摘要
目前的e-Science研究越来越依赖于大规模科学应用程序和软件工具对海量数据的分析处理能力及网格环境中高性能资源的计算能力,作为一种帮助科学家进行复杂流程组合和流程自动运行的管理平台,科学工作流管理系统(Scientific Workflow Management System, SWfMS)在科研过程中发挥着越来越重要的推动作用,而科学工作流(Scientific Workflow, SWF)的相关技术也逐渐成为当今学术界的研究热点。目前多个大型e-Science中心分别开发了面向特定领域的SWfMS,但这些系统缺乏统一标准且系统间的互操作也比较困难,在新的领域中进行复杂流程管理时需要对已有系统进行大量修改或重新开发,为了充分利用科研资源并达到多系统间互操作的目标,研究一个标准统一的通用型SWfMS成为目前SWF管理中亟需解决的问题之一。同时,随着越来越多网格资源的加入,目前对计算资源的付费使用也成为一种必然趋势,在这类效用网格中对工作流进行调度时需考虑工作流的执行时间、执行费用及可靠性等多个目标,这些目标间相互联系且相互制约,如何在多个目标间进行权衡并达到多目标综合性能的最优值也是近几年的研究热点之一。在深入分析SWF管理及调度的研究现状与不足的基础上,本文围绕通用型SWfMS的相关内容及工作流在效用网格下的有效调度展开了大量研究,主要贡献如下:
(1)分析并总结SWF的相关技术和研究现状
鉴于SWF刚刚起步的状态,对目前国内外已有的相关工作进行全面总结和比较。分别从SWF模型、表示、语言及调度等多方面对其关键技术进行全面总结、比较与评价,并对近两年的最新研究和国内的研究现状进行分析,为全文工作的开展奠定基础。
(2)设计并实现基于BPEL的通用型SWfMS
针对目前多个SWfMS间互操作较困难的缺点,研究通用性较好的BPEL (Busicess Process Execution Language)模型,以集合预报为应用背景,设计并实现基于BPEL的通用型科学工作流管理系统——集合预报科学工作流管理系统(Ensemble Prediction Scientific Workflow system, EPSWFlow)。系统利用BPEL中丰富的控制语义、对Web服务的全面支持等优点实现了对预报流程中各服务的按需组合与调度;并采用JSDL (Job Submission Description Language)对实验环境中无法进行Web服务封装的大量遗留应用程序进行描述,通过基于Web的标准作业调度软件GridSAM对这些作业进行调度与监控,解决了遗留应用程序的集成问题。
(3)研究通用型SWfMS的动态适应性
针对SWF的动态适用性需求,分别从SWF模型,SWfMS的系统实现及执行期的系统容错等方面对SWfMS的动态适应性进行研究。提出SWF四层抽象模型,在不同抽象层上实现对Web服务和底层遗留应用程序的抽象描述,并在执行过程中由SWF引擎动态选择服务,对资源进行实时绑定,以支持SWF的动态适应性;此外,研究SWF执行期的系统容错,在EPSWFlow中实现三种容错策略,有效提高系统在执行过程中的异常处理能力,进一步提高系统的动态适应性。
(4)研究效用网格下截止期约束的工作流费用优化调度问题
工作流在各种环境和不同条件下的任务调度是工作流研究领域的重要内容之一,其调度性能的好坏直接影响系统的运行效率。在对资源进行付费使用的效用网格中,针对截止期约束的工作流费用优化问题提出三种有效的调度算法:基于时序一致的截止期约束逆向分层算法TCDBL (Temporal Consistency based Deadline Bottom Level)、基于路径平衡的工作流费用优化算法PBCO (Path Balance based Cost Optimization)及基于优先级规则BFTCSTM (Best Fit based on Time-dependent Coupling Strength and Temporal Mobility)的迭代算法,三种算法从不同角度对工作流的费用优化问题进行研究,均取得了很好的调度效果。
(5)研究动态资源下基于优先级因子的工作流时间-费用优化调度问题
在资源状态动态变化的网格环境中,工作流执行完成之前很难对工作流的执行时间或执行费用进行准确预测,因此研究基于优先级因子的费用优化策略对工作流的执行时间与执行费用同时进行优化。在分层策略的基础上提出三种实时调度算法:基于逆向分层的Sufferage算法(BLSuff)、基于逆向分层的Min-Min算法(BLMin)及基于逆向分层的Min-Max算法(BLMax)。三种算法均基于逆向深度对任务进行分层,设计基于优先级因子的衡量标准对任务逐层进行调度,达到了同时优化工作流执行时间与执行费用的目标,取得了良好的调度效果。
综上所述,本文针对目前SWF技术中亟需解决的几个关键问题进行了研究,并提出有效的解决方案。本文的研究对于推动复杂科学计算流程的组合和管理,并最终推动科研进程的发展具有较高的理论价值和应用价值。
Recently, the development of the e-Science research is, to some extent, determined by the data analysis of large scale scientific application programs and software tools on a huge number of data, as well as the computation abilities of the high performance resources for utility grids. As an effective management platform for combining complex processes and operating processes automatically, Scientific Workflow Management System (SWfMS) plays a more and more important role in relevant researches, and the development of new technologies of Scientific Workflow (SWF) has gained more and more attention. Recently, many large e-Science centers have developed dozens of SWfMSs in their own specific research domains, however, there is no any general standard among these systems and the co-operation of several systems is difficult. Therefore, it is necessary to generate a new system for general use by modifying an existing system for a new domain, or even developing a new system. Moreover, the“pay-per-use”model is becoming popular as more and more resources are added to the grids, several aspects should be taken into consideration when scheduling the workflow on these utility grids, such as workflow execution time, workflow execution cost, system reliability and so on. These objectives are contracted and restricted with each other. Therefore, how to optimize the operation efficiency among these aspects has become a hot topic. Based on the discussion of the current studies and drawbacks of SWF management and scheduling, this thesis is focused on the studies of SWfMS and the workflow scheduling on the utility grids. The main contribution of this thesis can be summarized as follows:
(1) A review of the relative technologies and current studies of SWF
We review the current studies of SWF, including the lifecycles, models, presentations, languages, scheduling, and so on. We compare these technologies and analyze the recent studies, which provide the basis for the studies in this thesis.
