Game Theory-Based Approach for Optimizing Resource Allocation in Cloud Computing *

With the rapid development of cloud services, cloud load balancing has emerged as a critical research area. Data centers face challenges related to high energy consumption and low resource utilization, making energy efficiency optimization in load balancing essential for enhancing service quality. To address latency and energy-efficient load balancing issues, this paper employs the Stackelberg game model to construct a two-level optimization framework between users and cloud controllers. Users act as leaders, adjusting their resource requests to maximize individual benefits, while cloud controllers, acting as followers, efficiently allocate user requests to optimize energy consumption and response time. The existence of Nash equilibrium is theoretically proven, and an efficient algorithm for energy-aware resource allocation is proposed through game-theoretic modeling. To validate this approach, comparative experiments were conducted with three other load balancing schemes. Experimental results demonstrate that the proposed method effectively reduces energy consumption while simultaneously lowering response times, outperforming other approaches in cost control. In dynamic workload environments, the proposed method enhances energy efficiency and meets service level agreements. The model has also been shown to increase profits for both users and cloud service providers. Overall, this model is applicable to practical cloud computing scenarios, providing an efficient and energy-aware load balancing solution.