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ISSN: 2168-7161
Ehsan Ataie , Computer Engineering, Sharif University of Technology, 68260 Tehran, Tehran Iran (the Islamic Republic of) (e-mail:
Reza Entezari-Maleki , School of Computer Science, Institute for Research in Fundamental Sciences, 48449 Tehran, Tehran Iran (the Islamic Republic of) 1953833511 (e-mail:
Leila Rashidi , Computer Engineering, Sharif University of Technology, 68260 Tehran, Tehran Iran (the Islamic Republic of) (e-mail:
Kishor S. Trivedi , Electrical & Computer Eng., Duke University, Durham, North Carolina United States 27708-0291 (e-mail:
Danilo Ardagna , Elettronica e Informazione, Politecnico di Milano, Milan, MI Italy 20133 (e-mail:
Ali Movaghar , Computer Engineering, Sharif University of Technology, Tehran, Tehran Iran, Islamic Republic of 1998717869 (e-mail:
Infrastructure as a Service (IaaS) is one of the most significant and fastest growing fields in cloud computing. To efficiently use the resources of an IaaS cloud, several important factors such as performance, availability, and power consumption need to be considered and evaluated carefully. Evaluation of these metrics is essential for cost-benefit prediction and quantification of different strategies which can be applied to cloud management. In this paper, analytical models based on Stochastic Reward Nets (SRNs) are proposed to model and evaluate an IaaS cloud system at different levels. To achieve this, an SRN is initially presented to model a group of physical machines which are controlled by a management layer. Afterwards, the SRN models presented for the groups of physical machines in the first stage are combined to capture a monolithic model representing an entire IaaS cloud. Since the monolithic model does not scale well for large cloud systems, two approximate SRN models using folding and fixed-point iteration techniques are proposed to evaluate the performance, availability, and power consumption of the IaaS cloud. The existence of a solution for the fixed-point approximate model is proved using Brouwer's fixed-point theorem. A validation of the proposed monolithic and approximate models against both an ad-hoc discrete-event simulator developed in Java and the CloudSim framework is presented. The analytic-numeric results obtained from applying the proposed models to sample cloud systems show that the errors introduced by approximate models are insignificant while an improvement of several orders of magnitude in the state space reduction of the monolithic model is obtained.
Cloud computing, Computational modeling, Analytical models, Power demand, Stochastic processes, Measurement

E. Ataie, R. Entezari-Maleki, L. Rashidi, K. S. Trivedi, D. Ardagna and A. Movaghar, "Hierarchical Stochastic Models for Performance, Availability, and Power Consumption Analysis of IaaS Clouds," in IEEE Transactions on Cloud Computing.
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