移动云计算环境下多工作流任务调度的联合优化方法  

Joint optimization method for multi-workflow task scheduling in mobile cloud computing environment

作　　者：宋祖尧 戴月明[1] Song Zuyao;Dai Yueming(School of Internet of Things Engineering,Jiangnan University,Wuxi Jiangsu 214122,China)

机构地区：[1]江南大学物联网工程学院,江苏无锡214122

出　　处：《计算机应用研究》2020年第8期2261-2265,共5页Application Research of Computers

基　　金：国家自然科学基金资助项目(61572238)。

摘　　要：传统移动云计算环境下的任务调度通过random算法来决定任务执行位置,通过动态电压调节技术来调节工作频率,通过任务间的差异性判别进行任务的整合,这往往带来了很多不合理的任务迁移,并导致CPU负载严重,造成了系统损害和大量能耗。针对多工作流任务提出了CCS算法,它包括consolidation算法与多任务并发算法,通过增加任务之间传输与执行的并发性,增加任务集整合的概率,提高任务的处理速率,减少任务的响应时间,增加CPU使用率的同时将主机和内核CPU使用率控制在阈值上限以下,避免CPU过载并根据多任务并发来优化local算法,调整任务执行位置,提高迁移效率的同时也避免了随机算法的局限性。实验结果表明该算法可以有效地提高系统性能,避免CPU过载问题,并且优化了能耗和工作流的完成时间。In traditional mobile cloud computing environment,task scheduling is often determined by random algorithm,adjusting working frequency by dynamic voltage regulation technology.Differentiating tasks exist in task integration,which often leads to many unreasonable task migration and heavy CPU load,and resulting in system damage and a large amount of energy consumption.For multi-workflow tasks,this paper proposed CCS algorithm,which included consolidation algorithm and multitask concurrency algorithm.By increasing the concurrency of transmission and execution between tasks,it could increase the probability of task set integration,could increase the rate of task processing,could reduce the response time of tasks,could increase the CPU usage,and control the CPU usage of host and kernel under the upper threshold.It could avoid CPU overload and optimize local algorithm according to multi-task concurrency,which adjusted task execution location,improved migration efficiency and avoided the limitations of random algorithm.Experimental results show that this algorithm can effectively improve system performance,avoid CPU overload problem,optimize energy consumption and workflow completion time.

关 键 词：移动云计算 任务整合 多任务并发 CPU阈值 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]