|本期目录/Table of Contents|

 Wu Jiahao,Peng Zhiping*,Cui Delong,et al.Combining PSO and GA for workflow scheduling based on multi-agent system[J].Journal of Nanjing University(Natural Sciences),2017,53(6):1114.[doi:10.13232/j.cnki.jnju.2017.06.013]





Combining PSO and GA for workflow scheduling based on multi-agent system
Wu Jiahao1Peng Zhiping2*Cui Delong2Li Qirui2He Jieguang2
1.Department of Computer,Guangdong University of Technology,Guangzhou,510006,China;
2.Department of Computer and Electronic Information,Guangdong University of Petrochemical Technology,Maoming,525000,China
cloud workflowjob schedulingmulti-agent systemparticle swarm optimization algorithmgenetic algorithm
With the arrival of big data era,workflow applications are transferring from original infrastructure to cloud computing platforms,which is more efficient,reliable and affordable to meet the computational requirements of a large number of workflow applications.Aiming at the problems of workflow job scheduling on cloud,such as the minimization of the execution time,the optimal allocation of the job scheduling and the convergence time of the scheduling algorithm,an improved algorithm named Combining Particle Swarm Optimization Algorithm(PSO) and Genetic Algorithm(GA) for Workflow Scheduling Based on Multi-Agent System was presented.This algorithm firstly uses both the particle’s own historical position and particle group history optimal position to optimize the global optimal solution of the search process,and then introduces multi-agent system,where each particle acts as an agent,and those agents complete and coordinate each other.Finally,genetic algorithm operates which depends on an information interaction between agents cross-variation based on the target,not only to avoid the blind randomization of particle swarm and falling into the local optimal solution,but also to accelerate the global optimal solution of the convergence process.In this paper,a real workflow data is used for simulate the experiments,and the experimental result shows the effectiveness of the algorithm.


 [1] Shishira S R,Kandasamy A,Chandrasekaran K,et al.Survey on meta heuristic optimization techniques in cloud computing.In:Proceedings of 2016 International Conference on Advances in Computing,Communications and Informatics(ICACCI).Jaipur,India:IEEE.2016:1434-1440. 
[2] Thomas E,Zaigham M,Ricardo P.云计算:概念、技术与架构.龚奕利,贺莲,胡 创译.机械工业出版社,2014,14-18.
[3] Wu F H,Wu Q B,Tan Y S.Workflow scheduling in cloud:A survey.The Journal of Supercomputing,2015,71(9):3373-3418.
[4] Durillo J J,Prodan R.Workflow scheduling on federated clouds.In:European Conference on Parallel Processing.Springer Berlin Heidelberg,2014:45-50.
[5] Topcuoglu H,Hariri S,Wu M Y.Performance-effective and low-complexity task scheduling for heterogeneous computing.IEEE Transactions on Parallel and Distributed Systems,2002,13(3):260-274.
[6] El-Rewini H,Lewis T G.Scheduling parallel program tasks onto arbitrary target machines.Journal of Parallel and Distributed Computing,1990,9(2):138-153.
[7] Liu G Q,Poh K L,Xie M.Iterative list scheduling for heterogeneous computing.Journal of Parallel and Distributed Computing,2005,65(5):654-665. 
[8] Arabnejad H,Barbosa J G.List scheduling algorithm for heterogeneous systems by an optimistic cost table.IEEE Transactions on Parallel and Distributed Systems,2014,25(3):682-694.
[9] Salza P,Ferrucci F,Sarro F.Deploy and execute parallel genetic algorithms in the cloud.In:Proceedings of the 2016 on Genetic and Evolutionary Computation Conference(CECCO 2016).Denver,CO,USA:ACM,2016:121-122.
[10] Li H H,Chen Z G,Zhan Z H,et al.Renumber coevolutionary multiswarm particle swarm optimization for multi-objective workflow scheduling on cloud computing environment.In:Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation.Madrid,Spain:ACM,2015:1419-1420.
[11] Shojafar M,Javanmardi S,Abolfazli S,et al.FUGE:A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method.Cluster Computing,2015,18(2):829-844.
[12] Zhang F,Cao J W,Li K Q.Multi-objective scheduling of many tasks in cloud platforms.Future Generation Computer Systems,2014,37:309-320.
[13] 郭 禾,陈 征,于玉龙等.带通信开销的DAG工作流费用优化模型与算法.计算机研究与发展,2015,52(6):1400-1408.(Guo H,Chen Z,Yu Y L,et al.A communication aware DAG workflow cost optimization model and algorithm.Journal of Computer Research and Development,2015,52(6):1400-1408.)
[14] Pandey S,Wu L L,Guru S M,et al.A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments.In:Proceedings of the IEEE International Conference on Advanced Information Networking and Applications.Aina,Perth,Australia:IEEE,2010:400-407.
[15] Zuo L Y,Shu L,Dong S B,et al.A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing.IEEE Access,2015,3:2687-2699.
[16] Kianpisheh S,Charkari N M,Kargahi M.Ant colony based constrained workflow scheduling for heterogeneous computing systems.Cluster Computing,2016,19(3):1053-1070.
[17] Hafez M G,Elgamel M S.Agent-based cloud computing:A survey.In:Proceedings of the IEEE International Conference on Future Internet of Things and Cloud(FiCloud).Vienna,Austria:IEEE,2016:285-292.
[18] 肖 正,吴承荣,张世永.多Agent系统合作与协调机制研究综述.计算机科学,2007,34(5):139-143.(Xiao Z,Wu C R,Zhang S Y.A survey of cooperation and coordination in multi-agent system.Computer Science,2007,34(5):139-143.) 
[19] Padmavathi S,Dharani V P,Maithrreye S,et al.Multi-agent framework for cloud service composition.In:Proceedings of the 2016 International Conference on Emerging Trends in Engineering,Technology and Science(ICETETS).Pudukkottai,India:IEEE,2016:1-5.
[20] 侯 富,毛新军,吴 伟.一种基于多Agent系统的云服务自组织管理方法.软件学报,2015,26(4):835-848.(Hou F,Mao X J,Wu W.Self-organizing management approach for cloud services based on multi-agent system.Journal of Software,2015,26(4):835-848.)
[21] Wei Y,Blake M B.Proactive virtualized resource management for service workflows in the cloud.Computing,2016,98(5):523-538.
[22] Chen W W,Deelman E.Workflow Sim:A toolkit for simulating scientific workflows in distributed environments.In:Proceedings of the IEEE International Conference on E-Science.Chicago,IL,USA:IEEE,2012:1-8.
[23] Bharathi S,Chervenak A,Deelman E,et al.Characterization of scientific workflows.In:Proceedings of 2008 3rd Workshop on Workflows in Support of Large-Scale Science.Austin,TX,USA:IEEE,2008:1-10.
[24] Van Den Bergh F.An analysis of particle swarm optimizers.Ph.D.Dissertation.Pretoria:University of Pretoria,2002:87-110.



更新日期/Last Update: 2017-11-27