Bee optimization based random double adaptive whale optimization model for task scheduling in cloud computing environment

Abstract

Cloud computing is the computing technology that offers dynamically scalable and flexible computing resources. Task scheduling in the cloud system is the major problem that needs to be tackled for enhancing the system performance and cloud customer satisfaction level. The task scheduling scheme directly affects the execution time as well as the execution cost of the system. To overcome the above-stated issue, the novel hybrid Whale optimization algorithm-based MBA algorithm is proposed for solving the multi-objective task scheduling problems in cloud computing environments. In the hybrid WOA based MBA algorithm, the multi-objective behavior decreases the makespan by maximizing the resource utilization. The output of the Random double adaptive whale optimization algorithm (RDWOA) is enhanced by utilizing the mutation operator of the Bees algorithm. The performance evaluation is conducted and compared with other algorithms using the platform of Cloudsim tool kit for various measures such as completion, time, and computational cost. The results are analyzed for the performance measures such as makespan, execution time, resource utilization and computational cost and the analysis proves that the proposed algorithm performs better than other algorithms such as IWC, MALO, BA-ABC and MGGS. The proposed HWOA based MBA algorithm converged faster than any other approach for large search spaces and makes it appropriate for large scheduling problems. The experimental results reveal that the HWOA based MBA algorithm effectively minimizes the task completion time and also execution time.