Hybrid Henry Gas-Harris Hawks Comprehensive-Opposition Algorithm for Task Scheduling in Cloud Computing

Users can use online data computing services and computational resources from a distance in cloud computing environments. Task scheduling is a crucial part of cloud computing since it necessitates the creation of dependable and effective techniques for allocating tasks to resources. To achieve optimal performance, it requires accurate task allocation to resources. By optimizing task scheduling, cloud computing solutions can decrease processing times, boost efficiency, and improve overall system performance. To address these challenges, this paper proposes an improved version of Henry gas solubility optimization, which is presented as the Henry Gas-Harris Hawks-Comprehensive Opposition (HGHHC) method. This method is based on two elements: comprehensive opposition-based learning (COBL) and Harris Hawks Optimization (HHO). The HHO algorithm was employed as a local search strategy in this suggested algorithm to improve the quality of authorized solutions. Through meticulous analysis of their opposites and selecting an efficient option, COBL improves the less effective options. This method made it easier to improve insufficient solutions, which increased the overall effectiveness of the chosen strategies. The suggested technique was tested using CloudSim on the NASA, HPC2N, and Synthetic datasets. For makespan (MKS), it achieved performance of 34.30, 72.95, and 28.67, respectively. Regarding resource utilization (RU), the corresponding values were 16.92, 28.72, and 25.58. Therefore, the simulated makespan and resource usage of the proposed HGHHC algorithm were better than those of previous approaches. This highlights the effectiveness of hybrid meta-heuristic algorithms in achieving a balance between exploration and exploitation, preventing them from getting stuck in local optima.