面向QoS的网格应用—系统平衡型优化调度方法研究
|
摘要
随着网格系统逐步从科研领域走向更加广泛的商业领域,面向服务的网格已成为主流趋势,网格任务调度的研究重点是“提供非凡的服务质量保证(Non-trivial QoS Guarantee)"。网格系统提供QoS保证能力时明确区分了应用QOS指标和系统QoS指标,当前面向QoS的调度策略通常单独考虑网格应用的QoS需求或仅仅优化网格系统的目标,亟需一个既能保障应用QoS指标又能适应网格系统内在需求的合理、高效和公平的调度决策机制。因此,面向QoS的网格应用—系统平衡型优化调度研究具有良好理论价值和实用意义。
网格系统和网格应用从各自的角度提出不同的QoS指标,之间不存在简单直接映射关系,优化系统QoS指标的策略在实际中可能无法满足用户所提出的QOS需求,优化应用QoS指标则可能损害系统性能参数。基于对应用与系统双方QoS指标之间关联特性的深入分析,本文开展平衡应用与系统QoS指标的网格优化调度研究,重点考虑的应用QoS指标包括用户通常最关注的应用完成时间和执行费用,系统QoS指标则包括系统负载均衡程度与计算经济理论中最常用的系统目标—系统总收益,以期实现用户QoS保障并满足网格系统的内在需求。论文的主要工作和创新如下：(1)面向通用网格应用的平衡型优化调度策略
针对网格应用和网格系统双方的QoS需求存在一定冲突,提出了一种基于序贯博弈的平衡型优化策略SGPUBO。 SGPUBO以应用完成时间和系统的负载均衡程度为优化目标,将调度过程分为活动分发和处理器分配两个阶段,首先按照资源性能进行活动分发,之后基于已接纳活动的执行情况在资源内部实施处理器分配,通过两个阶段相互迭代求解获得最终分配方案。通过大量实验分析可知：SGPUBO具有集中分配特性,基于该特性,通过采用站点一活动队列的截尾处理方案,提出了可进一步优化应用完成时间的SGPUBOwTM算法。实验结果表明,与经典调度算法Min-Min和Sufferage相比,SGPUBOwTM可在短时间内求解处理大规模网格应用调度问题,并且在网格应用的完成时间和网格系统的负载均衡等方面都有更好的表现。(2)面向工作流应用的平衡型优化调度策略
针对经典的工作流逆向分层方案中均匀分配截止期宽裕时间将造成宽裕时间浪费这一缺陷,提出了一个非均匀浮差的工作流截止期分配预处理方案DBL_UnevenExt。为了优化截止期分配的活动选择,DBL_UnevenExt基于“服务级差性价比”实施截止期分配,并参考“服务级差”中“时间性能差”的分配需求,以非均匀方式分配工作流截止期宽裕时间。实验表明,DBL_UnevenExt的截止期分配方案可将截止期宽裕时间分配给更适当的活动,扩展了工作流应用子任务的费用优化区间。在采用DBL_UnevenExt实施工作流预处理的基础上,对每一个子工作流以实时模式进行基于序贯博弈的平衡型优化,进而研究提出了一个费用目标下工作流应用的平衡型优化算法DBLUCUBO。实验结果表明：DBLUCUBO在工作流执行费用和网格系统的负载均衡程度方面的性能表现显著优于同类经典工作流调度算法。(3)面向全局收益优化的管理域内平衡型调度策略
针对当前管理域内的调度大多沿用传统分布式系统的本地调度器、不能很好地适应商业应用环境中对公平性和合理性的新需求这一问题,提出了一种最大化管理域全局收益的域内调度策略CGBMA。该策略在建立管理域内的合作博弈模型并定义管理域的“平衡状态”基础上,通过理论分析得出管理域系统处于平衡状态时可以实现全局收益最大的结论。据此,将管理域内的调度问题转化为任务零售价、任务市场价和任务分配量的更新迭代过程。实验结果表明：与Proportional Share、Double Auction算法相比,CGBMA可实现管理域的最大全局收益。针对上述实验分析同时发现,当任务规模增大时,CGBMA的市场淘汰特性将愈加明显而致使负载不均衡加剧。为此,在CGBMA算法的基础上进一步实施负载均衡优化,依据资源的负载情况来动态调整合作博弈模型的关键参数,并提出了一个全局收益优化的负载平衡调度算法CGBMAFI。通过大量实验表明,管理域采用CGBMAFI进行调度时,本身可实现全局收益的优化,而用户则可以获得更优的应用执行时间和性价比。
With Grid technologies progressing towards a commercial and service-oriented paradigam, research focus is shifted to investigate how to ensure Grid to provide non-trivial Quality of Service (QoS). QoS guarantee in Gird makes a clear distinction between user's metrics and system's metrics. The existing QoS-oriented scheduling approaches usually are dedicated to certain user's criteria or only pursuit system-centric performance criteria. To this end, it is urgent to design a reasonable, effiecient and fair scheduling approach which could satisfy users'QoS demands and also meet systems'internal requirements. Therefore, QoS-based application-system balanced optimization scheduling in Grids becomes an important issue with theoretical and practical value.
The characteristics of Grid QoS such as diversity and interplay bring up great challenge for QoS-oriented schechduling, especially sometimes QoS of user and system conflict. In order to guarantee and improve the QoS requirements of user and system in Grid environment, analyzing current user's most concerned criteria including time and cost, and aiming at system's criteria including workload fairness and global benefit which is usually the most important criteria in economy theory, this thesis deeply investigates efficient and effective Grid application-system balanced scheduling mechanisms. The main research content and contributions are as follows:
1. Balanced-based optimization strategy for scheduling general Grid application
According to the fact that QoS requirements of user and system usually remain conflicts, a sequential-game-based balanced optimization scheduling scheme which optimizes the criteria of application's finishing time and Grid system's workload fairness, is presented. SGPUBO gets scheduling solution in a sequential game, which is turned into an iteration of activity distribution and processor allocation. In the first phase, activities'exectuted performances are compared among different sites, and in the second phase, processors are allocated on the basis of the conditions of admitting activities at each site. The simulation experimental results show the allocation concentration tendency of SGPUBO. Applying Tail Migration strategy, SGPUBOwTM is proposed which could achieve better performance on makespan. Comparised with two traditional leading algorithms Min-Min and Sufferage on performance, SGPUBOwTM leads to better application time and system load fairness in an efficient way.
2. Balanced-based Optimization strategy for scheduling workflow application
Considering the fact that traditional workflow preprocess scheme DBL, which allocates the total time float by an equal means, could lead to waste of Deadline Remaining, an uneven slack deadline assignment scheme based on DBL(DBL_UnevenExt) scheme is proposed. The DBL_UnevenExt algorithm makes choises of Deadline Remaining allocation based on Performance Cost Ratio Distance and Time Requirement Distance between Service Levels. The experimental results indicate that DBL_UnevenExt could enlarge cost optimization intervals of activities by allocating the Deadline Remaining to the more suitable activity. Based on workflow preprocess with DBL_UnevenExt, a novel approach called DBLUCUBO is proposed to realize workflow balanced optimization aiming of minimzing application cost and system workload fairness. The approach makes Full-ahead-planning for every subworkflow based on sequtial game scheme in Just-in-time mode. The experimantal results show that DBLUCUBO could optimize the cost of workflow application and improve fairness of grid system.
3. Balanced-based Optimization scheme for intra-domain scheduling
Exsiting intra-domain scheduling solutions usually resort to traditional local scheduling scheme, which could not satisfy some new requirements of intra-domain scheuling in commercial grid. From this prospective, a Fair Global Benefit Maximization intra-site Allocation CGMBA is proposed. Based on the cooperative model of intra-domain and the definition admistrate domain's'balanced state', a theoretical proof that the system would gain maximal global benefit if and only if it was in a balanced state is presented. On the basis of this conclusion, Grid task-bundle allocation problem is turned into an iteration process involving retail price, market price and assignment amount of tasks in CGBMA. The experimental results indicate that CGBMA could provide an effective solution with global benefit optimization. Meanwhile, owing to CGBMA's self-selection property which is inherited from market model, load unfairness condition turns worse with the scale of task bundle getting larger. A Cooperative-game-based Global Benefit Maximization Allocation with Fairness Improvement (CGBMAFI) is then presented. The experimental results indicate that administration domain could get better optimize global benefit and user could obtain better executing performance and Performance Cost Ratio with CGBMAFI.
