Hybrid metaheuristic optimization for energy efficient task scheduling in cloud computing environments

Cloud computing has emerged as a cornerstone technology for delivering scalable and on-demand computing resources. Efficient task scheduling in cloud environments is pivotal for optimizing resource utilization and enhancing overall system performance. This paper explores the application of the Golden Search Optimization with the Whale Optimization Algorithm (WOA), called GSWOA-H, to address the complex task-scheduling problem in cloud computing. The GSWOA-H, inspired by whale hunting behavior, offers a nature-inspired approach to global optimization. To validate our proposed method and configure an experimental setup of related performance evaluation metrics. The findings of the experiments are then presented, analyzed, and compared with those carried out by existing algorithms in terms of convergence speed and efficiency with regards to tasks scheduling using GSWOA. Compared with GA, GSO, WOA and QBCSSA, the resource utilization of GSWOA-H was improved by 42.71%, 34.64%, 18.55% and 12.65% separately and reduces make-span by 14.17%, 9.95%, 4.39% and 2.74% separately.