Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Cloud computing has taken over the high-performance distributed computing area, and it currently provides on-demand services and resource polling over the web. As a result of constantly changing user service demand, the task scheduling problem has emerged as a critical analytical topic in cloud computing. The primary goal of scheduling tasks is to distribute tasks to avail-able processors to construct the shortest possible schedule without breaching precedence restrictions. Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system. The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system. As a result, an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan. This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem. The basic idea of this method is to use the advantages of meta-heuristic algorithms to get the optimal solution. We assess our algorithm's performance by run-ning it through three scenarios with varying numbers of tasks. The findings demonstrate that the suggested technique beats existing methods New Genetic Algorithm (NGA), Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), Gravitational Search Algorithm (GSA), and Hybrid Heuristic and Genetic (HHG) by 7.9%, 2.1%, 8.8%, 7.7%, 3.4% respectively according to makespan.