<p>Data centers are increasingly consuming more energy due to the rapid growth in demand for cloud computing solutions. The concept of "green cloud computing" aims to curb the escalating energy consumption of these data centers. By employing service aggregation techniques, cloud computing systems can effectively reduce their energy usage. However, current aggregation methods often lead to issues such as unnecessary migrations, host load imbalance, and inadequate consideration of service communication, which can negatively impact service quality and energy efficiency. This study proposes a methodology to address these challenges by prioritizing the migration of critical services from hosts experiencing excessive or insufficient workloads, thereby reducing energy consumption. Factors such as service hierarchy and communication costs are taken into account during the migration process. This approach optimizes existing data center resources and enhances energy efficiency. The primary objectives include minimizing service migration to improve response times and system efficiency. Through live service migration prioritization, the proposed method achieves significant reductions: a 10.41% decrease in cloud data center energy usage, a 12.28% reduction in service migrations, and a 1.52% increase in host shutdowns compared to conventional methods. This research contributes to advancing sustainable practices in cloud computing, demonstrating substantial improvements in energy conservation and operational efficiency within data center environments.</p>