(2) Design of the SWfMS for general use based on BPEL
In order to solve the problems in co-operation among several SWfMSs, we exploit the general using of Business Process Execution Language (BPEL), and design a new SWfMS referred as Ensemble Prediction Scientific Workflow system (EPSWFlow) based on BPEL with application in ensemble prediction. Based on the merits of BPEL such as plenty of control structure, full support for the web services, etc, EPSWFlow accomplishes combining and scheduling the services exisiting in the ensemble process dynamically. Moreover, EPSWFlow exploits JSDL (Job Submission Description Language) to describe a large number of legacy applications which cannot be enveloped to web sevices, schedules and monitors these applications by using the standard job submission system GridSAM, which solves the problems of intergrating legacy applications.
(3) Research on adaptability of SWfMS on general purpose
To address the dynamic adaptability of SWF, we performed studies on SWF models of the architecture and implementation of EPSWFlow system, and propose a four-level abstracted model to provide an abstracted description of the Web services and legacy applications at each abstracted level. The SWF engine can select a service dynamically during the executing stage and make a real-time binding for the resource. At the same time, we study the system reliability, and provide three types of fault-tolerant strategies, which improve the abilities of solving abnormal situation, and improve the system reliability further.
(4) Research on the cost optimization problems in workflow scheduling with deadline constraint on utility grids
The workflow scheduling problem in different environments and conditions is one of the most important topics in SWF management because the scheduling result can make a great effect on the system performance. In order to solve the time and cost trade-off problem in workflow scheduling with deadline constraint, we present three novel algorithms in this thesis: Temporal Consistency based Deadline Bottom Level algorithm(TCDBL), Path Balance based Cost Optimization algorithm (PBCO) and BFTCSTM(Best Fit based on Time-dependent Coupling Strength and Temporal Mobility) rule-based iterative algorhtm. All these algorithms can decrease the workflow costs comparing with the previous algorithms.
(5) Research on the time-cost trade-off problem with priority factors in workflow scheduling under dynamic environment
As it is difficult to make an exact prediction of workflow execution time and workflow execution cost in the dynamic grid environments ahead of schedule, we study the time and cost trade-off problems based on the priority factor in workflow scheduling. We propose three real-time heuristics based on the bottom level strategy: Bottom Level based Sufferage (BLSuff), Bottom Level based Min-Min (BLMin) and Bottom Level based Min-Max (BLMax). These heuristics divide the tasks into several groups based on the workflow synchronization properties, and design a metric to optimize the workflow execution time and cost simultaneously using the trade-off factor, which obtain a better scheduling result.
To sum up, we have studied on several key problems in scientific workflow management and scheduling , and propose some effective solutions. The studies in this thesis are helpful for the further study on the composition and management for the complex scientific computations and therefore accelerate the pace of scientific progress in both theory and practice.
引文
[1]黄理灿.e-Science、网格及其可扩展性体系结构[M].北京:北京邮电大学出版社,2005:1-3.
[2]余德浩.计算数学与科学工程计算及其在中国的若干发展[J].数学进展,2002,31(1):1-6.
[3] Foster I, Kesselman C. The Grid 2: Blueprint for a New Computing Infrastructure [M]. Elsevier Inc, 2004:199~206.
[4] Deelman E, Gannon D, Shields M, et al. Workflows and e-Science: An overview of workflow system features and capabilities [J]. Future Generation Computer Systems, 2009,25(5):528-540.
[5] Gore A. The Digital Earth: Understanding our planet in the 21st Century [J]. Australian surveyor, 1998,48(2):89-91.
[6]黄振中.美国面向21世纪的信息技术发展战略(IT2计划)[J].中国信息导报, 2000,8:54-55.
[7] Stevens R. High Performance Computing and Communications [J]. Future Generation Computer Systems, 1994,10(2-3):159-167.
[8] Networked Computing for the 21st Century: High End Computing and Computation [R], Science American, 1999.
[9]石云,陈蜀宇.论高性能计算与普适计算[J].六盘水师范高等专业学校学报, 2008,20(3):18-20.
[10] Foster I. Kesselman C, Tuecke S. The Anatomy of the Grid: Enabling Scalable Virtual Organizations [J]. International Journal of High-Performance Computing Applications, 2001,15(3):200-222.
[11] Karplus M, Mccammon J. A. Molecular dynamics simulations of biomolecules [J]. Nature Structural Biology, 2002,9:646-652.
[12] Hut P. Dense stellar systems as laboratories for fundamental physics [J]. New Astronomy Reviews, 2010,54(3-6):163-172.
[13] Bader D. A. Petascale Computing: Algorithms and Applications [M]. Hoboken, NJ: Taylor & Francis Ltd, 2008.
[14] Shankar S, Kini A, Dewitt D. J, et al. Integrating databases and workflow systems [J]. ACM SIGMOD Record, 2005,34(3):5-11.
[15] Singh G, Kesselman C, Deelman E. Optimizing Grid-Based Workflow Execution [J]. Journal of Grid Computing, 2006,3(3-4):201-219.
[16] Berriman G. B, Good J. C, Laity A. C, et al. Montage: A Grid Enabled Image Mosaic Service for the National Virtual Observatory [C]. in the Proc. of 8th Astronomical Data Analysis Software and Systems (ADASS 2003), Strasbourg:France,Astronomical Society of the Pacific, 2004:593-601.
[17]罗海滨,范玉顺,吴澄.工作流技术综[J].软件学报, 2000,11(7):899-907.
[18] Altintas I, Berkley C, Jaeger E, et al. Kepler: An Extensible System for Design and Execution of Scientific Workflows [C]. in the Proc. of 16th International Conference on Scientific and Statistical Database Management (SSDBM'04), IEEE Computer Society press, 2004: 423-424.
[19] Oinn T, Greenwood M, Addis M, et al. Taverna: Lessons in creating a workflow environment for the life sciennces [J]. Concurrency and Computation, 2006,18(10):1067-1100.