网格系统和网格应用从各自的角度提出不同的QoS指标,之间不存在简单直接映射关系,优化系统QoS指标的策略在实际中可能无法满足用户所提出的QOS需求,优化应用QoS指标则可能损害系统性能参数。基于对应用与系统双方QoS指标之间关联特性的深入分析,本文开展平衡应用与系统QoS指标的网格优化调度研究,重点考虑的应用QoS指标包括用户通常最关注的应用完成时间和执行费用,系统QoS指标则包括系统负载均衡程度与计算经济理论中最常用的系统目标—系统总收益,以期实现用户QoS保障并满足网格系统的内在需求。论文的主要工作和创新如下：(1)面向通用网格应用的平衡型优化调度策略
针对网格应用和网格系统双方的QoS需求存在一定冲突,提出了一种基于序贯博弈的平衡型优化策略SGPUBO。 SGPUBO以应用完成时间和系统的负载均衡程度为优化目标,将调度过程分为活动分发和处理器分配两个阶段,首先按照资源性能进行活动分发,之后基于已接纳活动的执行情况在资源内部实施处理器分配,通过两个阶段相互迭代求解获得最终分配方案。通过大量实验分析可知：SGPUBO具有集中分配特性,基于该特性,通过采用站点一活动队列的截尾处理方案,提出了可进一步优化应用完成时间的SGPUBOwTM算法。实验结果表明,与经典调度算法Min-Min和Sufferage相比,SGPUBOwTM可在短时间内求解处理大规模网格应用调度问题,并且在网格应用的完成时间和网格系统的负载均衡等方面都有更好的表现。(2)面向工作流应用的平衡型优化调度策略
针对经典的工作流逆向分层方案中均匀分配截止期宽裕时间将造成宽裕时间浪费这一缺陷,提出了一个非均匀浮差的工作流截止期分配预处理方案DBL_UnevenExt。为了优化截止期分配的活动选择,DBL_UnevenExt基于“服务级差性价比”实施截止期分配,并参考“服务级差”中“时间性能差”的分配需求,以非均匀方式分配工作流截止期宽裕时间。实验表明,DBL_UnevenExt的截止期分配方案可将截止期宽裕时间分配给更适当的活动,扩展了工作流应用子任务的费用优化区间。在采用DBL_UnevenExt实施工作流预处理的基础上,对每一个子工作流以实时模式进行基于序贯博弈的平衡型优化,进而研究提出了一个费用目标下工作流应用的平衡型优化算法DBLUCUBO。实验结果表明：DBLUCUBO在工作流执行费用和网格系统的负载均衡程度方面的性能表现显著优于同类经典工作流调度算法。(3)面向全局收益优化的管理域内平衡型调度策略
针对当前管理域内的调度大多沿用传统分布式系统的本地调度器、不能很好地适应商业应用环境中对公平性和合理性的新需求这一问题,提出了一种最大化管理域全局收益的域内调度策略CGBMA。该策略在建立管理域内的合作博弈模型并定义管理域的“平衡状态”基础上,通过理论分析得出管理域系统处于平衡状态时可以实现全局收益最大的结论。据此,将管理域内的调度问题转化为任务零售价、任务市场价和任务分配量的更新迭代过程。实验结果表明：与Proportional Share、Double Auction算法相比,CGBMA可实现管理域的最大全局收益。针对上述实验分析同时发现,当任务规模增大时,CGBMA的市场淘汰特性将愈加明显而致使负载不均衡加剧。为此,在CGBMA算法的基础上进一步实施负载均衡优化,依据资源的负载情况来动态调整合作博弈模型的关键参数,并提出了一个全局收益优化的负载平衡调度算法CGBMAFI。通过大量实验表明,管理域采用CGBMAFI进行调度时,本身可实现全局收益的优化,而用户则可以获得更优的应用执行时间和性价比。
With Grid technologies progressing towards a commercial and service-oriented paradigam, research focus is shifted to investigate how to ensure Grid to provide non-trivial Quality of Service (QoS). QoS guarantee in Gird makes a clear distinction between user's metrics and system's metrics. The existing QoS-oriented scheduling approaches usually are dedicated to certain user's criteria or only pursuit system-centric performance criteria. To this end, it is urgent to design a reasonable, effiecient and fair scheduling approach which could satisfy users'QoS demands and also meet systems'internal requirements. Therefore, QoS-based application-system balanced optimization scheduling in Grids becomes an important issue with theoretical and practical value.
The characteristics of Grid QoS such as diversity and interplay bring up great challenge for QoS-oriented schechduling, especially sometimes QoS of user and system conflict. In order to guarantee and improve the QoS requirements of user and system in Grid environment, analyzing current user's most concerned criteria including time and cost, and aiming at system's criteria including workload fairness and global benefit which is usually the most important criteria in economy theory, this thesis deeply investigates efficient and effective Grid application-system balanced scheduling mechanisms. The main research content and contributions are as follows:
1. Balanced-based optimization strategy for scheduling general Grid application
According to the fact that QoS requirements of user and system usually remain conflicts, a sequential-game-based balanced optimization scheduling scheme which optimizes the criteria of application's finishing time and Grid system's workload fairness, is presented. SGPUBO gets scheduling solution in a sequential game, which is turned into an iteration of activity distribution and processor allocation. In the first phase, activities'exectuted performances are compared among different sites, and in the second phase, processors are allocated on the basis of the conditions of admitting activities at each site. The simulation experimental results show the allocation concentration tendency of SGPUBO. Applying Tail Migration strategy, SGPUBOwTM is proposed which could achieve better performance on makespan. Comparised with two traditional leading algorithms Min-Min and Sufferage on performance, SGPUBOwTM leads to better application time and system load fairness in an efficient way.
2. Balanced-based Optimization strategy for scheduling workflow application
Considering the fact that traditional workflow preprocess scheme DBL, which allocates the total time float by an equal means, could lead to waste of Deadline Remaining, an uneven slack deadline assignment scheme based on DBL(DBL_UnevenExt) scheme is proposed. The DBL_UnevenExt algorithm makes choises of Deadline Remaining allocation based on Performance Cost Ratio Distance and Time Requirement Distance between Service Levels. The experimental results indicate that DBL_UnevenExt could enlarge cost optimization intervals of activities by allocating the Deadline Remaining to the more suitable activity. Based on workflow preprocess with DBL_UnevenExt, a novel approach called DBLUCUBO is proposed to realize workflow balanced optimization aiming of minimzing application cost and system workload fairness. The approach makes Full-ahead-planning for every subworkflow based on sequtial game scheme in Just-in-time mode. The experimantal results show that DBLUCUBO could optimize the cost of workflow application and improve fairness of grid system.
3. Balanced-based Optimization scheme for intra-domain scheduling
Exsiting intra-domain scheduling solutions usually resort to traditional local scheduling scheme, which could not satisfy some new requirements of intra-domain scheuling in commercial grid. From this prospective, a Fair Global Benefit Maximization intra-site Allocation CGMBA is proposed. Based on the cooperative model of intra-domain and the definition admistrate domain's'balanced state', a theoretical proof that the system would gain maximal global benefit if and only if it was in a balanced state is presented. On the basis of this conclusion, Grid task-bundle allocation problem is turned into an iteration process involving retail price, market price and assignment amount of tasks in CGBMA. The experimental results indicate that CGBMA could provide an effective solution with global benefit optimization. Meanwhile, owing to CGBMA's self-selection property which is inherited from market model, load unfairness condition turns worse with the scale of task bundle getting larger. A Cooperative-game-based Global Benefit Maximization Allocation with Fairness Improvement (CGBMAFI) is then presented. The experimental results indicate that administration domain could get better optimize global benefit and user could obtain better executing performance and Performance Cost Ratio with CGBMAFI.
引文
[1]I. T. Foster, C. Kesselman. The Grid:Blueprint for a New Computing Infrastructure[M]. San Francisco, CA:Morgan Kaufmann,1998.
[2]I. T. Foster, C. Kesselman, S. Tuecke. The Anatomy of the Grid:Enabling Scalable Virtual Organizations [J]. International Journal of High Performance Computing Applications,2001,15(3):200-222.
[3]I. T. Foster, C. Kesselman. The Grid 2:Blueprint for a New Computing Infrastructure[M]. San Francisco, CA:Morgan Kaufmann,2004.
[4]V. Breton, A. E. Solomonides, R. H. McClatchey. A Perspective on the Healthgrid Initiative [A]. In:Proceedings of the 4th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2004) [C]. Chicago, Illinois, USA:IEEE Computer Society,2004:434-439.
