Efficient Task Scheduling for Cloud Environments with MSA: Enhancing Makespan and Resource Utilization

People still want the fastest and most effective answers, even as technology improves. Since the advent of cloud computing and its multiple substructures, many algorithms have been proposed to improve the presentation of each unit or structure. The massive cloud can solve any user-supplied task. Therefore, new methods are proposed to improve the process and reduce evaluation time. The task scheduler allocates jobs to virtual machines to maximize efficiency and minimize execution time in cloud computing. This paper introduces the Mantis Search Algorithm (MSA) as a scheduling solution and tests and compares it to other algorithms based on efficiency, makespan, and cost limits. Sexual cannibalism, prey pursuit, and prey assault comprised this optimization process. A multiobjective fitness purpose is created in the CC stage by reducing makespan and optimising resource utilization. There are several ways to test the strategy and evaluate the results. Simulations showed that the MSA technique outperformed alternatives in numerous situations.