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The Journal of Supercomputing

Erschienen in:

27.07.2018

A joint feature selection framework for multivariate resource usage prediction in cloud servers using stability and prediction performance

verfasst von: Shaifu Gupta, A. D. Dileep, Timothy A. Gonsalves

Erschienen in: The Journal of Supercomputing | Ausgabe 11/2018

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Abstract

Resource provisioning in cloud servers depends on future resource utilization of different jobs. As resource utilization trends vary dynamically, effective resource provisioning requires prediction of future resource utilization. The problem becomes more complicated as performance metrics related to one resource may depend on utilization of other resources also. In this paper, different multivariate frameworks are proposed for improving the future resource metric prediction in cloud. Different techniques for identifying the set of resource metrics relevant for the prediction of desired resource metric are analyzed. The proposed multivariate feature selection and prediction frameworks are validated for CPU utilization prediction in Google cluster trace. Joint analysis based on the prediction performance of the multivariate framework as well as its stability is used for selecting the most suitable feature selection framework. The results of the joint analysis indicate that features selected using the Granger causality technique perform best for multivariate resource usage prediction.

Vorheriger Artikel A survey on optimal utilization of preemptible VM instances in cloud computing

Nächster Artikel Internet of Music Things: an edge computing paradigm for opportunistic crowdsensing

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Titel: A joint feature selection framework for multivariate resource usage prediction in cloud servers using stability and prediction performance
verfasst von: Shaifu Gupta
A. D. Dileep
Timothy A. Gonsalves
Publikationsdatum: 27.07.2018
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 11/2018
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-018-2510-7

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