A Novel Energy-Efficient Load Balancing Algorithm Using Predictive Analytics and Real-Time Workload Adaptation

Abstract: In modern distributed and cloud computing environments, achieving optimal load distribution while minimizing energy consumption remains a significant challenge. Traditional load balancing algorithms primarily focus on performance metrics such as response time and throughput, often neglecting energy efficiency and dynamic workload variations. This paper proposes a novel energy-efficient load balancing algorithm that integrates predictive analytics and real-time workload adaptation to enhance resource utilization and system sustainability. The proposed approach employs machine learning models to forecast incoming workloads and make proactive load distribution decisions based on energy metrics, including power consumption, CPU utilization, and task execution time. By continuously analyzing system behavior and adjusting task allocation dynamically, the algorithm ensures balanced workloads, reduced energy consumption, and improved overall system performance. Experimental evaluations demonstrate that the proposed method outperforms conventional algorithms in terms of energy efficiency, scalability, and adaptability, thereby contributing to the development of sustainable and intelligent resource management frameworks for next-generation computing environments.

Load Balancing, Energy Efficiency, Predictive Analytics, Real-Time Workload Adaptation, Machine Learning, Resource Optimization, Cloud Computing, Sustainable Computing, Dynamic Resource Allocation

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