ENERGY-SAVING CLOUD WORKFLOW SCHEDULING BASED ON OPTIMISTIC COST TABLE

In recent years, intelligent flow sensors have been applied to many fields. Cloud operation is a design method to further improve the intelligence of such sensors. However, the cloud workflows of intelligent flow sensors consume too much energy, making it imperative to schedule cloud workflows. With the growing awareness of energy conservation, it is a hot topic to design an energy-efficient workflow scheduling algorithm. Therefore, this paper puts forward the predict minimum energy consumption (PMEC) algorithm, a cloud workflow scheduling algorithm that strikes a balance between energy consumption and execution time. Firstly, the optimistic cost table (OCT) was adopted to rank the tasks by priority. Then, the resources, i.e. virtual machines, were assigned statically to the tasks, in the light of task priority and energy consumption. After that, the workflow was scheduled according to the assignments. Simulation results show that the PMEC is much more energy efficient than traditional list-based scheduling algorithms.