[20] Majithia S, Shields M, Taylor I, et al. Triana: A Graphical Web Service Composition and Execution Toolkit [C]. in the Proc. of the IEEE International Conference on Web Services (ICWS’04). San Diego, IEEE Computer Society press, 2004: 514-526.
[21] Deelman E, Singh G, Su M-H, et al. Pegasus: A framework for Mapping Complex Scientific Workflows onto Distributed Systems [J]. Science Programming, 2005,13(3):219-237.
[22] Fahringer T, Prodan R, Duan R, et al. ASKALON: A Grid Application Development and Computing Environment [C]. in the Proc. of the 6th IEEE/ACM International Workshop on Grid Computing, IEEE Computer Society press, 2005:122-131.
[23] Guan Z, Hernandez F, Bangalore P, et al. Grid-Flow: A Grid-Enabled Scientific Workflow System with a Petri Net-Based Interface [J]. Concurrency and Computation: Practice and Experience. 2006,18(18):1115-1140
[24]文元桥.协同地球科学计算环境的协同与共享研究[D].武汉:华中科技大学,2006:180-181.
[25] Lin C, Lu S, Fei X, et al. A Reference Architecture for Scientific Workflow Management Systems and the VIEW SOA Solution [J]. IEEE Transaction on Service Computing, 2009,2(1):79-92.
[26] Ullman JD. NP-complete Scheduling Problems [J]. Journal of Computer and System Sciences, 1975,10(3):384-393.
[27] Kwok Y-K, Ahmad I. Dynamical Critical-Path Scheduling: An Effective Technique for Allocating Task Graphs to Multiprocessors [J]. IEEE Transaction on Parallel and Distributed System, 1996,7(5):506-521.
[28] Topcuoglu H, Hariri S, Wu M-Y. Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing [J]. IEEE Transaction on Parallel and Distributed System, 2002,13(3):260-274.
[29] Rahman M, Venugopal S, Buyya R. A Dynamic Critical Path Algorithm for Scheduling Scientific Workflow Applications on Global Grids [C]. in the Proc of the 3rdIEEE International Conference on e-Science and Grid Computing (e-Science’07), IEEE Computer Society press, 2007:35-42. 13787011840
[30] Sih G. C, Lee E. A. A Compile-Time Scheduling Heuristic for Interconnection-Constrained Heterogeneous Processor Architecture [J]. IEEE Transaction on Parallel and Distributed System, 1993,4(2):175-187.
[31] Iverson M. A, Ozguner F. J, Follen G. Parallelizing Existing Applications in a Distributed Heterogeneous Environment [C]. in the Proc. of the 4th Heterogeneous Computing Workshop, IEEE Computer Society press, 1995:93-100.
[32] Yang T, Gerasoulis A. DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors [J]. IEEE Transaction on Parallel and Distributed System, 1994,5(9):961-977.
[33] Kruatrachue B, Lewis T. Grain Size Determination for Parallel Processing [J]. IEEE Software, 1988,5(1):23-32.
[34] Shroff P, Watson D. W, Flann N. S, et al. Genetic Simulation Annealing for Scheduling Data-dependent Tasks in Heterogeneous Environments [C]. in the Proc. of Heterogeneous Computing Workshop, IEEE Computer Society press, 1996:98-104.
[35] Buyya R, Abramson D, Venugeopal S. The Grid Economy [J]. Proceeding of the IEEE. 2005, 93(3): 698-714.
[36] Wieczorek M, Prodan R, Fahringer T. Comparison of Workflow Scheduling Strategies on the Grid [J]. Parallel Processing and Applied Mathematics, LNCS 3911, 2006:792-800.
[37] Yu J, Buyya R. A Taxonomy of Workflow Management Systems for Grid Computing [J]. Grid Computing, 2006,3(3-4):171-200.
[38] Brucker P. Scheduling Algorithms [M]. New York: Springer, 2007:77~90.
[39] Wieczoreka M, Hoheisel A, Prodan R. Towards a general model of the multi-criteria workflow scheduling on the grid [J]. Future Generation Computer Systems, 2009,25(3):237-256.
[40] Knowles J, Corne D. The Pareto archived evolution strategy: A new baseline algorithm for Pareto multiobjective optimization [C]. in the proc. of IEEE Congress on Evolutionary Computation, IEEE Computer Society press, 1999: 84-93.
[41] Talukder A. K, Kirley M, Buyya R. Multiobjective differential evolution for scheduling workflow applications on global grids [J]. Concurrency and Computation: Practice and Experience, 2009,21(13):1742-1756.
[42] Yu J, Kirley M, Buyya R. Multi-objective Planning for Workflow Execution on Grids [C]. in the proc. of the 8th IEEE/ACM International Conference on Grid Computing, IEEE Computer Society press, 2007:181-190.
[43] Neng C. W, Jun Z. An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements. IEEE Transaction on Systems, Man and Cybernetics-Part C: Applications and Reviews, 2009,39(1):29-43.
[44]季一木,王汝传.基于粒子群的网格任务调度算法研究[J].通信学报, 2007,28(10):60-66.
[45]张晓东,李小平,王茜等.服务工作流的混合粒子群调度算法[J].通信学报,2008,29(8):87-94.
[46] Prodan R, Wieczorek M. Bi-Criteria Scheduling of Scientific Grid Workflows [J]. IEEE Transaction on Automation Science and Engineering, 2009.7(2):364-376.
[47] Yu J, Buyya R, Tham C. K. Cost-based Scheduling of Scientific Workflow Applications on Utility Grids [C]. in the Proc. of the 1st International Conference on e-Science and Grid Computing (e-Science'05), Melbourne, 2005:147-154.
[48] Yu J, Buyya R, Ramamohanarao K. Workflow Scheduling Algorithms for Grid Computing [J]. Metaheuristics for Scheduling in Distributed Computing Environments, LNCS 146, 2008:173-214.
[49]苑迎春,李小平,王茜.基于串归约的网格工作流费用优化方法[J].计算机研究与发展,2008,45(2):246-253.
