一种云环境下科学工作流执行计划的优化方法
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摘要
为降低云环境下科学工作流的执行代价,提出了一种执行计划的优化方法。引入猴群算法,依靠对当前执行计划的层内和层间优化,在保证工作流全局截止时间约束的前提下,通过同层任务的逻辑聚合和任务的层间调整,尽可能减少各层任务数的差异,以避免资源的闲置浪费,缩短任务的等待时间。实验表明,该方法与类似研究相比,可降低资源消耗量,减小总的延迟时间。
In order to reduce the cost of scientific workflow execution in cloud environment, we propose an approach to optimizing the execution plans of scientific workflows in cloud environment. It introduces the monkey group algorithm and relies on the intra-level and inter-level optimization of the current execution plan. Under the premise of ensuring the global deadline of the workflow, through the logical aggregation of the same-level tasks and the inter-level adjustment of the tasks, the difference in the number of tasks at each level is minimized to avoid waste of resources and reduce the waiting time of tasks. Experiments show that compared with the BTS algorithm and the SPSWVC algorithm, the proposed method can reduce resource consumption and the total delay time of tasks.
In order to reduce the cost of scientific workflow execution in cloud environment, we propose an approach to optimizing the execution plans of scientific workflows in cloud environment. It introduces the monkey group algorithm and relies on the intra-level and inter-level optimization of the current execution plan. Under the premise of ensuring the global deadline of the workflow, through the logical aggregation of the same-level tasks and the inter-level adjustment of the tasks, the difference in the number of tasks at each level is minimized to avoid waste of resources and reduce the waiting time of tasks. Experiments show that compared with the BTS algorithm and the SPSWVC algorithm, the proposed method can reduce resource consumption and the total delay time of tasks.
引文
[1] Wu Xiang, Deng Sun-gen, Lu Zhong-hua.A review of the study on the scientific workflow[J].E-Science Technology & Application,2014,5(5):86-95.(in Chinese)
[2] Rao R V,Selvamani K.Data security challenges and its solutions in cloud computing [J].Procedia Computer Science,2015,48:204-209.
[3] Jiang D,Pierre G,Chi C H.EC2 performance analysis for resource provisioning of service-oriented applications[C]//Proc of the 2009 International Conference on Service-Oriented Computing(ICSOC/ServiceWave’09),2009:197-207.
[4] Kim H,El-Khamra Y,Rodero I,et al.Autonomic management of application workflows on hybrid computing infrastructure[J].Scientific Programming,2011,19(2-3):75-89.
[5] Zuo Li-yun, Cao Zhi-bo, Dong Shou-bin. Virtual resource evaluation model based on entropy optimized and dynamic weighted in cloud computing[J].Journal of Software,2013,24(8):1937-1946.(in Chinese)
[6] Rodriguez M A,Buyya R.A responsive knapsack-based algorithm for resource provisioning and scheduling of scientific workflows in clouds[C]//Proc of IEEE International Conference on Parallel Processing,2015:839-848.
[7] He Zhi-ming, Liu Min.A fair resource allocation strategy based on preference in cloud environment[J].Journal of Computer Engineering & Science,2017,39(11):1991-1999.(in Chinese)
[8] Mao M,Humphrey M.Auto-scaling to minimize cost and meet application deadlines in cloud workflows[C]//Proc of International Conference for High Performance Computing,Networking,Storage and Analysis,2011:1-12.
[9] Duan Ju, Chen Wang-hu, Wang Run-ping,et al.Execution optimization policy of scientific workflow based on cluster aggregation under cloud environment[J].Journal of Computer Applications,2015,35(6):1580-1584.(in Chinese)
[10] Chen Xi,Mao Ying-chi, Jie Qing,et al.Related task scheduling algorithm based on task hierarchy and time constraint in cloud computing[J].Journal of Computer Applications,2014,34(11):3069-3072.(in Chinese)
[11] Oliveira D D,Viana V,Ogasawara E,et al.Dimensioning the virtual cluster for parallel scientific workflows in clouds[C]//Proc of ACM Workshop on Scientific Cloud Computing,2013:5-12.
