Extreme Solutions NSGA-III (E-NSGA-III) for Scientific Workflow Scheduling on Cloud

Abstract

The execution of scientific workflows on dynamic environments such as cloud computing has become multi-objective scheduling in order to satisfy user demands from several perspectives. Among these objectives, Cost and Makespan are probably the most common. This research also includes Data Movement as an additional objective as it has significant effect to network utilization and energy consumption in network equipment in cloud data center. This paper proposes a multi-objective scheduling, Extreme Nondominated Sorting Genetic Algorithm (E-NSGA-III). It is an extension of the Nondominated Sorting Genetic Algorithm (NSGA-III). E-NSGA-III utilizes extreme solutions in the population generation module in order improve quality of solutions. Five well-known scientific workflows are selected as testbeds. Hypervolume and the Pareto front are chosen as the performance metrics. E-NSGA-III is evaluated by comparing its performance against the two previous versions (NSGA-II and NSGA-III). The comparison reveals that E-NSGA-III yields the best performance among them in multi-objective scheduling of the five scientific workflows.