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Modeling Active Virtual Machines on IaaS Clouds Using an M/G/m/m plus K Queue

Authors Chang, Xiaolin
Wang, Bin
Muppala, Jogesh K. View this author's profile
Liu, Jiqiang
Issue Date 2016
Source IEEE Transactions on Services Computing , v. 9, (3), May 2016, article number 6971219, p. 408-420
Summary This paper develops a novel approximate analytical model to evaluate the performance of active virtual machines in IaaS clouds using an M/G/m/m+K queue. The proposed model, combined with the transform-based analytical approach, enables the computation of the probability distribution of the number of jobs in the system and subsequently a set of performance measures, including the mean number of jobs in the system, the mean response time, the probability of immediate service, and the blocking probability. Compared to the existing Markov models of cloud data centers, our approach can reflect the system behavior more accurately even when the service-time distribution has a large coefficient of variation (>1.5) in a medium-sized IaaS cloud. Numerical results obtained from the proposed analytical model are verified through extensive simulations under various system parameter settings, and compared with the results from existing models.
ISSN 1939-1374
Language English
Format Article
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