[12] Coutinho R D C,Drummond L M A,Frota Y,et al.Optimizing virtual machine allocation for parallel scientific workflows in federated clouds[J].Future Generation Computer Systems,2015,46(C):51-68.
[13] Lee W R,Teng H Y,Hwang R H.Optimization of cloud resource subscription policy[C]//Proc of IEEE International Conference on Cloud Computing Technology and Science,2012:449-455.
[14] Zhao R,Tang W.Monkey algorithm for global numerical optimization[J].Journal of Uncertain Systems,2008,2(3):165-176.
[15] Chen Hai-tao. Resource allocation in clouding computing based on monkey algorithm[J].Journal of Computer Systems & Applications,2015,24(8):191-196.(in Chinese)
[16] Xu Xiao-ping, Zhang Dong-jie.Improved monkey algorithm[J]. Computer Systems & Applications,2017,26(6):193-197.(in Chinese)
[17] Chen Xin, Zhou Yong-quan. Hybrid algorithm based on monkey algorithm and simple method[J].Computer Science,2013,40(11):248-254.(in Chinese)
[18] Chen W,Deelman E.WorkflowSim:A toolkit for simulating scientific workflows in distributed environments[C]//Proc of IEEE International Conference on E-Science,2012:1-8.
[19] Calheiros R N, Buyya R.Meeting deadlines of scientific workflows in public clouds with tasks replication[J].IEEE Transactions on Parallel & Distributed Systems,2014,25(7):1787-1796.
[20] Chen Wang-hu, Duan Ju, Yu Mao-yi,et al.A scheduling policy of scientific workflows allowing the violation of local time constraints[J].Journal of Computer Engineering & Science,2016,38(11):2165-2171.(in Chinese)
[1] 吴响,邓笋根,陆忠华.国内外科学工作流综述研究[J].科研信息化技术与应用,2014,5(5):86-95.
[5] 左利云,曹志波,董守斌.云计算虚拟资源的熵优化和动态加权评估模型[J].软件学报,2013,24(8):1937-1946.
[7] 贺智明,刘敏.云环境下基于偏好的资源公平分配策略[J].计算机工程与科学,2017,39(11):1991-1999.
[9] 段菊,陈旺虎,王润平,等.云环境下基于聚簇的科学工作流执行优化策略[J].计算机应用,2015,35(6):1580-1584.
[10] 陈曦,毛莺池,接青,等.云计算中基于任务分层和时间约束的关联任务调度算法[J].计算机应用,2014,34(11):3069-3072.
[15] 陈海涛.改进的猴群算法在云计算资源分配中的研究[J].计算机系统应用,2015,24(8):191-196.
[16] 徐小平,张东洁.一种改进的猴群算法[J].计算机系统应用,2017,26(6):193-197.
[17] 陈信,周永权.基于猴群算法和单纯法的混合优化算法[J].计算机科学,2013,40(11):248-254.
[20] 陈旺虎,段菊,俞茂义.允许违反局部时间约束的科学工作流调度策略[J].计算机工程与科学,2016,38(11):2165-2171.
[2] Rao R V,Selvamani K.Data security challenges and its solutions in cloud computing [J].Procedia Computer Science,2015,48:204-209.
[3] Jiang D,Pierre G,Chi C H.EC2 performance analysis for resource provisioning of service-oriented applications[C]//Proc of the 2009 International Conference on Service-Oriented Computing(ICSOC/ServiceWave’09),2009:197-207.
[4] Kim H,El-Khamra Y,Rodero I,et al.Autonomic management of application workflows on hybrid computing infrastructure[J].Scientific Programming,2011,19(2-3):75-89.
[5] Zuo Li-yun, Cao Zhi-bo, Dong Shou-bin. Virtual resource evaluation model based on entropy optimized and dynamic weighted in cloud computing[J].Journal of Software,2013,24(8):1937-1946.(in Chinese)
[6] Rodriguez M A,Buyya R.A responsive knapsack-based algorithm for resource provisioning and scheduling of scientific workflows in clouds[C]//Proc of IEEE International Conference on Parallel Processing,2015:839-848.
