A genetic algorithm for multi-objective optimisation in workflow scheduling with hard constraints Online publication date: Sat, 12-Jul-2014
by Sonia Yassa; Jérémie Sublime; Rachid Chelouah; Hubert Kadima; Geun-Sik Jo; Bertrand Granado
International Journal of Metaheuristics (IJMHEUR), Vol. 2, No. 4, 2013
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.
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