[5]J.-M. Pierson, L. Brunie, C. Dhaenens et al. Grid for Geno-Medicine:A Glimpse on the GGM Project[A]. In:Proceedings of the 5th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2005)[C]. Cardiff, UK: IEEE Computer Society,2005:527-528.
[6]M. A. Gutierrez, S.H.G. Lage, J. Lee et al. A Computer-Aided Diagnostic System using a Global Data Grid Repository for the Evaluation of Ultrasound Carotid Images[A]. In:Proceedings of the Seventh IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2007)[C]. Rio de Janeiro, Brazil:IEEE Computer Society,2007:840-845.
[7]D. Sulakhe, A. Rodriguez, M. Wilde et al. Using Multiple Grid Resources for Bioinformatics Applications in GADU[A]. In:Proceedings of the Sixth IEEE/ACM International Symposium on Cluster Computing and the Grid Workshops (CCGrid 2006)[C]. Singapore:IEEE Computer Society,2006:41.
[8]J. Zhu, A. Guo, Z. H. Lu et al. Analysis of the Bioinformatics Grid Technique Applications in China[A]. In:Proceedings of the Sixth IEEE/ACM International Symposium on Cluster Computing and the Grid Workshops(CCGrid 2006)[C]. Singapore:IEEE Computer Society,2006:44.
[9]V. Kasam, J. Salzemann, N. Jacq et al. Large Scale Deployment of Molecular Docking Application on Computational Grid infrastructures for Combating Malaria[A]. In:Proceedings of the Seventh IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2007)[C]. Rio de Janeiro, Brazil:IEEE Computer Society,2007:691-700.
[10]何儒汉,金海,廖振松等.ChinaGrid图像处理网格平台中的语义信息服务研究.计算机研究与发展,2006,43(5)：821-827.
[11]N. Chrisochoides, A. Fedorov, A. Kot et al. Imaging and visual analysis-Toward Real-Time Image Guided Neurosurgery Using Distributed and Grid Computing[A]. In:Proceedings of the ACM/IEEE SC2006 Conference on High Performance Networking and Computing[C]. Tampa, FL, USA:ACM,2006:76.
[12]J. Tian, K. K. Ma. Super-resolution Imaging Using Grid Computing[A]. In: Proceedings of the Seventh IEEE/ACM International Symposium on Cluster Computing and the Grid(CCGrid 2007)[C]. Rio de Janeiro, Brazil:IEEE Computer Society,2007:293-300.
[13]TeraGrid[EB/OL].:http://www.teragrid.org/.2010-12-20.
[14]European Grid[EB/OL].:http://www.egi.eu/.2010-6-15.
[15]R. P. Souto, R. B. Avila, P. O. A. Navaux et al. Processing Mesoscale Climatology in a Grid Environment[A]. In:Proceedings of the Seventh IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2007)[C]. Rio de Janeiro, Brazil:IEEE Computer Society,2007:363-370.
[16]W3C. Web Services Architecture[EB/OL].: http://www.w3.org/TR/2004/NOTE-ws-arch-20040211/,2008-10-20.
[17]K. Czajkowski, I. T. Foster, C. Kesselman. Resource Co-Allocation in Computational Grids [A]. In:Proceedings of the Eighth International Symposium on High Performance Distributed Computing (HPDC 1999)[C]. Redondo Beach, California, USA:IEEE Conputer Society,1999:219-228.
[18]I. T. Foster, C. Kesselman, C. Lee et al. A Distributed Resource Management Architecture that Supports Advance Reservation and Co-Allocation. In:Proceedings of International Workshop on Quality of Service, London, UK:IEEE Computer Society,1999:27-36.
[19]I. T. Foster. What is the Grid? A Three Point Checklist[J]. GRID Today,2002,1(6): 20-27.
[20]Platform Computing, Inc. Platform LSF [EB/OL].: http://www.platform.com/Products/Platform.LSF.Family,2010-12-20.
[21]Cluster Resources, Inc. Maui Cluster Scheduler [EB/OL].: http://www.clusterresources.com/pages/products/maui-cluster-scheduler.php 2010-12-20.
[22]Altair Engineering, Inc. PBS Professional [EB/OL].: http://www.altair.com/software/pbspro.htm,2010-12-20.
[23]Sun Microsystems, Inc. Grid Engine [EB/OL].:http://gridengine.sunsource.net. 2010-12-20.
[24]G. Singh, C. Kesselman, and E. Deelman. Application-Level Resource Provisioning on the Grid[A]. In:Proceedings of the Second International Conference on e-Science and Grid Technologies (e-Science 2006)[C]. Amsterdam, The Netherlands: IEEE Computer Society,2006:83.
[25]J. Yu and R. Buyya. Scheduling Scientific Workflow Applications with Deadline and Budget Constraints using Genetic Algorithms [J]. Scientific Programming,2006, 14(3-4):217-230.
[26]V. T'kindt and J. Billaut. Multicriteria Scheduling[M]. Berlin:Springer Verlag,2002.
[27]H. Zhao, E. Tsiakkouri, R. Sakellariou et al. Scheduling Workflows with Budget Constraints [A]. In:Proceedings of the CoreGRID Workshop Integrated research in Grid Computing[C]. Pisa, Italy,2005:347-357.
[28]I. T. Foster, C. Kesselman. Globus:A Metacomputing Infrastructure Toolkit[J]. International Journal of Supercomputer Applications and High Performance Computing,1997,11(2):115-128.
[29]都志辉,陈渝,刘鹏.网格计算[M].北京：清华大学出版社,2002：126-129.
[30]LCG [EB/OL]:http://lcg.web.cern.ch/lcg/,2010-12-5.
[31]Gridbus [EB/OL]:http://www.cloudbus.org/middleware/,2008-11-15.
[32]R. Buyya, M. Murshed. GridSim:A toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing[J]. Concurrency and Computation:Practice & Experience,2002,14:1175-1220.
[33]CNGrid [EB/OL].:http://www.cngrid.org/web/guest/home,2010-12-20.
[34]S. J. Chapin, D. Katramatos, K. Karpovich et al. Resource management in Legion[J]. Future Generation Computer Systems,1999,15(5-6):583-594.
[35]M. J. Lewis, A. J. Ferrari, M. A. Humphrey et al. Support for Extensibility and Site Autonomy in the Legion Grid System Object Model[J]. Journal of Parallel and Distributed Computing,2003,63(5):525-538.
[36]Condor [EB/OL].:http://www.cs.wisc.edu/condor/,2010-12-20.
[37]H. Casanova, G. Obertelli, F. Berman et al. The AppLeS Parameter Sweep Template: User-Level Middleware for the Grid[A]. In:Proceedings of IEEE/ACM Conference on Supercomputing[C], Dallas, Texas, USA:IEEE Computer Society,2000:60.
[38]F. Berman, R. Wolski, H. Casanova et al. Adaptive Computing on the Grid Using AppLeS[J]. IEEE Transaction on Parallel and Distributed Systems,2003,14(4): 369-382.
[39]R. Buyya, D. Abramson, J. Giddy. Nimrod/G:An Architecture for a Resource Management and Scheduling System in a Global Computational Grid[A]. In: Proceedings of the Fourth International Conference/Exhibition on High Performance Computing in Asia-Pacific Region[C]. Beijing,2000,1:283-289.
[40]D. Abramson, R. Buyya, J. Giddy. 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.
[41]K. Krauter, R. Buyya, M. Maheswaran. A toxonomy and survey of grid resource management systems for distributed computing[J]. Software Practice and Experience,2002,32(2):135-164.
[42]F. P. Dong and S. G. Akl. Scheduling Algorithms for Grid Computing:State of the Art and Open Problems[R]. Technical Report No.2006-504, School of Computing, Queen's University, Kingston, Ontario,2006.
[43]B. Yagoubi, Y. Slimani.Task Load Balancing Strategy for Grid Computing[J]. Journal of Computer Science,2007,3(3):186-194.
[44]S. K. Dimitriadou, H. D. Karatza. Multi-Site Allocation Policies on a Grid and Local Level[J]. Electronic Notes in Theoretical Computer Science,2010,261:163-179.
[45]H. Zhao, X. L. Li. Efficient Grid Task-Bundle Allocation using Bargaining Based Self-Adaptive Auction[A]. In:Proceeding of the 9th International Symposium on Cluster Computing and the Grid(CCGrid 2009)[C], Shanghai, China:IEEE Computer Society,2009:4-11.
