Research on resource-aware and energy-efficient virtual machine deployment in cloud datacenters

Aiming at the collaborative problem of resource load balancer and energy consumption optimization in cloud data center virtualization deployment, an efficient method is proposed to achieve a balance between these two objectives. Specifically, a load balancing model that integrates the multi-dimensional resource utilization balance is constructed, along with an energy consumption model that reflects energy loss under different system operating states. Based on these, a bi-objective optimization function with resource constraints is formulated. An improved hybrid Sparrow search algorithm (HSSA) is then designed to minimize this optimization function. Experimental results on a cloud simulation platform show that, in a heterogeneous cloud environment with 200 nodes, the proposed method improves performance by 45.28% compared to the Ant Colony System (ACS), and by 58.06% and 8.38% compared to the First Fit Decreasing (FFD) algorithm and Genetic Algorithm (GA), respectively.