Energy-aware framework with Markov chain-based parallel simulated annealing algorithm for dynamic management of virtual machines in cloud data centers

Significant savings in the energy consumption, without sacrificing service level agreement (SLA), are an excellent economic incentive for cloud providers. By applying efficient virtual Machine placement and consolidation algorithms, they are able to achieve these goals. In this paper, we propose a comprehensive technique for optimum energy consumption and SLA violation reduction. In the proposed approach, the issues of allocation and management of virtual machines are divided into smaller parts. In each part, new algorithms are proposed or existing algorithms have been improved. The proposed method performs all steps in distributed mode and acts in centralized mode only in the placement of virtual machines that require a global vision. For this purpose, the population-based or parallel simulated annealing (SA) algorithm is used in the Markov chain model for virtual machines placement policy. Simulation of algorithms in different scenarios in the CloudSim confirms better performance of the proposed comprehensive algorithm.