Nowadays, we live an unprecedented evolution in cloud computing technology that coincides with the development of the vast amount of complex interdependent data which make up the scientific workflows. All these circumstances developments have made the issue of workflow scheduling very important and of absolute priority to all overlapping parties as the provider and customer. For that, work must be focused on finding the best strategy for allocating workflow tasks to available computing resources. In this paper, we consider the scientific workflow scheduling in cloud computing. The main role of our model is to optimize the time needed to run a set of interdependent tasks in cloud and in turn reduces the computational cost while meeting deadline and budget. To this end, we offer a hybrid approach based on genetic algorithm for modelling and optimizing a workflow-scheduling problem in cloud computing. The heterogeneous earliest finish time (HEFT), an heuristic model, intervenes in the generation of the initial population. Based on results obtained from our simulations using real-world scientific workflow datasets, we demonstrate that the proposed approach outperforms existing HEFT and other strategies examined in this paper. In other words, experiments show high efficiency of our proposed approach, which makes it potentially applicable for cloud workflow scheduling. For this, we develop a GA-based module that was integrated to the WorkflowSim framework based on CloudSim.
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