[46]肖鹏,胡志刚.基于三方博弈的网格资源协同分配模型[J].华南理工大学学报(自然科学版),2009,37(4)：13-17.
[47]苑迎春,李小平,王茜.基于串规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.
[48]Henan Zhao, Eleni Tsiakkouri, Rizos Sakellariou et al. Scheduling Workflows with Budget Constraints. In Proceedings of the CoreGRID Workshop,2005:347-357.
[49]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.
[50]C. L. Li, L. Y. Li. A Utility-based Two Level Market Solution for Optimal Resource Allocation in Computational Grid[A]. In:Proceedings of the 34th IEEE International Conference on Parallel Processing(ICPP 2005)[C]. Oslo, Norway: IEEE Computer Society,2005:23-30.
[51]X. S. He, X. H. Sun, G. V. Laszewski. QoS Guided Min-Min Heuristic for Grid Task Scheduling [J]. Journal of Computer Science and Technology,2003,18(4):442-451.
[52]C. L. Weng, X. D. Lu. Heuristic scheduling for bag-of-tasks applications in combination with QoS in the computational grid [J]. Future Generation Computer Systems,2005,21(2):271-280.
[53]L. Z. Zeng, B. Benatallah, A. H. H. Ngu et al. QoS-Aware Middleware for Web Services Composition[J]. IEEE Transactions on Software Engineering,2004,30(5): 311-327.
[54]Y. Liu, A. H. H. Ngu, L. Zhao Zeng. QoS Computation and Policing in Dynamic Web Service Selection[A].In:Proceedings of the 13th International Conference on World Wide Web-Alternate Track Papers & Posters(WWW 2004)[C]. NY, USA, 2004:66-73.
[55]金海,陈汉华,吕志鹏等.CGSP作业管理器合成服务的QoS优化模型及求解.计算机学报,2005,28(4)：578-588.
[56]X. M. Zhu, J. H. Zhu, M. H. Ma et al. SAQA:A Self-Adaptive QoS-Aware Scheduling Algorithm for Real-Time Tasks on Heterogeneous Cluster[A]. In: Proceedings of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, Melbourne(CCGrid 2010) [C]. Victoria, Australia:IEEE,2010: 224-232.
[57]T. D. Braun, H. J. Siegel, N. Beck et al. A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems[J]. Journal of Parallel and Distributed Computing,2001,61(6): 810-837.
[58]N. Muthuvelu, J. Y. Liu, N. L. Soe et al. A Dynamic Job Grouping-Based Scheduling for Deploying Applications with Fine-Grained Tasks on Global Grids[A], In:Proceedings of the Australasian Workshop on Grid Computing and e-Research (AusGrid 2005) and the Third Australasian Information Security Workshop (AISW 2005)[C]. Newcastle, NSW, Australia:Australian Computer Society,2005:41-48.
[59]H. Topcuoglu, S. Hariri, M.-Y. Wu. Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing[J]. IEEE Transactions on Parallel and Distributed Systems,2002,13(3):260-274.
[60]H. N. Zhao, R. Sakellariou. An Experimental Investigation into the Rank Function of the Heterogeneous Earliest Finish Time Scheduling Algorithm[J].Lecture Notes in Computer Science(LNCS),2003,2790:189-194.
[61]J. Yu, R. Buyya, C.-K. Tham. Cost-based Scheduling of Scientific Workflow Applications on Utility Grids [A]. In:Proceedings of the First International Conference on e-Science and Grid Computing(e-Science'05)[C]. Melbourne, Australia:IEEE Computer Society,2005:147-154.
[62]H. Lamehamedi, B. Szymanski, Z. Shentu et al. Data Replication Strategies in Grid Environments [A]. In:Proceedings of the 5th International Conference on Algorithms and Architecture for Parallel Processing(ICA3PP 2002)[C]. Los Alamitos, CA:IEEE Computer Society,2002:378-383.
[63]B. Rood, M. J. Lewis. Availability Prediction Based Replication Strategies for Grid Environments [A]. In:Proceeding of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGRID 2010)[C]. Melbourne, Victoria, Australia:IEEE,2010:25-33.
[64]A. R. Abdurrab, T. Xie. FIRE:A File Reunion Based Data Replication Strategy for Data Grids[A]. In:Proceeding of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGRID 2010)[C]. Melbourne, Victoria, Australia:IEEE,2010:215-223.
[65]K. Ranganathan, I. T. Foster. Decoupling Computation and Data Scheduling in Distributed Data-Intensive Applications [A]. In:Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing (HPDC-11 2002)[C]. Edinburgh, Scotland, UK:IEEE Computer Society,2002:352-361.
[66]F. Desprez, A. Vernois, C. Blanchet. Simultaneous Scheduling of Replication and Computation for Bioinformatic Applications on the Grid[A]. In:Proceedings of workshop on Challenges of Large Applications in Distributed Environments (CLADE 2005)[C]. North Carolina, USA,2005:66-74.
[67]R. Wolski, N. T. Spring, J. Hayes. The Network Weather Service:A Distributed Resource Performance Forecasting Service for Metacomputing[J]. Journal of Future Generation Computing Systems,1999,15(5-6):757-768.
[68]K. Czajkowski, C. Kesselman, I. T. Foster et al. Grid Information Services for Distributed Resource Sharing[A]. In:Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing (HPDC 2001)[C]. San Francisco, California, USA:IEEE Computer Society,2001:181-194.
[69]F. D. Sacerdoti, M. J. Katz, M. L Massie et al. Wide area cluster monitoring with Ganglia[A]. In:Proceedings of IEEE International Conference on Cluster Computing[C], Kowloon, Hong Kong, China:IEEE Computer Society,2003: 289-298.
[70]H. Jin, X. H. Shi, W. Z. Qiang et al. An Adaptive Meta-scheduler for Data Intensive Applications[J], International Journal of Grid and Utility Computing,2005,1(1): 32-37.
[71]L. Y. Yang, J. M. Schopf, I. T. Foster. Conservative Scheduling:Using Predicted Variance to Improve Scheduling Decisions in Dynamic Environments [A]. In: Proceedings of the ACM/IEEE SuperComputing2003 Conference on High Performance Networking and Computing. Phoenix[C]. Arizona, USA:ACM,2003: 31-46.
[72]M. Wu, X. H. Sun. Grid Harvest Service:A Performance System of Grid Computing[J]. Journal of Parallel and Distributed Computing,2006,66(10): 1322-1337.
[73]K. D. Cooper, A. Dasgupta, K. Kennedy et al. New Grid Scheduling and Rescheduling Methods in the GrADS Project[A]. In:Proceedings of the 18th International Parallel and Distributed Processing Symposium (IPDPS 2004)[C]. Santa Fe, New Mexico, USA:IEEE Computer Society,2004:199-206.
[74]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.
[75]H. Dail, H. Casanova, F. Berman. A Decoupled Scheduling Approach for the GrADS Program Development Environment. In:Proceedings of. the 2002 ACM/IEEE Conference on Supercomputing. Baltimore(SC 2002)[C]. Maryland, USA:ACM,2002:1-14.
[76]L. Marchal, Y. Yang, H. Casanova et al. A realistic network/application model for scheduling divisible loads on large-scale platforms[A]. In:Proceedings of the 19th International Parallel and Distributed Processing Symposium(IPDPS 2005)[C]. Denver, CO, USA:IEEE Computer Society,2005,01:48b.
[77]M. M. Kostreva, W. Ogryczak, A. Wierzbicki. Equitable aggregations and multiple criteria analysis[J]. European Journal of Operational Research,2004,158(2): 362-377.
[78]陈晓苏,刘剑.面向Web服务重组的平衡多目标遗传算法研究[J].小型微型计算机系统,2007,28(10)：1880-1883.
[79]N. Feng, S.-C. Mau, N. B. Mandayam. Pricing and power control for joint network-centric and user-centric radio resource management[J]. IEEE Transactions on Communications,2004,52(9):1547-1557.
[80]N. Feng, S.-C. Mau, N. B. Mandayam. Joint network-centric and user-centric radio resource management in a multicell system [J]. IEEE Transactions on Communications,2005,53(7):1114-1118.
[81]C. L. Li, L. Y. Li. An optimization approach for decentralized QoS-based scheduling based on utility and pricing in grid computing[J]. Concurrency and Computation: Practice & Experience.2007,19(1):107-128.
