Optimizing Cloud Resource Management Through Energy-Efficient VM Placement Techniques

Abstract

Cloud computing has seen rapid growth in recent years, leading to a surge in demand for data center services. To meet this demand, data centers deploy a large number of servers, resulting in substantial energy consumption. Virtual Machine Consolidation (VMC) is an effective strategy to reduce energy usage by shutting down underutilized servers while ensuring that Service Level Agreements (SLAs) are maintained. The VMC process comprises four key steps: detecting overloaded hosts, identifying underloaded hosts, selecting virtual machines (VMs), and determining their placement. This research presents the Energy-Efficient Virtual Machine Placement (EEVMP) approach, which aims to optimize resource utilization by selecting suitable destination hosts for migrating VMs based on utilization and resource skewness. The proposed method is evaluated using the CloudSim simulator. Experimental results show that EEVMP consistently outperforms existing placement strategies such as PABFD, IQRMC, PEBFD, ESVMP, and HVMAP in terms of energy efficiency and overall performance.