Accelerated Genetic Algorithm with Population Control for Energy-Aware Virtual Machine Placement in Data Centers

Zhe Ding, Yu-Chu Tian, Maolin Tang, You-Gan Wang, Zu-Guo Yu, Jiong Jin, Weizhe Zhang

Abstract: Energy efficiency is crucial for the operation and management of cloud data centers, which are the foundation of cloud computing. Virtual machine (VM) placement plays a vital role in improving energy efficiency in data centers. The genetic algorithm (GA) has been extensively studied for solving the VM placement problem due to its ability to provide high-quality solutions. However, GA’s high computational demands limit further improvement in energy efficiency, where a fast and lightweight solution is required. This paper presents an adaptive population control scheme that enhances gene diversity through population control, adaptive mutation rate, and accelerated termination. Experimental results show that our scheme achieves a 17% faster acceleration and 49% fewer generations compared to the standard GA for energy-efficient VM placement in large-scale data centers.