Abstract: Cloud provides a high-efficient and reliable execution environment for scheduling large-scale workflow.However,the high energy consumption resulted by workflow execution not only increases the economic cost of cloud resource providers,but influences the system reliability and has a negative effect to the environment.For meeting user-defined deadline QoS requirement and reducing the execution consumption of workflow scheduling in cloud,a workflow energy-efficient scheduling algorithm QCWES was proposed.QCWES divides the energy-efficient scheduling scheme of workflow into three phases:the deadline redistribution,the ordering of scheduled tasks and the best resource selection based on DVFS.The deadline redistribution phase is to redistribute the user-defined overall workflow deadline among all tasks,the ordering of scheduled tasks is to obtain the scheduling order of tasks by top-down task leveling,the best resource selection based on DVFS is to select the best available resource with appropriate voltage/frequency level for each task so that the total energy consumption is minimal while meeting its sub-deadline.Some simulation experiments were constructed to evaluate the performance of our algorithm by random workflow and the real-world workflow based on Gaussian Elimination.The results show that QCWES can reduce the energy consumption of workflow scheduling under meeting deadline constraint,and achieve the trade-off between users’ QoS requirement and resources’ energy consumption.

Key words: Cloud computing, Dynamic voltage/frequency scaling, Enegy-efficient scheduling, QoS constraint, Workflow scheduling