Abstract
Cloud computing is a new trend of information and communication technology that enables resource distribution and sharing at a large scale. The Cloud consists of a collection of virtual machine that promise to provision on-demand computational and storage resources when needed. End-users can access these resources via the Internet and have to pay only for their usage. Scheduling of scientific workflow applications on the Cloud is a challenging problem that has been the focus of many researchers for many years. In this work, we propose a novel algorithm for workflow scheduling that is derived from the Opposition-based Differential Evolution method. This algorithm does not only ensure fast convergence but it also averts getting trapped into local extrema. Our CloudSim-based simulations show that our algorithm is superior to its predecessors. Moreover, the deviation of its solution from the optimal one is negligible.
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