A genetic algorithm for multi-objective optimisation in workflow scheduling with hard constraints

Authors:
Sonia Yassa;Jérémie Sublime;Rachid Chelouah;Hubert Kadima;Geun-Sik Jo;Bertrand Granado
Affiliations:
University of Cergy-Pontoise, L@ris Laboratory, EISTI Engineering School, Avenue du Parc, 95000 Cergy, France;University of Cergy-Pontoise, L@ris Laboratory, EISTI Engineering School, Avenue du Parc, 95000 Cergy, France;University of Cergy-Pontoise, L@ris Laboratory, EISTI Engineering School, Avenue du Parc, 95000 Cergy, France;University of Cergy-Pontoise, L@ris Laboratory, EISTI Engineering School, Avenue du Parc, 95000 Cergy, France;University of Cergy-Pontoise, L@ris Laboratory, EISTI Engineering School, Avenue du Parc, 95000 Cergy, France;University of Cergy-Pontoise, L@ris Laboratory, EISTI Engineering School, Avenue du Parc, 95000 Cergy, France
Venue:
International Journal of Metaheuristics
Year:
2013

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Abstract

Cloud computing is a fast growing technology allowing companies to use on-demand computation, and data services for their everyday needs. The main contribution of this work is to propose a new model of genetic algorithm for the workflow scheduling problem. The algorithm must be capable of: 1 dealing with the multi-objective problem of optimising several quality of service QoS variables, namely: computation time, cost, reliability or security; 2 handling a large number of workflow scheduling aspects such as adding constraints on QoS variables deadlines and budgets; 3 handling hard constraints such as restrictions on task scheduling that the previous algorithms have not addressed. Using data from Amazon elastic compute cloud EC2 and workflows from the London e-Science Centre; we have compared our algorithm with other scheduling algorithms. Simulation results indicate the efficiency of the proposed metaheuristic both in terms of solution quality and computational time.