[82]C. L. Li, L. Y. Li. A Pricing Approach for Grid Resouce Scheduling with QoS Guarantees [J]. Fundamenta Informaticae,2007,76(1-2):59-73.
[83]马艳,龚斌,邹立达.基于平衡定价和成本梯度的科学工作流调度策略[J].电子学报,2010,38(10)：2416-2421.
[84]胡明生.网格环境中决策资源协同调度研究[D].武汉：华中科技大学,2006.
[85]李静.数据网格的资源管理相关策略及算法研究[D].重庆：重庆大学,2007.
[86]R. Buyya, M. Murshed, D. Abramson et al. Scheduling parameter sweep applications on global Grids:a deadline and budget constrained cost-time optimization algorithm[J]. International Journal of Software:Practice and Experience (SPE), Wiley Press, New York, USA,2005,35(5):491-512.
[87]A. Wibisono, Z. M. Zhao, A. Belloum et al. A Framework for Interactive Parameter Sweep Applications [A]. In:Proceedings of the 8th IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2008)[C]. Lyon, France:IEEE Computer Society,2008:703.
[88]J. Yu, R. Buyya. A Taxonomy of Workflow Management Systems for Grid Computing[J]. Journal of Grid Computing,2005,3(3-4):171-200.
[89]O. H. Ibarra, C. E. Kim. Heuristic Algorithms for Scheduling Independent Tasks on Nonindentical Processors [J]. Journal of the ACM,1977,24(2):280-289.
[90]V. Hamscher, U. Schwiegelshohn, A. Streit et al. Evaluation of Job-Scheduling Strategies for Grid Computing[A]. In:Proceedings of the First IEEE/ACM International Workshop (GRID 2000)[C]. Bangalore, India,2000:191-202.
[91]丁肇辉.网格元调度系统的研究和CSF4元调度器的设计与实现[D].吉林：吉林大学,2009.
[92]J.-K. Kim, S. Shivle, H. J. Siegel et al. Dynamic Mapping in a Heterogeneous Environment with Tasks Having Priorities and Multiple Deadlines[A]. In: Proceedings of the 17th International Parallel and Distributed Processing Symposium(IPDPS 2003)[C]. Nice, France:IEEE Computer Society,2003:98.
[93]R. J. Al-Ali, O. F. Rana, D. W. Walker et al. G-QoSM:Grid Service Discovery using QoS Properties [J]. Computing and Artificial Intelligence,2002,21(4):363-382.
[94]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.
[95]I. T. Foster, C. Kesselman, C. Lee et al. A Distributed Resource Management Architecture that Supports Advance Reservation and Co-Allocation[A]. In: Proceedings of the Seventh International Workshop on Quality of Service[C], London, UK,1999:27-36.
[96]Rashid J. Al-Ali, Kaizar Amin, Gregor von Laszewski et al. Analysis and Provision of QoS for Distributed Grid Applications [J]. Journal of Grid Computing.2004,2(2): 163-182.
[97]R. J. Al-Ali, K. Amin, G. V. Laszewski et al. An OGSA-Based Quality of Service Framework[J]. Lecture Notes in Computer Science,2004,3033:529-540.
[98]K. Czajkowski, I. T. Foster, C. Kesselman et al. SNAP:A protocol for negotiating service level agreements and coordinating resource management in distributed systems[J]. Lecture Notes in Computer Science,2002,2537:153-183.
[99]R. J. Al-Ali, A. Hafid, O. F. Rana et al. QoS Adaptation in Service-Oriented Grids[C]. In:Proceedings of International Workshop on Middleware for Grid Computing, Rio de Janeiro, Brazil,2003:200-210.
[100]N. Furmento, W. Lee, A. Mayer et al. ICENI:an open grid service architecture implemented with Jini[A]. In:Proceedings of ACM/IEEE Conference on Supercomputing[C]. Baltimore Maryland, USA:ACM,2002:1-10.
[101]A. Dogan, F. Ozguner. On QoS-based scheduling of a meta-task with multiple QoS demands in heterogeneous computing[A]. In:Proceedings of the 16th International Symposium on Parallel and Distributed Processing (IPDPS 2002)[C], Fort Lauderdale FL, USA:IEEE Computer Society,2002:50-55.
[102]Y. Badr, A. Abraham, F. Biennier et al. Enhancing Web Service Selection by User Preferences of Non-functional Features[A]. In:Proceedings of the 4th International Conference on Next Generation Web Services Practices[C]. Washington, DC, USA: IEEE Computer Society,2008:60-65.
[103]E. Vinek, P. P. Beran, E. Schikuta. Classification and Composition of QoS Attributes in Distributed, Heterogeneous Systems [A]. In:Proceedings of the 11th IEEE/ACM International Symposium on Cluster Cloud and Crid Computing(CCGrid 2011)[C], Newport Beach, CA, USA:IEEE,2011:424-433.
[104]E. Caron, B. Depardon, F. Desprez. Deployment of a hierarchical middleware[J]. Lecture Notes in Computer Science,6271:343-354.
[105]E. Caron, B. Depardon, F. Desprez. Multiple Services Throughput Optimization in a Hierarchical Middleware [A]. In:Proceedings of the 11th IEEE/ACM International Symposium on Cluster Cloud and Crid Computing(CCGrid 2011)[C]. Newport Beach, CA, USA:IEEE,2011:94-103.
[106]M. Maheswaran, S. Ali, H. J. Siegel et al. Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems[A]. In: Proceedings of the 8th Heterogeneous Computing Workshop[C]. San Juan, Puerto Rico:IEEE Computer Society,1999:30-44.
[107]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.
[108]D. C. Vanderster, N. J. Dimopoulos, R. J. Sobie. Metascheduling Multiple Resource Types Using the MMKP[A]. In:Proceedings of the 7th IEEE/ACM International Conference on Grid Computing (GRID 2006)[C]. Barcelona, Spain:IEEE,2006: 231-237.
[109]X. Liu, W. Wei, C. Qiao et al. Task Scheduling and Lightpath Establishment in Optical Grids[A]. In:Proceedings of the 27th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies[C], Phoenix, AZ, USA:IEEE,2008:1966-1974.
[110]S. S. Song, K. Hwang, Y. K. Kwok. Risk-resilient Heuristics and Genetic Algorithms for Security-assured Grid Job Scheduling[J]. IEEE Transactions on Computers,2006,55(6):703-719.
[111]J Yu, R. Buyya, C. K. Tham. QoS-based Scheduling of Workflow Applications on Service Grids[A]. In:Proceedings of the 15th Ⅲ Inernational Symposium on High Performance Distributed Computing (HPDC 2006)[C]. Pris, France:IEEE Computer Society,2006.
[112]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.
[113]李志洁,刘向东,段晓东.改进粒子群算法在网格资源分配中的优化[J].计算机集成制造系统,2009,15(12)：2375-2382.
[114]R. Hu, Z. Hu. A Scheduling Algorithm Aimed at Time and Cost for Meta-tasks in Grid Computing Using Fuzzy Applicability[A]. In:Proceedings of the 8th International Conference on High-Performance Computing in Asia-Pacific Region (HPC-Asia'05)[C]. Beijing, China:IEEE Computer Society,2005:564-569.
[115]蒋哲远,韩江洪,王钊.动态的QoS感知Web服务选择和组合优化模型[J].计算机学报,2009,32(5)：1014-1025.
[116]V. Fiolet, B. Toursel. A clustering method to distribute a database on a grid[J]. Future Generation Computer Systems,2007,23(8):997-1002.
[117]陈志刚,杨博.网格服务资源多维性能聚类任务调度[J].软件学报,2009,20(10)：2766-2775.
[118]Parallel Workloads Archive[EB/OL].:http://www.cs.huji.ac.il/labs/parallel/workload, January 2011.
[119]K. S. Golconda, F. Ozguner. A comparison of static QoS-based scheduling heuristics for a meta-task with multiple QoS dimensions in heterogeneous computing[A]. In: Proceeding of the 18th International Parallel and Distributed Processing Symposium (IPDPS 2004)[C]. Santa Fe, New Mexico, USA:IEEE Computer Society,2004: 106-119.
[120]R. K. Jain, D.-M. Chiu, W. R. Hawe. A Quantitative Measure of Fairness and Discrimination for Rresource Allocation in Shared Computer Systems[R]. CoRR, 1998.
[121]Jia Yu, Rajkumar Buyya. A Taxonomy of Scientif Workflow Systems for Grid Computing[J]. SIGMOD Record,2005,34(3):44-49.
