Resource Optimization in Cloud Environment Using Advanced Metaheuristic Scheduling Algorithm

Task Scheduling (TS) especially in cloud environments is quite a challenge because the resources within cloud are dynamic while the tasks present within a cloud have diverse characteristics. This paper aims at presenting a new scheduling technique based on one of the metaheuristic algorithms called Harris Hawks Optimization (HHO), which is derived from the social instinct and predatory hunting behavior of the Harris's hawks. The HHO algorithm is intended to deal with the challenges that appear in the pattern of tasks by adjusting the important factors like makespan, energy and resource utilization. We evaluate the performance of HHO through comprehensive simulations and compare it against traditional scheduling methods, highlighting its efficacy in balancing load and minimizing latency. The results demonstrate that the HHO-based scheduling approach achieves notable improvements in scheduling efficiency, with 15% improvement in energy consumption, a 16% reduction in makespan, and an 18% increase in throughput. The work that is presented in the paper offers a comprehensive approach to effective coordination of tasks in cloud computing systems to propose a satisfactory way to address this challenge in the context of current application-driven compute-intensive applications.