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Computational Intelligence and Neuroscience
Volume 2017, Article ID 4873459, 12 pages
https://doi.org/10.1155/2017/4873459
Research Article

Adaptive Resource Utilization Prediction System for Infrastructure as a Service Cloud

1Computer Engineering Department, Bahria University, Islamabad, Pakistan
2Department of Electrical Engineering, COMSATS Institute of Information Technology Attock, Attock, Pakistan
3Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Peshawar, Pakistan

Correspondence should be addressed to Qazi Zia Ullah; moc.oohay@naismoc_aiz

Received 31 December 2016; Revised 19 March 2017; Accepted 16 April 2017; Published 25 July 2017

Academic Editor: Silvia Conforto

Copyright © 2017 Qazi Zia Ullah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Infrastructure as a Service (IaaS) cloud provides resources as a service from a pool of compute, network, and storage resources. Cloud providers can manage their resource usage by knowing future usage demand from the current and past usage patterns of resources. Resource usage prediction is of great importance for dynamic scaling of cloud resources to achieve efficiency in terms of cost and energy consumption while keeping quality of service. The purpose of this paper is to present a real-time resource usage prediction system. The system takes real-time utilization of resources and feeds utilization values into several buffers based on the type of resources and time span size. Buffers are read by R language based statistical system. These buffers’ data are checked to determine whether their data follows Gaussian distribution or not. In case of following Gaussian distribution, Autoregressive Integrated Moving Average (ARIMA) is applied; otherwise Autoregressive Neural Network (AR-NN) is applied. In ARIMA process, a model is selected based on minimum Akaike Information Criterion (AIC) values. Similarly, in AR-NN process, a network with the lowest Network Information Criterion (NIC) value is selected. We have evaluated our system with real traces of CPU utilization of an IaaS cloud of one hundred and twenty servers.