基于多QoS约束条件的广域信息管理系统任务调度算法
|
摘要
提出了面向广域信息管理系统(SWIM)的多QoS约束条件的蚁群优化任务调度算法(QoS-ACO)。针对SWIM中用户对任务请求完成服务质量(QoS)的要求,综合考虑任务完成时间、执行安全性和可靠性因素,构造了新的用户综合满意度评价函数和系统任务调度模型,使用SWIM中业务调度QoS总效用评价函数来更新蚁群算法中的信息素。仿真实验结果表明,同等条件下QoS-ACO算法在任务完成时间、安全性、可靠性和QoS总效用值方面都优于传统Min-Min算法和粒子群优化算法,满足用户的任务调度服务质量要求,较好地完成了SWIM调度任务。
An ant colony optimization task scheduling algorithm based on multiple quality of service constraint(QoS-ACO) for SWIM was proposed. Focusing on the multiple quality of service(QoS) requirements for task requests completed in system-wide information management(SWIM), considering the task execution time, security and reliability factors, a new evaluate user satisfaction utility function and system task scheduling model were constructed. Using the QoS total utility evaluation function of SWIM service scheduling to update the pheromone of the ant colony algorithm. The simulation results show that under the same conditions, the QoS-ACO algorithm is better than the traditional Min-Min algorithm and particle swarm optimization(PSO) algorithm in terms of task completion time, security, reliability and quality of service total utility evaluation value, and it can ensure that the user's task scheduling quality of service requirements are met, and can better complete the scheduling tasks of the SWIM.
An ant colony optimization task scheduling algorithm based on multiple quality of service constraint(QoS-ACO) for SWIM was proposed. Focusing on the multiple quality of service(QoS) requirements for task requests completed in system-wide information management(SWIM), considering the task execution time, security and reliability factors, a new evaluate user satisfaction utility function and system task scheduling model were constructed. Using the QoS total utility evaluation function of SWIM service scheduling to update the pheromone of the ant colony algorithm. The simulation results show that under the same conditions, the QoS-ACO algorithm is better than the traditional Min-Min algorithm and particle swarm optimization(PSO) algorithm in terms of task completion time, security, reliability and quality of service total utility evaluation value, and it can ensure that the user's task scheduling quality of service requirements are met, and can better complete the scheduling tasks of the SWIM.
引文
[1]STEPHENS B.System-wide information management(SWIM)demonstration security architecture[C]//2006 IEEE/AIAA Digital Avionics Systems Conference.IEEE,2006:1-12.
[2]CRESCENZO D D,STRANO A,TRAUSMUTH G.SWIM:a next generation ATM information bus-the SWIM-SUIT prototype[C]//IEEE International Enterprise Distributed Object Computing Conference Workshops.IEEE Computer Society,2010:41-46.
[3]赵婷.基于Diameter协议的民航SWIM架构认证的研究[D].天津:中国民航大学,2014.ZHAO T.Research on the authentication of SWIM architecture in civil aviation based on diameter protocol[D].Tianjin:Civil Aviation University of China,2014.
[4]MAHESWARAN M,ALI S,SIEGAL H J,et al.Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems[C]//Heterogeneous Computing Workshop.IEEE,1999:30-44.
[5]CHAUHAN S S,JOSHI R C.A weighted mean time Min-Min Max-Min selective scheduling strategy for independent tasks on grid[C]//Advance Computing Conference.IEEE,2010:4-9.
[6]WU M Y,SHU W,ZHANG H.Segmented Min-Min:a static mapping algorithm for meta-tasks on heterogeneous computing systems[C]//Heterogeneous Computing Workshop.DBLP,2000:375-385.
[7]胡艳华,唐新来.基于改进遗传算法的云计算任务调度算法[J].计算机技术与发展,2016,26(10):137-141.HU Y H,TANG X L.A task scheduling algorithm based on improved genetic algorithm in cloud computing environment[J].Computer Technology and Development,2016,26(10):137-141.
[8]BUYYA R,ABRAMSON D,VENUGOPAL S.The grid economy[J].Proceedings of the IEEE,2005,93(3):698-714.
[9]CASTILLO C,ROUSKAS G N,HARFOUSH K.Online algorithms for advance resource reservations[J].Journal of Parallel and Distributed Computing,2011,71(7):963-973.
[10]刘宴兵,陈杰,熊仕勇.基于QoS相似度的网格任务调度算法[J].重庆邮电大学学报(自然科学版),2009,21(3):416-420.LIU Y B,CHEN J,XIONG S Y.Grid task scheduling algorithm based on QoS similarity[J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2009,21(3):416-420.
[11]邓见光,赵跃龙,袁华强.一种多QoS目标约束的云计算任务调度策略[J].计算机应用研究,2016,33(8):2479-2482.DENG J G,ZHAO Y L,YUAN H Q.Multi-QoS objective constrained task scheduling strategy of cloud computing[J].Application Research of Computers,2016,33(8):2479-2482.
[12]祁晓峰,张兴明,高彦钊.基于离散粒子群优化的可重构系统任务调度算法[J].小型微型计算机系统,2018,39(3):556-561.QI X F,ZHANG X M,GAO Y Z.Discrete particle swarm optimization-based task scheduling algorithm in reconfigurable system[J].Journal of Chinese Computer Systems,2018,39(3):556-561.
[13]RANDLES M,LAMB D,TALEB-BENDIAB A.A comparative study into distributed load balancing algorithms for cloud computing[C]//International Conference on Advanced Information NETWORKINGand Applications Workshops.IEEE Computer Society,2010:551-556.
[14]DORIGO M,GAMBARDELLA L M.Ant colony system:a cooperative learning approach to the traveling salesman problem[J].IEEETransactions on Evolutionary Computation,1997,1(1):53-66.
