关键词: 云计算, 工作流调度, 能效, 任务分配, 资源合并

Abstract: The high energy consumption brought by executing workflow tasks not only increases the economic cost of cloud resource providers, but reduces the realibility of cloud system. In order to meet the deadline and reduce the energy consumption of executing workflow, an energy-efficient workflow scheduling algorithm CWEES is presented. CWEES divides the energy-efficient optimization scheduling into three stages：the initial tasks mapping, the processors resource merging and the tasks slacking. The initial tasks mapping aims to get the intial tasks scheduling orders by using the down-up leveling ordering. The processors resource merging aims to reduce the number of used resources by reclaiming the slack time and merging the relatively inefficient processors. The tasks slacking aims 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. Simulation experiments are constructed to evaluate CWEES’s performance by the random workflow tasks model. The results show that CWEES not only can obtain higher resource utilization, but can reduce the energy consumption of executing workflow, which could achieve the better trade-off between the execution efficiency and the energy consumption.

Key words: cloud computing, workflow scheduling, energy efficiency, task allocation, resource merging