Section: New Results

Workflow management on Cloud environment

Participants : Daniel Balouek-Thomert, Eddy Caron, Laurent Lefevre.

Multi-objective workflow placements in Clouds

The recent rapid expansion of Cloud computing facilities triggers an attendant challenge to facility providers and users for methods for optimal placement of workflows on distributed resources, under the often-contradictory impulses of minimizing makespan, energy consumption, and other metrics. Evolutionary Optimization techniques that from theoretical principles are guaranteed to provide globally optimum solutions, are among the most powerful tools to achieve such optimal placements. Multi-Objective Evolutionary algorithms by design work upon contradictory objectives, gradually evolving across generations towards a converged Pareto front representing optimal decision variables – in this case the mapping of tasks to resources on clusters. However the computation time taken by such algorithms for convergence makes them prohibitive for real time placements because of the adverse impact on makespan. In [11], we described parallelization, on the same cluster, of a Multi-objective Differential Evolution method (NSDE-2) for optimization of workflow placement, and the attendant speedups that bring the implicit accuracy of the method into the realm of practical utility. We did experimental validation on a reallife testbed using diverse Cloud traces. The solutions under different scheduling policies demonstrate significant reduction in energy consumption with some improvement in makespan. We designed, implementation and evaluation of an energy-efficient resource management system that builds upon Diet , an open source middleware and NSDE-divisible tasks with precedence constraints. Real-life experiment of this approach on the Grid’5000 testbed demonstrates its effectiveness in a dynamic environment.