QoS prediction methods to avoid SLA violation in post-interaction time phase

Hussain, W; Hussain, F; Hussain, O

QoS prediction methods to avoid SLA violation in post-interaction time phase

Hussain, W

Hussain, F

Hussain, O

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016, 2016, pp. 32 - 37
Issue Date:: 2016-10-19

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Field	Value	Language
dc.contributor.author	Hussain, W https://orcid.org/0000-0003-0610-4006	en_US
dc.contributor.author	Hussain, F https://orcid.org/0000-0003-1513-8072	en_US
dc.contributor.author	Hussain, O	en_US
dc.date.issued	2016-10-19	en_US
dc.identifier.citation	Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016, 2016, pp. 32 - 37	en_US
dc.identifier.isbn	9781509026050	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121814
dc.description.abstract	© 2016 IEEE. Due to the dynamic nature of cloud computing it is very important for a small to medium scale service providers to optimally assign computing resources and apply accurate prediction methods that enable the best resource management. The choice of an ideal quality of service (QoS) prediction method is one of the key factors in business transactions that help a service provider manage the risk of SLA violations by taking appropriate and immediate action to reduce occurrence, or avoid operations that may cause risk. In this paper we analyze ten prediction methods, including neural network methods, stochastic methods and others to predict time series cloud data and compare their prediction accuracy over five time intervals. We use Cascade Forward Backpropagation, Elman Backpropagation, Generalized Regression, NARX, Simple Exponential Smoothing, Simple Moving Average, Weighted Moving Average, Extrapolation, Holt-Winters Double Exponential Smoothing and ARIMA and predict resource usage at 1, 2, 3, 4 and 5 hours into the future. We use Root Means Square Error and Mean Absolute Deviation as a benchmark for their prediction accuracy. From the prediction results we observed that the ARIMA method provides the most optimal prediction results for all time intervals.	en_US
dc.relation.ispartof	Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016	en_US
dc.relation.isbasedon	10.1109/ICIEA.2016.7603547	en_US
dc.title	QoS prediction methods to avoid SLA violation in post-interaction time phase	en_US
dc.type	Conference Proceeding
utslib.for	080301 Bioinformatics Software	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. Due to the dynamic nature of cloud computing it is very important for a small to medium scale service providers to optimally assign computing resources and apply accurate prediction methods that enable the best resource management. The choice of an ideal quality of service (QoS) prediction method is one of the key factors in business transactions that help a service provider manage the risk of SLA violations by taking appropriate and immediate action to reduce occurrence, or avoid operations that may cause risk. In this paper we analyze ten prediction methods, including neural network methods, stochastic methods and others to predict time series cloud data and compare their prediction accuracy over five time intervals. We use Cascade Forward Backpropagation, Elman Backpropagation, Generalized Regression, NARX, Simple Exponential Smoothing, Simple Moving Average, Weighted Moving Average, Extrapolation, Holt-Winters Double Exponential Smoothing and ARIMA and predict resource usage at 1, 2, 3, 4 and 5 hours into the future. We use Root Means Square Error and Mean Absolute Deviation as a benchmark for their prediction accuracy. From the prediction results we observed that the ARIMA method provides the most optimal prediction results for all time intervals.

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http://hdl.handle.net/10453/121814