[122]T. Andrews, F. Curbera, H. Dholakia et al. Business Process Execution Language forWeb Services[R]. Technical report, BEA Systems, et al.,2003.
[123]T. Fahringer, J. Qin, S. Hainzer. Specification of Grid Workflow Applications with AGWL:An Abstract Grid Workflow Language[A]. In:Proceedings of the 5th International Symposium on Cluster Computing and the Grid(CCGrid 2005)[C]. Cardiff, UK:IEEE Computer Society,2005:676-685.
[124]A. Hoheisel. User tools and languages for graph-based Grid Workflows[J]. Concurrency and Computation:Practice & Experience,2006,18(10):1101-1113.
[125]H. N. Zhao, R. Sakellariou. Scheduling Multiple DAGs onto Heterogeneous Systems[A]. In:Proceedings of the 20th International Parallel and Distributed Processing Symposium (IPDPS 2006)[C]. Rhodes Island, Greece:IEEE,2006: 1-14.
[126]R. Ranjan, M. Rahman, R. Buyya. A Decenralized and Cooperaive Worklow Scheduling Algorithm[A]. In:Proceedings of the 8th International Symposium on Cluster Computing and the Grid(CCGrid 2008)[C]. Lyon, France:IEEE Computer Society,2008:1-8.
[127]M. Wieczorek, S. Podlipnig, R. Prodan et al. Bi-criteria Scheduling of Scientific Workflows for the Gird[A]. In:Proceedings of the 8th International Symposium on Cluster Computing and the Grid(CCGrid 2008)[C], Lyon, France:IEEE Computer Society,2008:9-16.
[128]Y. Zhang, A. Mandal, C. Koelbel et al. Combined Fault Tolerance and Scheduling Techniques for Workflow Applications on Computational Grids[A]. In:Proceedings of the 9th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2009)[C]. Shanghai, China:IEEE Computer Society,2009:244-251.
[129]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.
[130]M. Wieczorek, R. Prodan, A. Hoheisel. Taxonomies of the Multi-criteria Gird Workflow Scheduling Problem[R]. CoreGRID Technical Report Number TR-0106.
[131]DAGMan (Directed Acyclic Graph Manager)[EB/OL].: http://www.cs.wisc.edu/condor/dagman/,2010-6-25.
[132]E. Deelman, G. Singh, M. H. Su et al. Pegasus:a Framework for Mapping Complex Scientific Workflows onto Distributed Systems[J]. Scientific Programming,2005, 13(3):219-237.
[133]I. Taylor, M. Shields, I. Wang. Resource Management of Triana P2P Services[J]. Grid Resource Management,2004:451-462.
[134]A. S. MCGough, L. Young, A. Afzal et al. Workflow Enactment in ICENI. In:UK e-Science All Hands Meeting, Nottingham, UK:IOP Publishing Ltd,2004:894-900.
[135]T. M. Oinn, M. Addis, J. Ferris et al. Taverna:a tool for the composition and enactment of bioinformatics workflows[J]. Bioinformatics,2004,20(17): 3045-3054.
[136]F. Berman, A. A. Chien, K. D. Cooper et al. The GrADS Project:Software Support for High-Level Grid Application Development[J]. International Journal of High Performance Computing Applications,2001,15(4):327-344.
[137]J. W. Cao, S. A. Jarvis, S. Saini et al. GridFlow:Workflow Management for Grid Computing[A]. In:Proceedings of the 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2003)[C]. Tokyo, Japan:IEEE Computer Society,2003:198-205.
[138]D. W. Erwin, D. F. Snelling. UNICORE:A Grid Computing Environment[J]. Lecture Notes in Computer Science,2001,2150:825-834.
[139]J. Yu, R. Buyya. A Novel Architecture for Realizing Grid Workflow using Tuple Spaces[A]. In:Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing (GRID 2004)[C]. Pittsburgh, PA, USA:IEEE Computer Society,2004: 302-309.
[140]T. Fahringer, R. Prodan, R. Duan et al. ASKALON:A Grid Application Development and Computing Environment [A]. In:Proceedings of the 6th IEEE/ACM International Conference on Grid Computing (GRID 2005)[C]. Seattle, Washington, USA:IEEE,2005:122-131.
[141]Java CoG Kit Karajan Guide[EB/OL].:http://wiki.cogkit.org/wiki/Karajan, 2010-10-20.
[142]B. Ludascher, I. Altintas, C. Berkley et al. Scientific Workflow Management and the KEPLER System[J]. Concurrency and Computation:Practice & Experience, Special Issue on Workflow in Grid Systems,2006,18(10):1039-1065.
[143]F. Suter, F. Desprez, H. Casanova. From Heterogeneous Task Scheduling to Heterogeneous Mixed Parallel Scheduling[J]. Lecture Notes in Computer Science, 2004,3149:230-237.
[144]M. Wieczorek, R. Prodan, T. Fahringer. Scheduling of Scientific Workflows in the ASKALON Grid Environment [J]. ACM SIGMOD Record,2005,34(3):56-62.
[145]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.
[146]Y. Zhao, M. Wilde, I. T. Foster et al. Grid Middleware Service for Virtual Data Discovery, Composition and Integration[A]. In:Proceedings of the 2nd Workshop on Middleware for Grid Computing[C]. Toronto,Ontario,Canada:ACM,2004:57-62.
[147]A. O'Brien, S. Newhouse, J. Darlington. Mapping of Scientific Workflow within the E-Protein Project to Distributed Resources. In:UK e-Science All Hands Meeting, Nottingham, UK,2004:404-409.
[148]GRACE:Grid Application Coordination, Collaboration and Execution[EB/OL].: http://www.cs.uh.edu/-gracce,2011-3-20.
[149]O. Regev, N. Nisan. The POPCORN market Online markets for computational resource [J]. Decision Support Systems,2000,28(1-2):177-189.
[150]Z. G. Hu, P. Xiao. A novel resource co-allocation model with constraints to budget and deadline in computation grid[J]. Journal of Central South University of Technology,2009,16(3):458-466.
[151]郑美光,胡志刚,张凯.网格环境下基于序贯博弈的性能-效率平衡型优化[J]. 华南理工大学学报,2010,38(1)：92-96,107.
[152]胡周军,胡志刚,丁长松.基于博弈论健壮性增强的资源分配模型[J].系统工程理论与实践,2009,29(8)：102-110.
[153]李志洁,程春田,黄飞雪等.一种基于序贯博弈的网格资源分配策略[J].软件学报,2006,17(11)：2373-2383.
[154]R. Buyya. Economic-based Distributed Resource Management and Scheduling for Grid Computing[D]. Australia:Monash University,2002.
[155]S. K. Garg, S. Venugopal, R. Buyya. A Meta-scheduler with Auction Based Resource Allocation for Global Grids [A]. In:Proceedings of the 14th International Conference on Parallel and Distributed Systems (ICPADS 2008)[C]. Melbourne, Victoria, Australia:IEEE,2008:187-194.
[156]Y. K. Kwok, S. Song, K. Hwang. Selfish grid computing:game theoretic modeling and NAS performance results[A]. In:Proceeding of the 5th International Symposium on Cluster Computing and the Grid(CCGrid 2005)[C]. Cardiff, UK:IEEE Computer Society,2005:1143-1150.
[157]K. Rzadca, D. Trystram, A. Wierzbicki. Fair Game-Theoretic Resource Management in Dedicated Grids[A]. In:Proceeings of the seventh International Symposium on Cluster Computing and the Grid (CCGrid 2007)[C]. Rio de Janeiro, Brazil:IEEE Computer Society,2007:343-350.
[158]S. U. Khan, I. Ahmad. Non-cooperative, semi-cooperative, and cooperative games-based grid resource allocation[A]. In:Proceeding of the 20th International Parallel and Distributed Processing Symposium(IPDPS 2006)[C]. Rhodes Island, Greece:IEEE,2006:1-10.
[159]A. Iosup, O. O.Sonmez, S. Anoep et al. The performance of bags-of-tasks in large-scale distributed systems [A]. In:Proceedings of the 17th International Symposium on High-Performance Distributed Computing(HPDC-17 2008)[C]. Boston, MA, USA:ACM,2008:97-108.
[160]中南大学网格计算平台[EB/OL].:http://hpc.csu.edu.cn/Default.aspx,2011-6-15.
[2]I. T. Foster, C. Kesselman, S. Tuecke. The Anatomy of the Grid:Enabling Scalable Virtual Organizations [J]. International Journal of High Performance Computing Applications,2001,15(3):200-222.
