MOHEFT: A multi-objective list-based method for workflow scheduling

Nowadays, scientists and companies are confronted with multiple competing goals such as makespan in high-performance computing and economic cost in Clouds that have to be simultaneously optimized. Multi-objective scheduling of scientific workflows in distributed systems is therefore receiving increasing research attention. Most existing approaches typically aggregate all objectives in a single function, defined a-priori without any knowledge about the problem being solved, which negatively impacts the quality of the solutions. In contrast, Pareto-based approaches having as outcome a set of several (nearly-) optimal solutions that represent a tradeoff among the different objectives, have been scarcely studied. In this paper, we propose a new Pareto-based list scheduling heuristic that provides the user with a set of tradeoff optimal solutions from where the one that better suits the user requirements can be manually selected. We demonstrate the potential of MOHEFT for a bi-objective scheduling problem that optimizes makespan and economic cost in a Cloud-based computing scenario. We compare MOHEFT with two state-of-the-art approaches using different synthetic and real-world workflows: the classical HEFT algorithm used in single-objective scheduling and the SPEA2* genetic algorithm used for multi-objective optimisation problems.