QoS约束云环境下的工作流能效调度算法

Workflow Energy-efficient Scheduling Algorithm in Cloud Environment with QoS Constraint

云环境可以为大规模工作流的执行提供高效、可靠的运行环境,但工作流执行时带来的高能耗不仅会增加云资源提供方的经济成本,还会影响云系统的可靠性,并对环境产生不利影响。为了在满足用户截止时间QoS需求的同时降低云环境中工作流调度的执行能耗,提出一种工作流能效调度算法QCWES。该算法将工作流的能效调度方案求解划分为3个阶段:截止时间重分配、任务调度选择排序以及基于DVFS的最佳资源选择。截止时间重分配阶段旨在将用户定义的全局工作流截止时间在各个任务间进行重分配,任务调度选择排序阶段旨在通过自顶向下的任务分级方式得到任务调度序列;基于DVFS的最佳资源选择阶段旨在为每个任务选择带有合适电压/频率等级的最优目标资源,在满足任务的子截止时间的前提下使总体能耗达到最小。通过随机工作流和基于高斯消元法的现实工作流结构,对算法的性能进行仿真实验分析。结果表明,所提算法可以在满足截止时间约束下降低工作流的执行能耗,实现用户方的QoS需求与资源方的能耗间的均衡。

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.