[3]I. T. Foster, C. Kesselman. The Grid 2:Blueprint for a New Computing Infrastructure[M]. San Francisco, CA:Morgan Kaufmann,2004.
[4]V. Breton, A. E. Solomonides, R. H. McClatchey. A Perspective on the Healthgrid Initiative [A]. In:Proceedings of the 4th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2004) [C]. Chicago, Illinois, USA:IEEE Computer Society,2004:434-439.
[5]J.-M. Pierson, L. Brunie, C. Dhaenens et al. Grid for Geno-Medicine:A Glimpse on the GGM Project[A]. In:Proceedings of the 5th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2005)[C]. Cardiff, UK: IEEE Computer Society,2005:527-528.
[6]M. A. Gutierrez, S.H.G. Lage, J. Lee et al. A Computer-Aided Diagnostic System using a Global Data Grid Repository for the Evaluation of Ultrasound Carotid Images[A]. In:Proceedings of the Seventh IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2007)[C]. Rio de Janeiro, Brazil:IEEE Computer Society,2007:840-845.
[7]D. Sulakhe, A. Rodriguez, M. Wilde et al. Using Multiple Grid Resources for Bioinformatics Applications in GADU[A]. In:Proceedings of the Sixth IEEE/ACM International Symposium on Cluster Computing and the Grid Workshops (CCGrid 2006)[C]. Singapore:IEEE Computer Society,2006:41.
[8]J. Zhu, A. Guo, Z. H. Lu et al. Analysis of the Bioinformatics Grid Technique Applications in China[A]. In:Proceedings of the Sixth IEEE/ACM International Symposium on Cluster Computing and the Grid Workshops(CCGrid 2006)[C]. Singapore:IEEE Computer Society,2006:44.
[9]V. Kasam, J. Salzemann, N. Jacq et al. Large Scale Deployment of Molecular Docking Application on Computational Grid infrastructures for Combating Malaria[A]. In:Proceedings of the Seventh IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2007)[C]. Rio de Janeiro, Brazil:IEEE Computer Society,2007:691-700.
[10]何儒汉,金海,廖振松等.ChinaGrid图像处理网格平台中的语义信息服务研究.计算机研究与发展,2006,43(5)：821-827.
[11]N. Chrisochoides, A. Fedorov, A. Kot et al. Imaging and visual analysis-Toward Real-Time Image Guided Neurosurgery Using Distributed and Grid Computing[A]. In:Proceedings of the ACM/IEEE SC2006 Conference on High Performance Networking and Computing[C]. Tampa, FL, USA:ACM,2006:76.
[12]J. Tian, K. K. Ma. Super-resolution Imaging Using Grid Computing[A]. In: Proceedings of the Seventh IEEE/ACM International Symposium on Cluster Computing and the Grid(CCGrid 2007)[C]. Rio de Janeiro, Brazil:IEEE Computer Society,2007:293-300.
[13]TeraGrid[EB/OL].:http://www.teragrid.org/.2010-12-20.
[14]European Grid[EB/OL].:http://www.egi.eu/.2010-6-15.
[15]R. P. Souto, R. B. Avila, P. O. A. Navaux et al. Processing Mesoscale Climatology in a Grid Environment[A]. In:Proceedings of the Seventh IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2007)[C]. Rio de Janeiro, Brazil:IEEE Computer Society,2007:363-370.
[16]W3C. Web Services Architecture[EB/OL].: http://www.w3.org/TR/2004/NOTE-ws-arch-20040211/,2008-10-20.
[17]K. Czajkowski, I. T. Foster, C. Kesselman. Resource Co-Allocation in Computational Grids [A]. In:Proceedings of the Eighth International Symposium on High Performance Distributed Computing (HPDC 1999)[C]. Redondo Beach, California, USA:IEEE Conputer Society,1999:219-228.
[18]I. T. Foster, C. Kesselman, C. Lee et al. A Distributed Resource Management Architecture that Supports Advance Reservation and Co-Allocation. In:Proceedings of International Workshop on Quality of Service, London, UK:IEEE Computer Society,1999:27-36.
[19]I. T. Foster. What is the Grid? A Three Point Checklist[J]. GRID Today,2002,1(6): 20-27.
[20]Platform Computing, Inc. Platform LSF [EB/OL].: http://www.platform.com/Products/Platform.LSF.Family,2010-12-20.
[21]Cluster Resources, Inc. Maui Cluster Scheduler [EB/OL].: http://www.clusterresources.com/pages/products/maui-cluster-scheduler.php 2010-12-20.
[22]Altair Engineering, Inc. PBS Professional [EB/OL].: http://www.altair.com/software/pbspro.htm,2010-12-20.
[23]Sun Microsystems, Inc. Grid Engine [EB/OL].:http://gridengine.sunsource.net. 2010-12-20.
[24]G. Singh, C. Kesselman, and E. Deelman. Application-Level Resource Provisioning on the Grid[A]. In:Proceedings of the Second International Conference on e-Science and Grid Technologies (e-Science 2006)[C]. Amsterdam, The Netherlands: IEEE Computer Society,2006:83.
[25]J. Yu and R. Buyya. Scheduling Scientific Workflow Applications with Deadline and Budget Constraints using Genetic Algorithms [J]. Scientific Programming,2006, 14(3-4):217-230.
[26]V. T'kindt and J. Billaut. Multicriteria Scheduling[M]. Berlin:Springer Verlag,2002.
[27]H. Zhao, E. Tsiakkouri, R. Sakellariou et al. Scheduling Workflows with Budget Constraints [A]. In:Proceedings of the CoreGRID Workshop Integrated research in Grid Computing[C]. Pisa, Italy,2005:347-357.
[28]I. T. Foster, C. Kesselman. Globus:A Metacomputing Infrastructure Toolkit[J]. International Journal of Supercomputer Applications and High Performance Computing,1997,11(2):115-128.
[29]都志辉,陈渝,刘鹏.网格计算[M].北京：清华大学出版社,2002：126-129.
[30]LCG [EB/OL]:http://lcg.web.cern.ch/lcg/,2010-12-5.
[31]Gridbus [EB/OL]:http://www.cloudbus.org/middleware/,2008-11-15.
[32]R. Buyya, M. Murshed. GridSim:A toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing[J]. Concurrency and Computation:Practice & Experience,2002,14:1175-1220.
[33]CNGrid [EB/OL].:http://www.cngrid.org/web/guest/home,2010-12-20.
[34]S. J. Chapin, D. Katramatos, K. Karpovich et al. Resource management in Legion[J]. Future Generation Computer Systems,1999,15(5-6):583-594.
[35]M. J. Lewis, A. J. Ferrari, M. A. Humphrey et al. Support for Extensibility and Site Autonomy in the Legion Grid System Object Model[J]. Journal of Parallel and Distributed Computing,2003,63(5):525-538.
[36]Condor [EB/OL].:http://www.cs.wisc.edu/condor/,2010-12-20.
[37]H. Casanova, G. Obertelli, F. Berman et al. The AppLeS Parameter Sweep Template: User-Level Middleware for the Grid[A]. In:Proceedings of IEEE/ACM Conference on Supercomputing[C], Dallas, Texas, USA:IEEE Computer Society,2000:60.
[38]F. Berman, R. Wolski, H. Casanova et al. Adaptive Computing on the Grid Using AppLeS[J]. IEEE Transaction on Parallel and Distributed Systems,2003,14(4): 369-382.
[39]R. Buyya, D. Abramson, J. Giddy. Nimrod/G:An Architecture for a Resource Management and Scheduling System in a Global Computational Grid[A]. In: Proceedings of the Fourth International Conference/Exhibition on High Performance Computing in Asia-Pacific Region[C]. Beijing,2000,1:283-289.
[40]D. Abramson, R. Buyya, J. Giddy. 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.
[41]K. Krauter, R. Buyya, M. Maheswaran. A toxonomy and survey of grid resource management systems for distributed computing[J]. Software Practice and Experience,2002,32(2):135-164.
[42]F. P. Dong and S. G. Akl. Scheduling Algorithms for Grid Computing:State of the Art and Open Problems[R]. Technical Report No.2006-504, School of Computing, Queen's University, Kingston, Ontario,2006.
[43]B. Yagoubi, Y. Slimani.Task Load Balancing Strategy for Grid Computing[J]. Journal of Computer Science,2007,3(3):186-194.
