大数据流式计算环境下的阈值调控节能策略

摘要

在大数据实时分析计算领域,流式计算的重要性不断提高,但是流式计算平台处理数据的能耗不断上升.针对这一问题,改变流式计算中节点对数据的处理方式,提出了一种阈值调控节能策略(ESTC).首先,根据系统负载差异确定工作节点的阈值情况;其次,通过工作节点的阈值对系统数据流进行随机选择,确定不同数据处理情况调节系统的物理电压;最后,根据不同的物理电压确定系统功率.实验结果和理论分析表明,在20台普通PC机构成的流式计算集群中,实施ESTC的系统比原系统有效节能约35.2％;此外,ESTC下的性能与能耗的比值为0.080 3 tuple/(s·J),而原系统性能与能耗的比值为0.069 8 tuple/(s·J).ESTC能够在不影响系统性能的前提下,有效降低了能耗.%In the field of big data real-time analysis and computing,the importance of stream computing is constantly improved while the energy consumption of dealing with data on stream computing platform rises constantly.In order to solve the problems,an Energy-efficient Strategy for Threshold Control (ESTC) was proposed by changing the processing mode of node to data in stream computing.First of all,according to system load difference,the threshold of the work node was determined.Secondly,according to the threshold of the work node,the system data stream was randomly selected to determine the physical voltage of the adjustment system in different data processing situation.Finally,the system power was determined according to the different physical voltage.The experimental results and theoretical analysis show that in stream computing cluster consisting of 20 normal PCs,the system based on ESTC saves about 35.2％ more energy than the original system.In addition,the ratio of performance and energy consumption under ESTC is 0.080 3 tuple/(s · J),while the original system is 0.0698 tuple/(s · J).Therefore,the proposed ESTC can effectively reduce the energy consumption without affecting the system performance.