Simulated annealing based VM placement strategy to maximize the profit for Cloud Service Providers

Abstract

Virtual machine (VM) placement strategies reported in the literature focuses mainly on minimization of power consumption and maximization of placed VMs. The revenue earned by a cloud service provider (CSP) depends on the number of VMs placed. Increasing the number of VMs placed by a CSP not only increases the power consumption but also decreases the profit margin of the CSP. In this paper, we propose a technique called maximum VM placement with minimum power consumption (MVMP) to maximize the profit earned by a CSP. The proposed technique attempts to maximize the revenue and minimize the power budget. It is formulated as a bi-objective optimization problem, and is solved using simulated annealing (SA) technique. To reach a sub-optimal solution more randomness is applied to SA. Our MVMP algorithm is compared to five state of the art algorithms in the realm of strategic VM placement, namely Marotta and Avallone (MA) approach, Hybrid genetic algorithm (HGA), Modified Best-Fit decreasing (MBFD), First-Fit decreasing (FFD) and Random deployment. We observe that MVMP performs better than Marotta and Avallone (MA) approach, HGA, MBFD, FFD and Random placement in terms of number of servers used, energy consumption, profit and execution time. Scalability of MVMP is verified using two different scenarios: (i) fixed number of VMs and, (ii) fixed number of servers. It is observed that MVMP is scalable too.

Keywords

• Cloud computing
• Data center
• Virtual machine
• Energy efficiency
• Revenue
• Simulated annealing