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This purpose of this paper is to propose a novel hybrid genetic algorithm based on a virtual machine (VM) placement method to improve energy efficiency in cloud data centers. How to place VMs on physical machines (PMs) to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers. Over the past few years, many approaches for VM placement (VMP) have been proposed; however, existing VM placement approaches only consider energy consumption by PMs, and do not consider the energy consumption of the communication network of a data center.

This paper attempts to solve the energy consumption problem using a VM placement method in cloud data centers. Our approach uses a repairing procedure based on a best-fit decreasing heuristic to resolve violations caused by infeasible solutions that exceed the capacity of the resources during the evolution process.

In addition, by reducing the energy consumption time with the proposed technique, the number of VM migrations was reduced compared with existing techniques. Moreover, the communication network caused less service level agreement violations (SLAV).

The proposed algorithm aims to minimize energy consumption in both PMs and communication networks of data centers. Our hybrid genetic algorithm is scalable because the computation time increases nearly linearly when the number of VMs increases.