[50]苑迎春,李小平,王茜等.基于优先级规则的网格工作流调度[J].电子学报,2009,37(7):1457-1464.
[51]苑迎春,李小平,王茜等.基于逆向分层的网格工作流调度算法[J].计算机学报,2008,31(2):282-290.
[52]陈宏伟,王汝传.费用-时间优化的网格有向无环图调度算法[J].电子学报, 2005, 33(8):1375-1380.
[53] Yuan Y, Li X. P, Wang Q, et al. Deadline division-based heuristic for cost optimization in workflow scheduling [J]. Information Sciences, 2009,179(15):2562-2575.
[54] Ranaldo N, Zimeo E. Time and Cost-Driven Scheduling of Data Parallel Tasks in Grid Workflows [J]. IEEE System Journal, 2009,3(1):104-130.
[55]苑迎春,李小平,王茜等.成本约束的网格工作流时间优化方法[J].计算机研究与发展, 2009,46(2):194-201.
[56] Sakellariou R, Zhao H, Tsiakkouri E, et al. Scheduling Workflows with Budget Constraints [C]. Integrated Research in GRID Computing, Springer, 2007:189-202.
[57] Dogan, A. and F. Ozguner, Biobjective Scheduling Algorithm for Execution Time-Reliability Trade-off in Heterogeneous Computing Systems [J]. Computer Journal 2005,48(3):300-314.
[58] Maheswaran M, Ali S, Siegel H. J, et al. Dynamic mapping of a class of independent tasks onto heterogeneous computing systems [J]. Journal of Parallel and Distributed Computing, 1999,59(2):107-131.
[59]王勇,胡春明,杜宗霞.服务质量感知的网格工作流调度[J].软件学报,2006,17(11):2341-2351.
[60]伍之昂,罗军舟,宋爱波.基于QoS的网格资源管理[J].软件学报, 2006,17(11):2264-2276.
[61] Ludascher B, Altintas I, Berkley C, et al. Scientific Workflow and the Kepler System [J]. Concurrency and Computation: Practice and Experiences , 2005,18(10): 1039-1065.
[62] Mandal N, Deelman E, Mehta G, et al. Integrating Existing Scientific Workflow Systems: The Kepler/Pegasus Example [C]. in the Proc. of the 2nd workshop on Workflows in support of large-scale science, Monterey, USA: ACM Press, 2007,21-28.
[63] Hull D, Wolstencroft K, Stevens R, et al. Taverna: a tool for building and running workflows of services [R]. Nucleic Acids Research, Oxford University Press, 2006.
[64] Oinn T, Li P, Kell D. B. Taverna/myGrid: Aligning a Workflow System with the life Sciences Community [C]. Workflows for e-Science, London, Springer, 2007:300-319.
[65] Zhao J, Goble C, Stevens R, et al. Mining Taverna's semantic web of provenance [J]. Concurrency and Computation: Practice & Experience, 2008,20(5):463-472.
[66] Deelman E, Mehta G, Singh G, et al. Pegasus: Mapping Large-Scale Workflows to Distributed Resources [C]. Workflow for e-Science. London, Springer, 2007: 376-394.
[67] Gil Y, Ratnakar V, Deelman E, et al. Wings for Pegasus: Creating Large-Scale Scientific Applications Using Semantic Representations of Computational Workflows [C]. in the Proc. of the 19th Conference on Innovative Applications of Artificial Intelligence (IAAI’07). Vancouver, Canada: AAAI Press, 2007:1767-1774.
[68] Fahringer T, Prodan R, Duan R, et al. ASKALON: A Development and Grid Computing Environment for Scientific Workflows [C]. Workflow for e-Science, London, Springer, 2007:450-471.
[69] Wieczorek M, Prodan R, Fahringer T. Scheduling of Scientific Workflows in the ASKALON Grid Environment [J]. ACM SIGMOD Record, 2005,34(3):56-62.
[70] Qin J, Fahringer T, Pllana S. UML based grid workflow modeling under Askalon [C]. Distributed and Parallel Systems, Springer, 2007:191-200.
[71] Babik M, Gatial E, Habala O, et al. Semantic Grid Services in K-Wf Grid [C]. in the Proc. of the 2nd International Conference on Semantics, Knowledges and Grid (SKG’06), Guilin:China, IEEE Computer Society press, 2003:66-73.
[72] Bubak M, Nowakowski P, Unger S. K-WfGrid– Knowledge-Based Workflow System for Grid Applications [C]. in the Proc. of the Cracow Grid Workshop2006,39-42.
[73] Truong H-L, Brunner P, Fahringer T, et al. K-WfGrid Distributed Monitoring and Performance Analysis Services for Workflows in the Grid [C]. in the Proc. of 2nd IEEE International Conference on e-Science and Grid Computing (e-Science’06), Amsterdam, Netherlands, IEEE Computer Society press, 2006:15-29.
[74] Taylor I, Shields M, Wang I, et al. The Triana Workflow Environment: Architecture and Applications [C], in Workflow for e-Science, 2007:320-339.
[75] Taylor I, Shields M, Wang I, et al. Visual Grid Workflow in Triana [J]. Journal of Gird Computing, 2006,3(3-4):153-169.
[76] Churches D, Gombas G, Harrison A, et al. Programming Scientific and Distributed Workflow with Triana Services [J]. Concurrency and Computation: Practice and Experience, 2004,18(10):1021-1037
[77] Bahsi E. M, Ceyhan E, Kosar T. Conditional workflow management: A survey and analysis [J]. Scientific Programming, 2007,15(4):283-297.
[78] Kacsuk P. T, Sipos G. Multi-Grid, Multi-User Workflows in the P-GRADE Grid Portal [J]. Journal of Grid Computing, 2006,3(3-4):221-238.
[79] Glatard T, Sipos G, Montagnat J, et al. Workflow-Level Parameteric Study Surport by MOTEUR and the P-GRADE Portal [C]. Workflow for e-Science, London, Springer, 2007:279-299.
[80] Kacsuk P, Farkas Z, Sipos G, et al. Workflow-level Parameter Study Management in multi-Grid environments by the PGRADE Grid portal [C]. in the Proc. of International Workshop on Grid Computing Environment, 2006:43-55.
