Abstract
Cloud computing is a virtualized compute power and storage delivered via platform-agnostic infrastructures of abstracted hardware and software accessed over the Internet. Workflow is adopted as an attractive paradigm for its powerful ability in expressing a wide range of applications, including scientific computing, multi-tier Web, and big data processing applications. Despite it has been the focus of many researchers, a handful efficient solutions have been proposed for Cloud computing. In this work, we propose a novel algorithm for workflow scheduling that is derived from the Opposition-based Differential Evolution method, MODE. This algorithm not only ensures fast convergence but also averts getting trapped in local extrema. Our simulation experiments CloudSim show that MODE is superior to its predecessors. Moreover, the deviation of its solution from the optimal one is negligible.
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