Scheduling Parallel Jobs with Deadline and Makespan Constraints using Multi-Objective Monarch Butterfly Algorithm

摘要

To minimize the scheduling issues in parallel computing, we describes the scheduling of independent tasks using makespan with deadline as a scheduling importance. Parallel computing facilities to scheduling on different nodes becomes an important concerns in the ongoing years. In multi-objective heterogeneous computing nodes enhances large amount of scheduling and resource allocation to optimize performance and application tasks are factor that make the scheduling on NP Hard problem. For high scheduling of parallel jobs in grid environment we used metaheuristics to find near optimal solution. In this paper, we study scheduling of parallel jobs and concentrate on high-performance workload and optimizes the parameters such as for instance deadline, makespan and flowtime with the utilization of multi-objective monarch butterfly optimization algorithm (MOMBOA) predicated on multi-objective genetic algorithm (MOGA). Our aim should be to idle for high scheduling of parallel jobs in heterogeneous environment. The end result show the potency of the proposed method MOMBO providing solutions that enhance the performance, makespan and deadline regarding other well-known techniques in the literature.