Abstract
Nowadays, many evolutionary algorithms for workflow scheduling in cloud computing are available. Most of those algorithms focus on the effectiveness, discarding the issue of flexibility. Research on Petri nets addresses the issue of flexibility; many extensions have been proposed to facilitate the modelling of complex systems. Typical extensions are the addition of 'colour', 'time' and 'hierarchy'. By mapping scheduling problems into Petri nets, we are able to use standard Petri net theory. In this case, the scheduling problem can be reduced to finding an optimal sequence of transitions leading from an initial marking to a final one. To find the optimal scheduling, we propose a new approach based on a recently proposed formalism 'Evolutionary Petri Net' (EPN), which is an extension of Petri net, enriched with two genetic operators, crossover and mutation. The objectives of our research are to minimise the workflow application completion time (makespan) as well as the cost incurred by using cloud resources. Some numerical experiments are carried out to demonstrate the usefulness of our algorithm.
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