M-dimension hybrid algorithm for scientific workflow in cloud computing

Abstract

Cloud computing is emerging with growing popularity in workflow scheduling, especially for scientific workflow. With the emergence cloud computing, can benefit from virtually unlimited resources with minimal hardware investment. Scheduling the submitted Scientific Workflow Application (SWFA) tasks to the available computational resources while optimizing the cost of executing the SWFA is one of the most challenging processes of Workflow Management System (WfMS) in a cloud computing environment. Several cost optimization approaches have been proposed to improve the economic aspect of SWFS in cloud computing. The main goal of the paper is to present a new M-dimension hybrid algorithm, which uses a meta-heuristic algorithm such as Completion Time Driven Hyper-Heuristic (CTDHH), Hybrid Cost-effective Hybrid-Scheduling (HCHS), particle swarm optimization (PSO) and genetic algorithm (GA) and using heuristic algorithms such as the IC-PCPD2 and IC-Loss algorithms. Based on the results of the experimental comparison, the proposed method has proven to yield the most effective performance results for all considered experimental scenarios.