[81] Laszewski G. V, Foster I, Gawor J. Java CoG Kit Workflow Concepts for Scientific Experiments [R]. Argonne National Laboratory, Argonne, USA, 2005.
[82] Laszewski, G. V, Foster I, Gawor J. CoG Kits: A Bridge between Commodity Distributed Computing and High Performance Grids [C]. in the Proc. of the ACM 2000 Conference on Java Grade, ACM press, 2000.
[83] Gulamali M. Y, Mcgough A. S, Marsh R. J, et al. Performance guided scheduling in GENIE through ICENI [C], in the Proc. of the UK e-Science All Hands Meeting 2004, http://www.lesc.doc.ic.ac.uk/iceni/pdf/ahm2004_gen.
[84] Mcgough A. S, Lee W, Cohen J, et al. ICENI [C]. Workflow for e-Science, London, Springer, 2007:395-415.
[85] Deelman E, Blythe J, Gil Y, et al. Pegasus: Mapping Scientific Workflows onto the Grid [J]. Grid Computing, LNCS 3165, 2004:131-140.
[86] Wassermann B, Emmerich W, Butchar B, et al. Sedna: A BPEL-Based environment for visual Scientific Workflow Modeling [C]. Workflows for e-Science, London, Springer, 2007:428-449.
[87] Barga R. S, Jackson J, Araujo N, et al. Trident: Scientific Workflow Workbench for Oceanography [C]. in the Proc. of the 2008 IEEE Congress on Services,Honolulu, HI, IEEE Computer Society press, 2008:465-466.
[88] Deelman E. Looking into the Future of Workflow: The challenge Ahead[C]. Workflow for e-Science, London, Springer, 2007:475-481.
[89] Taylor I, Deelman E, Gannon D, et al. Workflow for e-Science [M]. London, Springer, 2007.
[90] Mcphillips T, Bowers S, Zinn D, et al. Scientific workflow design for mere mortals [J]. Future Generation Computer Systems, 2009,25(5):541-551.
[91] Barker A, Hemert J. V. Scientific Workflow: A Survey and Research Directions [C]. Parallel Processing and Applied Mathematics, LNCS 4976, 2008:746-753.
[92] Ludascher B, Goble C. Special Section on Scientific Workflows [J]. ACM SIGMOD Record, 2005,34(3):3-4.
[93] Zhao Y, Raicu I, Foster I. Scientific Workflow Systems for 21st Century, New Bottle or New Wine? [C], in the Proc. of the 2008 IEEE Congress on Services, Honolulu:HI, IEEE Computer Society press, 2008:467-471.
[94] Lin C, Lu S, Fei X, et al. A Reference Architecture for Scientific Workflow Management System and the VIEW SOA Solution [J]. IEEE Transaction on Services Computing, 2009,2(1):79-92.
[95] Ren K, Chen J, Xiao N, et al. Building Quick Service Query List(QSQL) to Support Automated Service Discovery for Scientific Workflow [J]. Concurrency and Computation: Practice & Experience, 2009,21(16):2099-2117.
[96] Gil Y, Deelman E, Ellisman M, et al. Examining the Challenges of Scientific Workflows [J]. Computer, 2007,40(12):24-32.
[97] Lud¨Ascher B, Weske M, Mcphillips T, et al. Scientific Workflows: Business as Usual? [C].Business Process Management, LNCS 5701, 2009:31-47.
[98] Tan W, Missier P, Madduri R, et al. Building Scientific Workflow with Taverna and BPEL: A Comparative Study in caGrid [C]. in the Proc. of Service-Oriented Computing (ICSOC 2008 Workshops), LNCS 5472, 2009:118-129.
[99] Aalst W. M. P. V. D. Workflow Patterns [J]. Distributed and Parallel Databases, 2003,14(1):5-51.
[100] Aalst W. M. P. V. D, Barros A P, Hofstede A. H. M. T, et al. Advanced Workflow Patterns [J]. Cooperative Information Systems, LNCS 1901,2000:18-29.
[101] Aalst W. M. P. V. D, Hofstede A. H. M. T. YAWL: Yet Another Workflow Language [J]. Information Systems, 2005,30(4):245-275.
[102] Agostini A, Michelis G. D, Petruni K. Keeping Workflow Models as Simple as Possible [C]. in the Proc. of the Workshop on Computer-Supported Coorperative Work, Petri Nets and Related Formalisms within the 15th International Conference on Application and Theory of Nets. Zaragoza, IEEE Computer Society press, 1994.
[103] Addis M, Ferris J, Greenwood M, et al. Experiences with e-Scienceworkflow specification and enactment in bioinformatics [C], in the Proc. of UK e-Scinece All Hands Meeting, 2003:459-467.
[104] Taylor D. I. Triana Generations [C]. in the Proc. of the 2nd IEEE International Conference on e-Science and Grid Computing, Amsterdam, Netherlands, IEEE Computer Society press, 2006:143-143.
[105] Erwin D. W. UNICORE: A Grid Computing Environment [J]. Euro-Par 2001 Parallel Processing, LNCS 2150, 2001:825-834.
[106] Couvares P, Kosar T, Roy A, et al. Workflow Management in Condor [C]. Workflow for e-science, London, Springer, 2007:357-375.
[107] Goodale T. Expressing Workflow in the Cactus Framework [C]. Workflow for e-Science, London, Springer, 2007: 416-427.
[108] Andrews T, Curbera F, Dholakia H, et al. Business Process Execution Language for Web Services Version 1.1 [S], 2003.
[109] Oinn T, Addis M, Ferris J, et al. Delivering Web Service Coordination Capability to Users [C]. in the Proc. of the 13th International World Wide Web conference on Alternate track papers & posters, New York, ACM press, 2004:438-439.
[110] Krishnan S, Wagstrom P, Laszewski G. V. GSFL: A Workflow Framework for Grid Services [R]. Technical Report Preprint ANL/MCS-P980-0802, Argonne National Laboratory, 2002.
