Scientific workflows arc being used today in a number of areas. As they grow in complexity and importance, cloud computing emerges as an important execution environment. In this scenario, scheduling the workflow tasks and data on the cloud ensuring proper use of the computational resources is one of the key issues in the management of workflow execution. Although many workflow schedulers have been proposed in the literature, few of them deal with heterogeneous computing resources and data file assignment. The Hybrid Evolutionary Algorithm to Task Scheduling and Data File Assignment Problem (HEA-TaSDAP) addresses these two problems simultaneously, but the scheduling is time consuming, especially if we consider large scale workflows. In this work, we propose optimizations on HEA-TaSDAP by taking advantage of the massive parallelism provided by GPUs, leveraging the scheduling of larger instances in a reasonable amount of time. Our parallel solution provided about 98.83% of reductions in the scheduling time, keeping the quality of the solutions.
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