection in Cloud service provisioning platforms is of significant importance, as the presence of anomalies indicates a deviation from normal behaviour, and in turn places the reliability of the distributed Cloud network into question. Existing solutions lack a multi-level approach to anomaly detection in Clouds. This paper presents a wavelet-inspired anomaly detection framework for detecting anomalous behaviours across Cloud layers. It records the evolution of multiple metrics and extracts a two-dimensional spectrogram representing a monitored system’s behaviour. Over two weeks of historical monitoring data were used to train the system to identify healthy behaviour. Anomalies are then characterised as deviations from this expected behaviour. The training technique as well as the pre-processing techniques are highly configurable. Based on a Cloud service deployment use case scenario, the effectiveness of the framework was evaluated by randomly injecting anomalies into the re corded metric data and performing comparison using the resulting spectrograms. (More)

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Paper citation in several formats:
O'Shea, D.; Emeakaroha, V.; Pendlebury, J.; Cafferkey, N.; Morrison, J. and Lynn, T. (2016). A Wavelet-inspired Anomaly Detection Framework for Cloud Platforms.In Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-182-3, pages 106-117. DOI: 10.5220/0005913701060117

@conference{closer16,
author={David O'Shea. and Vincent C. Emeakaroha. and John Pendlebury. and Neil Cafferkey. and John P. Morrison. and Theo Lynn.},
title={A Wavelet-inspired Anomaly Detection Framework for Cloud Platforms},
booktitle={Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2016},
pages={106-117},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005913701060117},
isbn={978-989-758-182-3},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - A Wavelet-inspired Anomaly Detection Framework for Cloud Platforms
SN - 978-989-758-182-3
AU - O'Shea, D.
AU - Emeakaroha, V.
AU - Pendlebury, J.
AU - Cafferkey, N.
AU - Morrison, J.
AU - Lynn, T.
PY - 2016
SP - 106
EP - 117
DO - 10.5220/0005913701060117

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