Metaheuristic coevolution workflow scheduling in cloud environment

Today technological progress makes scientific community to challenge more and more complex issues related to computational organization in distributed heterogeneous environments, which usually include cloud computing systems, grids, clusters, PCs and even mobile phones. In such environments, traditionally, one of the most frequently used mechanisms of computational organization is the Workflow approach. Taking into account new technological advantages, such as resources virtualization, we propose new coevolution approaches for workflow scheduling problem. The approach is based on metaheuristic coevolution that evolves several diverse populations that influence each other with final positive effect. Besides traditional population, that optimizes tasks execution order and task's map to the computational resources, additional populations are used to change computational environment to gain more efficient optimization. As a result, proposed scheduling algorithm optimizes both computation tasks to computation environment and computation environment to computation tasks, making final execution process more efficient than traditional approaches can provide.