VM queuing optimal scheduling in cloud using heuristic ant colony optimal based multi-objective genetic approach

Abstract

The usages of cloud based applications are increasing tremendously. The cloud computing task distribution is an unknown polynomial time issue that is challengeable to find the optimal solution. In solve above mentioned issue with large amount user’s job requests, heuristic ant colony optimal based multi-objective genetic (HACOMOG) approach based job allocation and resource optimization is proposed. Utilization basis scheduler recognizes the task order and optimal resources to be scheduled. The primary contribution of the proposed technique is to develop several online techniques to find solution for the virtual machines (VM) Packing problem sharing-aware and for performing a comprehensive number of studies in order to assess their efficiency with online sharing algorithms. The proposed algorithm considers the utilization basis scheduler output and identified the optimzed task allocation technique based on job execution time, MakeSpan and throughput. The experimental outcomes show that the proposed HACOMOG Algorithm reduces 0.70 seconds job execution time (JET), 0.13 MakeSpan and improve 1.98 throughput on given parameters for 100, 200, and 500 tasks with conventional methodologies.