A Performance Analysis on Load Balancing in Cloud Computing with Hybrid Approach

An Effective Load Balancing in Cloud Computing is a complex problem and optimization techniques are employed to allocate resources intelligently, balance workloads and optimize task scheduling, enhancing the overall performance and resource utilization in cloud environments. This research introduces the Jaguar Algorithm — Improved Reptile Search Optimization model, a hybrid approach combining the strengths of two nature-inspired metaheuristic techniques. The Jaguar Algorithm is utilized for load balancing and Improved Reptile Search Optimization is employed for task scheduling, which improves the resource utilization and reduction in wastage. The proposed JA-IRSO model has superior performance for load balancing, task scheduling mechanism, and robust and consistent task execution in Cloud Computing. The proposed JA-IRSO model attains an average execution time of 16[Formula: see text]s, a makespan of 45[Formula: see text]s, 70% of CPU utilization memory usage and 99% of average throughput for minor operations. These results showcase the proposed model performance that can manage medium-size workloads while ensuring higher reliability and efficient resource allocation. Overall, the proposed JA-IRSO model has the potential to address the complex challenges and enhance efficiency, reliability and user satisfaction.