MULTI-OBJECTIVE-OPTIMIZATION APPROACH FOR OPTIMAL TASK SCHEDULING THROUGH IN DELAY SENSITIVE CLOUD ENVIRONMENT

This work introduces a dynamic load-balancing algorithm called CHHO (Cuckoo Harris Hawks multiobjective optimization) applied to task scheduling in cloud environment. CHHO is a new hybrid method that combines Cuckoo Search Optimization (CSO) and Harris Hawks Optimization (HHO). This combination uses the strengths of both algorithms to address the complex issues of cloud task scheduling. Specifically, CHHO uses Cuckoo Search Optimization to widen the search area of Harris Hawks Optimization, aiming to improve factors such as cost, response time, and resource use. The CHHO algorithm improves system performance by increasing VM throughput, effectively distributing workloads across VMs and maintaining a balance among task priorities through dynamic adjustments in task waiting times. To test the performance of CHHO, the algorithm is implemented in the CloudSim environment. It is compared with existing load-balancing algorithms on various performance measures. Our simulation results clearly show that CHHO performs better than existing algorithms, providing a strong and efficient solution for load balancing in cloud computing. Introducing CHHO offers a significant advancement in the field, providing a dynamic and adaptable approach that improves cloud task scheduling and enhances the overall efficiency and effectiveness of cloud computing systems.