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http://dx.doi.org/10.3837/tiis.2015.12.012

Profit-Maximizing Virtual Machine Provisioning Based on Workload Prediction in Computing Cloud  

Li, Qing (State Key Laboratory on ISN, School of Telecommunications Engineering, Xidian University)
Yang, Qinghai (State Key Laboratory on ISN, School of Telecommunications Engineering, Xidian University)
He, Qingsu (State Grid Information and Telecommunication Group)
Kwak, Kyung Sup (Graduate School of Information Technology and Telecommunications, Inha University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.9, no.12, 2015 , pp. 4950-4966 More about this Journal
Abstract
Cloud providers now face the problem of estimating the amount of computing resources required to satisfy a future workload. In this paper, a virtual machine provisioning (VMP) mechanism is designed to adapt workload fluctuation. The arrival rate of forthcoming jobs is predicted for acquiring the proper service rate by adopting an exponential smoothing (ES) method. The proper service rate is estimated to guarantee the service level agreement (SLA) constraints by using a diffusion approximation statistical model. The VMP problem is formulated as a facility location problem. Furthermore, it is characterized as the maximization of submodular function subject to the matroid constraints. A greedy-based VMP algorithm is designed to obtain the optimal virtual machine provision pattern. Simulation results illustrate that the proposed mechanism could increase the average profit efficiently without incurring significant quality of service (QoS) violations.
Keywords
Cloud computing; virtual machine provision; service level agreement; workload prediction; profit maximization;
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