NDLI: Wavelet-based multi-scale anomaly identification in cloud computing systems

Content Provider	IEEE Xplore Digital Library
Author	Qiang Guan Song Fu
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Univ. of North Texas, Denton, TX, USA (Qiang Guan; Song Fu)
Abstract	Modern cloud computing systems contain thousands of computing and storage servers. Such a scale combined with ever-growing system complexity of their components and interactions, introduces a key challenge to failure and resource management for highly dependable cloud computing. Automated anomaly detection is a crucial technique for understanding emergent, cloud-wide phenomena and self-managing cloud resources for system level dependability assurance. In this paper, we present a wavelet-based multi-scale anomaly identification mechanism, that can analyze profiled cloud performance metrics in both time and frequency domains and identify anomalous cloud behaviors. Learning technologies are exploited to adapt the selection of mother wavelets and a sliding detection window is employed handle cloud dynamicity and improve anomaly detection accuracy. We test a prototype implementation of our cloud anomaly detection mechanism on an institute-wide cloud system. Experimental results show our approach can identify cloud failures accurately.
Sponsorship	IEEE Commun. Soc.
Starting Page	1379
Ending Page	1384
File Size	407508
Page Count	6
File Format	PDF
ISBN	9781479913534
DOI	10.1109/GLOCOM.2013.6831266
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cloud computing Servers Adaptation models Frequency-domain analysis Computational modeling Wavelet domain Autonomic management Anomaly detection Wavelet Analysis Dependable systems
Content Type	Text
Resource Type	Article

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