Towards a novel NSGA-II-based approach for multi-objective scientific workflow scheduling on hybrid clouds

Abstract

In the era of the big data and e-science revolution, the execution of such applications known as High Performance Computing (HPC) is becoming a challenging issue. In order to face these challenges, a new promising Large Scale Distributed Systems (LSDS) has emerged suchlike Grid and Cloud Computing. As a matter of fact, these HPC applications are commonly arranged as a form of interdependent tasks named workflows. Nevertheless, the new challenging topic is that the scheduling of these scientific workflows in the LSDS is a well-known NP-hard problem. The goal of this work is to design an Non-dominated Sorting Genetic Algorithm Version II (NSGA-II)-based approach for optimizing a multi objective scheduling of scientific workflows in hybrid distributed systems. This paper work deals with the proposition of two execution models: i) A Cumulative one aiming to improve the Pareto front quality in term of Makespan-Cost trade-off; ii) An Incremental execution fashion, what kind of Cost-driven approach leading to a solution diversity of the Pareto front in the objective space. Experiments conducted with multiple common scientific workflows point out significant improvement against the classic NSGA-II algorithm.