[111] Amin K, Laszewski G. V, Hategan M, et al. GridAnt: A Client-Controllable Grid Workflow System [C]. in the Proc. of the 37th Hawaii International Conference on System Sciences, 2004:10-18.
[112] Hoheisel A, User Tools and Languages for Graph-based Grid Workflows [J]. Concurrency and Computation: Practice & Experience, 2006,18(10):1101-1113.
[113] Alt M, Hoheisel A, Pohl H-W, et al. A GridWorkflow Language Using High-Level Petri Nets [J]. Parallel Processing and Applied Mathematics, LNCS 3911, 2006:715-722.
[114] Ludascher B, Altintas I, Berkley C, et al. Scientific workflow management and the Kepler system [J]. Concurrency and Computation: Practice and Experience, 2005,18(10):1039-1065.
[115] Lee E. A, Neuendorffer S. MoML—A Modeling Markup Language in XML—Version 0.4 [R]. Technical Memorandum ERL/UCB M 00/12, 2000.
[116] Avery P, Foster I. GriPhyN Annual Report for 2003– 2004 [R]. 2004.
[117] Wang Y, Hu C, Huai J. A New Grid Workflow Description Language [C]. in the Proc. of the 2005 IEEE International Conference on Services Computing, IEEE Computer Society press, 2005:257-258.
[118] Woodman S, Parastatidis S, Webber J. Protocol-Based Integration Using SSDL and Pi-Calculus [C]. Workflow for e-Science, London, Springer, 2007:227-243.
[119] Huang C, Huang Q. SWFL 2.0 Specification Service Workflow Language[R]. United Kingdom: Cariff University, 2006: 1-95.
[120] Malinova A, Gocheva-Ilieva S. Using the Business Process Execution Language for Managing Scientific Processes [J]. Information Technologies and Knowledge, 2008,2(3):257-261
[121] Callahan S. P, Freire J, Santos E, et al. VisTrails: visualization meets data management [C]. in the Proc. of the 2006 ACM SIGMOD international conference on Management of data. Chicago, ACM press, 2006:745-747.
[122] XBaya: A Graphical Workflow Composer for Web Services [R]. Technical Report 004, LEAD, 2006.
[123] Wroe C, Stevens R, Goble C, et al. A Suite of DAML+OIL Ontologies to Describe Bioinformatics Web Services and Data [J]. International Journal of Cooperative Information Systems, 2003,12(2):197-224.
[124] Chen J, Yang Y. A taxonomy of grid workflow verification and validation [J]. Concurrency and Computation: Practice & Experience, 2008,20(4):347-360.
[125] Adlst W. V. D, Hee K. V. Workflow Management: Models, Methods and System [M]. MIT press, Cambridge, MA, 2002.
[126] Buyya R, Murshed M, Abramsom D, et al. Scheduling parameter sweep applications on global Grids: a deadline and budget constrained cost–time optimization algorithm [J]. Software—Practice & Experience, 2005,35(5):491-512.
[127] Ramakrishnan A, Singh G, Zhao H, et al. Scheduling Data-Intensive Workflows onto Storage-Constrained Distributed Resources [C]. in the Proc. of the 7th IEEE International Symposium on Cluster Computing and the Grid (CCGrid’07), IEEE Computer Society press, 2007:401-409.
[128] Wieczorek M, Prodan R, Hoheisel A, Taxonomies of the Multi-criteria Grid Workflow Scheduling Problem [J]. Grid Middleware and Services, Springer US, 2008,3:237-264.
[129] Singh H, Youssef A. Mapping and Scheduling Heteogeneous Task Graphs using Genetic Algorithm [C]. in the Proc. of the 5th IEEE Heterogeneous Computing Workshop (HCW’96), IEEE Computer Society press, 1996:86-97.
[130] Freund R, Gherrity M, Campbell M, et al. Scheduling Resources in Multi-User, Heterogeneous, Computing Environments with SmartNet [C]. In the Proc. of the 7th IEEE Heterogeneous Computing Workshop (HCW '98), Oralando USA, IEEE Computer Society press, 1998:184-199.
[131] Legrand A, Marchal L, Casanova H. Scheduling Distributed Applications: the SimGrid Simulation Framework [C]. in the Proc. of the 3rd International Symposium on Cluster Computing and the Grid (CCGrid'03), IEEE Computer Society press, 2003:138-145.
[132]杜晓丽,蒋昌俊,徐国荣等.一种基于模糊聚类的网格DAG任务图调度算法[J].软件学报, 2006,17(11):2277-2288.
[133]林伟伟,齐德昱,李拥军等.树型网格计算环境下的独立任务调度[J].软件学报,2006,17(11):2352-2361.
[134] Yong W, Chun-Ming H, Zong-Xia D. QoS-Awared Grid Workflow Schedule[J]. Journal of Software, 2006,17(11):2341-2351.
[135] Yu J, Buyya R. Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms [J]. Science Programming, 2006,14(3-4):217-230.
[136] Yu J, Buyya R. A Taxonomy of Workflow Management Systems for Grid Computing [J]. Journal of Grid Computing, LNCS 3-4, 2006:171-200.
[137] Chen J, Yang Y. Adaptive Selection of Necessary and Sufficient Checkpoints for Dynamic Verification of Temporal Constraints in Grid Workflow Systems [J]. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 2007, 2(2):1-25.
[138] Plankensteiner K, Prodan R, Fahringer T, Fault-tolerant behavior in state-of-the-art Grid Workflow Management Systems [R]. TR-0091, Core-GRID, 2007.
[139] Crawl D, Altintas I. A Provenance-Based Fault Tolerance Mechanism for Scientific Workflows [J]. Provenance and Annotation of Data and Processes, LNCS 5272, 2008:152-159.
[140] Miles S, Groth P, Deelman E, et al. Provenance: The Bridge Between Experiments and Data [J]. Computing in Science & Engineering, 2008,10(3):38-46.
[141] Davidson S, Cohen-Boulakia S, Eyal A, et al. Provenance in Scientific Workflow Systems [J]. IEEE Data Engineering Bulletin, 2007,30(4):44-50.