[7] He Zhi-ming, Liu Min.A fair resource allocation strategy based on preference in cloud environment[J].Journal of Computer Engineering & Science,2017,39(11):1991-1999.(in Chinese)
[8] Mao M,Humphrey M.Auto-scaling to minimize cost and meet application deadlines in cloud workflows[C]//Proc of International Conference for High Performance Computing,Networking,Storage and Analysis,2011:1-12.
[9] Duan Ju, Chen Wang-hu, Wang Run-ping,et al.Execution optimization policy of scientific workflow based on cluster aggregation under cloud environment[J].Journal of Computer Applications,2015,35(6):1580-1584.(in Chinese)
[10] Chen Xi,Mao Ying-chi, Jie Qing,et al.Related task scheduling algorithm based on task hierarchy and time constraint in cloud computing[J].Journal of Computer Applications,2014,34(11):3069-3072.(in Chinese)
[11] Oliveira D D,Viana V,Ogasawara E,et al.Dimensioning the virtual cluster for parallel scientific workflows in clouds[C]//Proc of ACM Workshop on Scientific Cloud Computing,2013:5-12.
[12] Coutinho R D C,Drummond L M A,Frota Y,et al.Optimizing virtual machine allocation for parallel scientific workflows in federated clouds[J].Future Generation Computer Systems,2015,46(C):51-68.
[13] Lee W R,Teng H Y,Hwang R H.Optimization of cloud resource subscription policy[C]//Proc of IEEE International Conference on Cloud Computing Technology and Science,2012:449-455.
[14] Zhao R,Tang W.Monkey algorithm for global numerical optimization[J].Journal of Uncertain Systems,2008,2(3):165-176.
[15] Chen Hai-tao. Resource allocation in clouding computing based on monkey algorithm[J].Journal of Computer Systems & Applications,2015,24(8):191-196.(in Chinese)
[16] Xu Xiao-ping, Zhang Dong-jie.Improved monkey algorithm[J]. Computer Systems & Applications,2017,26(6):193-197.(in Chinese)
[17] Chen Xin, Zhou Yong-quan. Hybrid algorithm based on monkey algorithm and simple method[J].Computer Science,2013,40(11):248-254.(in Chinese)
[18] Chen W,Deelman E.WorkflowSim:A toolkit for simulating scientific workflows in distributed environments[C]//Proc of IEEE International Conference on E-Science,2012:1-8.
[19] Calheiros R N, Buyya R.Meeting deadlines of scientific workflows in public clouds with tasks replication[J].IEEE Transactions on Parallel & Distributed Systems,2014,25(7):1787-1796.
[20] Chen Wang-hu, Duan Ju, Yu Mao-yi,et al.A scheduling policy of scientific workflows allowing the violation of local time constraints[J].Journal of Computer Engineering & Science,2016,38(11):2165-2171.(in Chinese)
[1] 吴响,邓笋根,陆忠华.国内外科学工作流综述研究[J].科研信息化技术与应用,2014,5(5):86-95.
[5] 左利云,曹志波,董守斌.云计算虚拟资源的熵优化和动态加权评估模型[J].软件学报,2013,24(8):1937-1946.
[7] 贺智明,刘敏.云环境下基于偏好的资源公平分配策略[J].计算机工程与科学,2017,39(11):1991-1999.
[9] 段菊,陈旺虎,王润平,等.云环境下基于聚簇的科学工作流执行优化策略[J].计算机应用,2015,35(6):1580-1584.
[10] 陈曦,毛莺池,接青,等.云计算中基于任务分层和时间约束的关联任务调度算法[J].计算机应用,2014,34(11):3069-3072.
[15] 陈海涛.改进的猴群算法在云计算资源分配中的研究[J].计算机系统应用,2015,24(8):191-196.
[16] 徐小平,张东洁.一种改进的猴群算法[J].计算机系统应用,2017,26(6):193-197.
[17] 陈信,周永权.基于猴群算法和单纯法的混合优化算法[J].计算机科学,2013,40(11):248-254.
[20] 陈旺虎,段菊,俞茂义.允许违反局部时间约束的科学工作流调度策略[J].计算机工程与科学,2016,38(11):2165-2171.
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