A Fast and Elitist Bi-objective Evolutionary Algorithm for Scheduling Independent Tasks on Heterogeneous Systems

To meet the increasing computational demands, geographically distributed resources need to be logically coupled to make them work as a unified resource. In analyzing the performance of such distributed heterogeneous computing systems scheduling a set of tasks to the available set of resources for execution is highly important. Task scheduling being an NP-complete problem, use of metaheuristics is more appropriate in obtaining optimal solutions. Schedules thus obtained can be evaluated using several criteria that may conflict with one another which require multi objective problem formulation. This paper investigates the application of an elitist Nondominated Sorting Genetic Algorithm (NSGA-II), to efficiently schedule a set of independent tasks in a heterogeneous distributed computing system. The objectives considered in this paper include minimizing makespan and average flowtime simultaneously. The implementation of NSGA-II algorithm and Weighted-Sum Genetic Algorithm (WSGA) has been tested on benchmark instances for distributed heterogeneous systems. As NSGA-II generates a set of Pareto optimal solutions, to verify the effectiveness of NSGA-II over WSGA a fuzzy based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set.