[142] Bowers S, Mcphillips T. M, Ludascher B. Provenance in collection-oriented scientific workflows [J]. Concurrency and Computation: Practice & Experience, 2008,20(5):519-529.
[143] Anand M. K, Bowers S, Mcphillips T, et al. Exploring Scientific Workflow Provenance Using Hybrid Queries over Nested Data and Lineage Graphs [C]. in the Proc. of the 21st International Conference on Scientific and Statistical Database Management, New Orleans, USA, Springer, 2009: 237-254.
[144] Simmhan Y. L, Plale B, Gannon D. Karma2: Provenance Management for Data Driven Workflows [J]. International Journal of Web Services Research, 2008,5(2):1-22.
[145] Vijayakumar N. N, Plale B. Tracking Stream Provenance in Complex Event Processing Systems for Workflow-Driven Computing [C]. in VLDB’07, Vienna, Austria, ACM press, 2007.
[146] Special Issue: The First Provenance Challenge [J]. Concurrency and Computing: Practice and Experience, 2008,20:409-418.
[147] Simmhan Y. L, Plale B, Gannon D. A Survey of Data Provenance in e-Science [J], ACM SIGMOD Record, 2005,34(3):31-36.
[148] Roure D. D, Goble C. Six Principles of Software Design to Empower Scientists [J]. IEEE Software, 2008:26-40.
[149] Turuncoglu U, Murphy S, Technical Summary and Progress Report for a Kepler-based Modeling Workflow System [R], 2009, http://esmfcontrib.cvs.sourceforge.net/viewvc/esmfcontrib/workflow.
[150] Liu M, He H, Sun X, et al. Scientific Workflow Approach (Kepler) for Carbon Flux Data Processing [C]. in the Proc. of the 2nd International Conference on Intelligent Computation Technology and Automation (ICITA’09). Changsha, China, IEEE Computer Society press, 2009:694-697.
[151] Lin A. W, Peltier S. T, Grethe J. S, et al. Case studied on the use of workflow technologies for scientific analysis: the biomedical informatics research networkl and the telescience project [C]. workflow for e-science, London, Springer, 2007:109-125.
[152] Zeng J, Du Z, Hu C, et al. CROWN FlowEngine: A GPEL-Based Grid Workflow Engine [J]. High Performance Computing and Communications , LNCS 4782, 2007:249-259.
[153] Cancan Liu, Weimin Zhang, Zhigang Luo, et al. Managing Large-Scale Scientific Computing in Ensemble Prediction Using BPEL [C]. in the Proc. of the IEEE International Symposium on Parallel and Distributed Processing with Applications. 2009. Chengdu, China: IEEE Computer Society press, 2009: 94-101.
[154]刘灿灿.数值天气预报系统中网格工作流的研究与应用[D].长沙:国防科学技术大学, 2005.
[155]王黎维,黄泽谦,罗敏等.集成对象代理数据库的SWF服务框架中的数据跟踪[J].计算机学报, 2008,31(5):721-732.
[156]苏明明,宋文. 2007-2008年国外SWF研究进展[J].图书馆建设, 2009,7:96-100.
[157] Buizza R. The Value of Probabilistic Prediction [J]. Atmospheric Science Letters, 2008,9(2):36-42.
[158] Molteni F, Buizza R, Palmer T. N, et al. The ECMWF Ensemble Prediction System: Methodology and validation [J]. Quarterly Journal of the Royal Meteorological Society, 1996,122(529):73-119.
[159] Wu Y, Doshi P. Making BPEL Flexible-Adapting in the Context of Coordination Constraints Using WS-BPEL [C]. in the Proc. of WWW 2008. Beijing, ACM press, 2008:1199-1200.
[160] Pasley J. How BPEL and SOA Are Changing Web Services Development [J]. IEEE Internet Computing, 2005,9(3):60-67.
[161] Louridas P. Orchestrating Web Services with BPEL [J]. IEEE Software, 2008,9(3):85-87.
[162] Nakajima S. Model-Checking Behavioral Specification of BPEL Applications [J]. Electronic Notes in Theoretical Computer Science, 2006,151(2):89-105.
[163] Slomiski A. On Using BPEL Extensibility to Implement OGSI and WSRF Grid Workflows [J]. Concurrency and Computation: Practice & Experience, 2006,18(10):1229-1241.
[164] Slominski A. Adapting BPEL to Scientific Workflows [C]. Workflows for e-Science , London, Springer, 2007:208-226.
[165] OASIS Standard, Web Services Business Process Execution Language Version 2.0 [S]. 2006.
[166] Hobona G, Fairbairn D, Hiden H, et al. Orchestration of Grid-Enabled Geospatial Web Services in Geoscientific Workflows [J]. IEEE Transaction on Automation Science and Engineering, 2009,7(2):407-411.
[167] Moser O, Rosenberg F, Dustdar S. Non-Intrusive Monitoring and Service Adaptation for WS-BPEL [C]. in the Proc. of the 17th international conference on World Wide Web (WWW’08). Beijing, ACM press, 2008:815-824.
[168] Hwang S-Y, Lim E-P, Lee C-H, et al. Dynamic Web Service Selection for Reliable Web Service Composition [J]. IEEE Transaction on Service Computing, 2008,1(2):104-116.
[169] Fu X, Bultan T, Su J. Analysis of Interacting BPEL Web Services[C]. in the Proc. of the 13th international conference on World Wide Web (WWW’04). New York, ACM press, 2004:621-630.
[170] Koning M, Sun C-A, Sinnema M, et al. VxBPEL: Supporting variability for Web services in BPEL [J]. Information and Software Technology, 2009, 1:58-269.
[171] Pautasso C. RESTful Web service composition with BPEL for REST [J]. Data & Knowledge Engineering, 2009,68(9):851-866.
[172]王紫瑶,南俊杰,段紫辉等. SOA核心技术及应用[M],北京:电子工业出版社.2008.
[173] Emmerich W, Butchart B, Chen L, et al. Grid Service Orechestration Using the Business Process Execution Language (BPEL) [J]. Grid Computing, 2006,3(3-4):283-304.
