Efficient resource allocation is crucial for cloud data centers performance, scalability, and cost effectiveness. Traditional load balancing systems frequently fail to adjust changing workloads resulting in poor resource use, increased latency and excessive energy consumption. Adaptive Load Balancing Algorithm that optimizes resource allocation in cloud systems is implemented. It combines heuristic and metaheuristic techniques to efficiently distribute workloads among virtual machines resulting in lower response times and balanced computing loads. Centralised Control System monitors real time system data and dynamically reallocates resources in response to workload needs. Performance evaluation shows suggested solution reduces delay by 35 percentage from 120 ms to 85 ms and reduces energy consumption by 30 percent from 200W to 140W. This increases efficiency of resources from 65 to 98 percent. Suggested adaptive technique improves distribution of loads, energy usage and level of service in cloud computing settings. It provides fault tolerance by dynamically shifting jobs during server failures, reducing downtime and increasing system dependability. Comparative research with existing load balancing approaches confirms its advantages in managing large scale dynamic workloads demonstrating suitability for implementation in cloud data centers.