[15]张海燕,吴凡,王建新.基于蚁群算法的Hadoop资源感知调度器研究[J].计算机工程与应用,2014,50(15):65-71.ZHANG H Y,WU F,WANG J X.Research of Hadoop resource-aware scheduler based on ant colony algorithm[J].Computer Engineering and Applications,2014,50(15):65-71.
[16]华丹.基于QoS的蚁群优化在网格资源调度中的研究[D].南京:南京信息工程大学,2011.HUA D.Research on grid resources scheduling based on QoS-Ant colony optimization algorithm[D].Nanjing:Nanjing University of Information Science&Technology,2011.
[17]LU X,GU Z.A load-adapative cloud resource scheduling model based on ant colony algorithm[C]//IEEE International Conference on Cloud Computing and Intelligence Systems.IEEE,2011:296-300.
[18]童钊,陈洪剑,陈明,等.一种云环境下基于混合型BBO的任务调度算法[J].计算机工程与科学,2018,40(5):765-772.TONG Z,CHEN H J,CHEN M,et al.A hybrid biogeography-based optimization algorithm for task scheduling in cloud computing[J].Journal of Computer Engineering&Science,2018,40(5):765-772.
[2]CRESCENZO D D,STRANO A,TRAUSMUTH G.SWIM:a next generation ATM information bus-the SWIM-SUIT prototype[C]//IEEE International Enterprise Distributed Object Computing Conference Workshops.IEEE Computer Society,2010:41-46.
[3]赵婷.基于Diameter协议的民航SWIM架构认证的研究[D].天津:中国民航大学,2014.ZHAO T.Research on the authentication of SWIM architecture in civil aviation based on diameter protocol[D].Tianjin:Civil Aviation University of China,2014.
[4]MAHESWARAN M,ALI S,SIEGAL H J,et al.Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems[C]//Heterogeneous Computing Workshop.IEEE,1999:30-44.
[5]CHAUHAN S S,JOSHI R C.A weighted mean time Min-Min Max-Min selective scheduling strategy for independent tasks on grid[C]//Advance Computing Conference.IEEE,2010:4-9.
[6]WU M Y,SHU W,ZHANG H.Segmented Min-Min:a static mapping algorithm for meta-tasks on heterogeneous computing systems[C]//Heterogeneous Computing Workshop.DBLP,2000:375-385.
[7]胡艳华,唐新来.基于改进遗传算法的云计算任务调度算法[J].计算机技术与发展,2016,26(10):137-141.HU Y H,TANG X L.A task scheduling algorithm based on improved genetic algorithm in cloud computing environment[J].Computer Technology and Development,2016,26(10):137-141.
[8]BUYYA R,ABRAMSON D,VENUGOPAL S.The grid economy[J].Proceedings of the IEEE,2005,93(3):698-714.
[9]CASTILLO C,ROUSKAS G N,HARFOUSH K.Online algorithms for advance resource reservations[J].Journal of Parallel and Distributed Computing,2011,71(7):963-973.
[10]刘宴兵,陈杰,熊仕勇.基于QoS相似度的网格任务调度算法[J].重庆邮电大学学报(自然科学版),2009,21(3):416-420.LIU Y B,CHEN J,XIONG S Y.Grid task scheduling algorithm based on QoS similarity[J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2009,21(3):416-420.
[11]邓见光,赵跃龙,袁华强.一种多QoS目标约束的云计算任务调度策略[J].计算机应用研究,2016,33(8):2479-2482.DENG J G,ZHAO Y L,YUAN H Q.Multi-QoS objective constrained task scheduling strategy of cloud computing[J].Application Research of Computers,2016,33(8):2479-2482.
[12]祁晓峰,张兴明,高彦钊.基于离散粒子群优化的可重构系统任务调度算法[J].小型微型计算机系统,2018,39(3):556-561.QI X F,ZHANG X M,GAO Y Z.Discrete particle swarm optimization-based task scheduling algorithm in reconfigurable system[J].Journal of Chinese Computer Systems,2018,39(3):556-561.
[13]RANDLES M,LAMB D,TALEB-BENDIAB A.A comparative study into distributed load balancing algorithms for cloud computing[C]//International Conference on Advanced Information NETWORKINGand Applications Workshops.IEEE Computer Society,2010:551-556.
[14]DORIGO M,GAMBARDELLA L M.Ant colony system:a cooperative learning approach to the traveling salesman problem[J].IEEETransactions on Evolutionary Computation,1997,1(1):53-66.
[15]张海燕,吴凡,王建新.基于蚁群算法的Hadoop资源感知调度器研究[J].计算机工程与应用,2014,50(15):65-71.ZHANG H Y,WU F,WANG J X.Research of Hadoop resource-aware scheduler based on ant colony algorithm[J].Computer Engineering and Applications,2014,50(15):65-71.
[16]华丹.基于QoS的蚁群优化在网格资源调度中的研究[D].南京:南京信息工程大学,2011.HUA D.Research on grid resources scheduling based on QoS-Ant colony optimization algorithm[D].Nanjing:Nanjing University of Information Science&Technology,2011.
[17]LU X,GU Z.A load-adapative cloud resource scheduling model based on ant colony algorithm[C]//IEEE International Conference on Cloud Computing and Intelligence Systems.IEEE,2011:296-300.
[18]童钊,陈洪剑,陈明,等.一种云环境下基于混合型BBO的任务调度算法[J].计算机工程与科学,2018,40(5):765-772.TONG Z,CHEN H J,CHEN M,et al.A hybrid biogeography-based optimization algorithm for task scheduling in cloud computing[J].Journal of Computer Engineering&Science,2018,40(5):765-772.