[174] D?rnemann T, Friese T, Herdt S, et al. Grid Workflow Modelling Using Grid-Specific BPEL Extensions [C]. in the Proc. of German e-Science Conference, Baden, 2007.
[175] Wang Y, Huai J. Comparative Analysis of BPEL4WS and a Grid Workflow Language Called GPEL [C]. in the Proc. of the 2005 IEEE International Conference onServices Computing (SCC’05). IEEE Computer Society press, 2005:253-254.
[176] Gunarathne T, Herath C, Chinthaka E, et al. Experience with Adapting a WS-BPEL Runtime for eScience Workflows [C]. in the Proc. of the 5th Grid Computing Environments Workshop(GCE’09). Portland, ACM press, 2009.
[177] Ma R, Wu Y, Meng X, et al. Grid-enabled Workflow Management System Based on BPEL [J]. International Journal of High-Performance Computing Applications, 2008,22(2):1-12.
[178] Lucchi R, Mazzara M. A pi-calculus based semantics for WS-BPEL [J]. Journal of Logic and Algebraic Programming, 2007,70(1):96-118.
[179] D¨Ornemann T, Juhnke E, Freisleben B. On-Demand Resource Provisioning for BPEL Workflows Using Amazon’s Elastic Compute Cloud [C]. in the Proc. of the 9th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'09). Shanghai, IEEE Computer Society press, 2009:140-147.
[180] Targino R, Cavalcanti M. C, Mattoso M. An Environment to Define and Execute In-Silico Workflows Using Web Services [J]. Data Integration in the Life Sciences, LNCS 3615, 2005:288-291.
[181] Lee W, Mcgough A. S, Darlington J. Performance Evaluation of the GridSAM Job Submission and Monitoring System[R]. in UK eScience Program All Hands Meeting, 2005.
[182] Job Submission Description Language (JSDL) Specification, Version 1.0 [S]. 2005.
[183]金海,袁平鹏.网格计算[M].北京:电子工业出版社,2004.
[184] Ardagna D, Comuzzi M, Mussi E, et al. PAWS: A Framework for Executing Adaptive Web-Service Processes [J]. IEEE software, 2007,11-12:39-47.
[185] Chunga P. W. H, Cheunga L, Staderb J, et al. Knowledge-based process management—an approach to handling adaptive workflow [J]. Knowledge-Based Systems, 2003,16(3):149-160.
[186] Edmond D, Hofstede A. H. M. T. A reflective infrastructure for workflow adaptability [J]. Data & Knowledge Engineering , 2000,34(3):271-304.
[187]冯兴智.基于服务质量的动态Web服务组合关键技术研究[D].长沙:国防科学技术大学,2007.
[188]任开军.基于QSQL的高效语义Web服务发现及合成关键技术研究[D].长沙:国防科学技术大学,2007.
[189] Reich C, Banholzer M, Buyya R, et al. Engineering an Autonomic Container for WSRF-based Web Services [C]. in the Proc. of the 15th International Conference on Advanced Computing and Communications (ADCOM 2007), Guwahati, India, IEEE Computation Press, 2007.
[190]胡春华.面向QoS需求的Web服务工作流构造模型及调度算法研究[D].长沙:中南大学, 2007.
[191]杨艳萍.自动Web服务组合关键技术研究[D].长沙:国防科学技术大学,2007.
[192]陈丁剑.基于语义的Web服务发现和组合技术研究[D].西安:西北工业大学, 2006.
[193]范小芹,蒋昌俊,王俊丽等.随机QoS感知的可靠Web服务组合[J].软件学报, 2009,20(3):546-556.
[194]王勇,代桂平,侯亚荣.信任感知的组合服务动态选择方法[J].计算机学报, 2009,32(8):1668-1675.
[195] Krishnan S, Gannon D. Checkpoint and Restart for Distributed Components in XCAT3[C]. in the Proc. of the 5th IEEE/ACM International Workshop on Grid Computing (GRID'04), Pittsburgh, IEEE Computer Society press, 2004:281-288.
[196] Chen J, Yang Y. Multiple states based temporal consistency for dynamic verification of fixed-time constraints in Grid workflow systems [J]. Concurrency and Computation: Practice & Experience, 2006,19(7):965-982.
[197] Buyya R, Abramson D, Giddy J, et al. Economic models for resource management and scheduling in Grid computing [J]. Concurrency and Computation: Practice and Experience, 2002,14(13-15):1507-1542.
[198] Garg S, Konugurthi P, Buyya R. A Linear Programming Driven Genetic Algorithm for Meta-Scheduling on Utility Grids [C]. in the Proc. of the 16th International Conference on Advanced Computing and Communications (ADCOM 2008), Chennai, IEEE Computer Society press, 2009:19-26.
[199] Razek R. H, Diab A. M, Hafez S. M, et al. Time-Cost-Quality Trade-off Software by using Simplified Genetic Algorithm for Typical repetitive Construction Projects [J], World Academy of Science, Engineering and Technology 61, 2010:312-321.
[200]王远,范玉顺.工作流时序约束模型分析与验证方法[J].软件学报,2007, 18(9): 2153-2161.
[201]杜彦华,范玉顺.基于生成图的工作流多过程动态时序一致性验证方法[J].电子学报, 2009,37(10): 2181-2187.
[202] Akkan C, Drexl A, Kimms A. Network decomposition-based benchmark results for the discrete time–cost tradeoff problem [J]. European Journal of Operational Research, 2005,165(2):339-358.
[203] Hazir O, Haouari M, Erel E. Discrete time/cost trade-off problem: A decomposition-based solution algorithm for the budget version [J]. Computers & Operations Research 2010,37(4):649-655.
[204] Garg S. K, Buyya R, Siegel H. J. Time and cost trade-off management forscheduling parallel applications on Utility Grids [J]. Future Generation Computer Systems, 2009,26(8):1344-1355.
[205] Abramson D, Buyya R. Giddy J. A computational economy for grid computing and its implementation in the Nimrod-G resource broker [J]. Future Generation Computer Systems, 2002,18(8):1061-1074.