[44]S. K. Dimitriadou, H. D. Karatza. Multi-Site Allocation Policies on a Grid and Local Level[J]. Electronic Notes in Theoretical Computer Science,2010,261:163-179.
[45]H. Zhao, X. L. Li. Efficient Grid Task-Bundle Allocation using Bargaining Based Self-Adaptive Auction[A]. In:Proceeding of the 9th International Symposium on Cluster Computing and the Grid(CCGrid 2009)[C], Shanghai, China:IEEE Computer Society,2009:4-11.
[46]肖鹏,胡志刚.基于三方博弈的网格资源协同分配模型[J].华南理工大学学报(自然科学版),2009,37(4)：13-17.
[47]苑迎春,李小平,王茜.基于串规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.
[48]Henan Zhao, Eleni Tsiakkouri, Rizos Sakellariou et al. Scheduling Workflows with Budget Constraints. In Proceedings of the CoreGRID Workshop,2005:347-357.
[49]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.
[50]C. L. Li, L. Y. Li. A Utility-based Two Level Market Solution for Optimal Resource Allocation in Computational Grid[A]. In:Proceedings of the 34th IEEE International Conference on Parallel Processing(ICPP 2005)[C]. Oslo, Norway: IEEE Computer Society,2005:23-30.
[51]X. S. He, X. H. Sun, G. V. Laszewski. QoS Guided Min-Min Heuristic for Grid Task Scheduling [J]. Journal of Computer Science and Technology,2003,18(4):442-451.
[52]C. L. Weng, X. D. Lu. Heuristic scheduling for bag-of-tasks applications in combination with QoS in the computational grid [J]. Future Generation Computer Systems,2005,21(2):271-280.
[53]L. Z. Zeng, B. Benatallah, A. H. H. Ngu et al. QoS-Aware Middleware for Web Services Composition[J]. IEEE Transactions on Software Engineering,2004,30(5): 311-327.
[54]Y. Liu, A. H. H. Ngu, L. Zhao Zeng. QoS Computation and Policing in Dynamic Web Service Selection[A].In:Proceedings of the 13th International Conference on World Wide Web-Alternate Track Papers & Posters(WWW 2004)[C]. NY, USA, 2004:66-73.
[55]金海,陈汉华,吕志鹏等.CGSP作业管理器合成服务的QoS优化模型及求解.计算机学报,2005,28(4)：578-588.
[56]X. M. Zhu, J. H. Zhu, M. H. Ma et al. SAQA:A Self-Adaptive QoS-Aware Scheduling Algorithm for Real-Time Tasks on Heterogeneous Cluster[A]. In: Proceedings of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, Melbourne(CCGrid 2010) [C]. Victoria, Australia:IEEE,2010: 224-232.
[57]T. D. Braun, H. J. Siegel, N. Beck et al. A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems[J]. Journal of Parallel and Distributed Computing,2001,61(6): 810-837.
[58]N. Muthuvelu, J. Y. Liu, N. L. Soe et al. A Dynamic Job Grouping-Based Scheduling for Deploying Applications with Fine-Grained Tasks on Global Grids[A], In:Proceedings of the Australasian Workshop on Grid Computing and e-Research (AusGrid 2005) and the Third Australasian Information Security Workshop (AISW 2005)[C]. Newcastle, NSW, Australia:Australian Computer Society,2005:41-48.
[59]H. Topcuoglu, S. Hariri, M.-Y. Wu. Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing[J]. IEEE Transactions on Parallel and Distributed Systems,2002,13(3):260-274.
[60]H. N. Zhao, R. Sakellariou. An Experimental Investigation into the Rank Function of the Heterogeneous Earliest Finish Time Scheduling Algorithm[J].Lecture Notes in Computer Science(LNCS),2003,2790:189-194.
[61]J. Yu, R. Buyya, C.-K. Tham. Cost-based Scheduling of Scientific Workflow Applications on Utility Grids [A]. In:Proceedings of the First International Conference on e-Science and Grid Computing(e-Science'05)[C]. Melbourne, Australia:IEEE Computer Society,2005:147-154.
[62]H. Lamehamedi, B. Szymanski, Z. Shentu et al. Data Replication Strategies in Grid Environments [A]. In:Proceedings of the 5th International Conference on Algorithms and Architecture for Parallel Processing(ICA3PP 2002)[C]. Los Alamitos, CA:IEEE Computer Society,2002:378-383.
[63]B. Rood, M. J. Lewis. Availability Prediction Based Replication Strategies for Grid Environments [A]. In:Proceeding of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGRID 2010)[C]. Melbourne, Victoria, Australia:IEEE,2010:25-33.
[64]A. R. Abdurrab, T. Xie. FIRE:A File Reunion Based Data Replication Strategy for Data Grids[A]. In:Proceeding of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGRID 2010)[C]. Melbourne, Victoria, Australia:IEEE,2010:215-223.
[65]K. Ranganathan, I. T. Foster. Decoupling Computation and Data Scheduling in Distributed Data-Intensive Applications [A]. In:Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing (HPDC-11 2002)[C]. Edinburgh, Scotland, UK:IEEE Computer Society,2002:352-361.
[66]F. Desprez, A. Vernois, C. Blanchet. Simultaneous Scheduling of Replication and Computation for Bioinformatic Applications on the Grid[A]. In:Proceedings of workshop on Challenges of Large Applications in Distributed Environments (CLADE 2005)[C]. North Carolina, USA,2005:66-74.
[67]R. Wolski, N. T. Spring, J. Hayes. The Network Weather Service:A Distributed Resource Performance Forecasting Service for Metacomputing[J]. Journal of Future Generation Computing Systems,1999,15(5-6):757-768.
[68]K. Czajkowski, C. Kesselman, I. T. Foster et al. Grid Information Services for Distributed Resource Sharing[A]. In:Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing (HPDC 2001)[C]. San Francisco, California, USA:IEEE Computer Society,2001:181-194.
[69]F. D. Sacerdoti, M. J. Katz, M. L Massie et al. Wide area cluster monitoring with Ganglia[A]. In:Proceedings of IEEE International Conference on Cluster Computing[C], Kowloon, Hong Kong, China:IEEE Computer Society,2003: 289-298.
[70]H. Jin, X. H. Shi, W. Z. Qiang et al. An Adaptive Meta-scheduler for Data Intensive Applications[J], International Journal of Grid and Utility Computing,2005,1(1): 32-37.
[71]L. Y. Yang, J. M. Schopf, I. T. Foster. Conservative Scheduling:Using Predicted Variance to Improve Scheduling Decisions in Dynamic Environments [A]. In: Proceedings of the ACM/IEEE SuperComputing2003 Conference on High Performance Networking and Computing. Phoenix[C]. Arizona, USA:ACM,2003: 31-46.
[72]M. Wu, X. H. Sun. Grid Harvest Service:A Performance System of Grid Computing[J]. Journal of Parallel and Distributed Computing,2006,66(10): 1322-1337.
[73]K. D. Cooper, A. Dasgupta, K. Kennedy et al. New Grid Scheduling and Rescheduling Methods in the GrADS Project[A]. In:Proceedings of the 18th International Parallel and Distributed Processing Symposium (IPDPS 2004)[C]. Santa Fe, New Mexico, USA:IEEE Computer Society,2004:199-206.
[74]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.
[75]H. Dail, H. Casanova, F. Berman. A Decoupled Scheduling Approach for the GrADS Program Development Environment. In:Proceedings of. the 2002 ACM/IEEE Conference on Supercomputing. Baltimore(SC 2002)[C]. Maryland, USA:ACM,2002:1-14.
[76]L. Marchal, Y. Yang, H. Casanova et al. A realistic network/application model for scheduling divisible loads on large-scale platforms[A]. In:Proceedings of the 19th International Parallel and Distributed Processing Symposium(IPDPS 2005)[C]. Denver, CO, USA:IEEE Computer Society,2005,01:48b.
[77]M. M. Kostreva, W. Ogryczak, A. Wierzbicki. Equitable aggregations and multiple criteria analysis[J]. European Journal of Operational Research,2004,158(2): 362-377.
[78]陈晓苏,刘剑.面向Web服务重组的平衡多目标遗传算法研究[J].小型微型计算机系统,2007,28(10)：1880-1883.
[79]N. Feng, S.-C. Mau, N. B. Mandayam. Pricing and power control for joint network-centric and user-centric radio resource management[J]. IEEE Transactions on Communications,2004,52(9):1547-1557.
[80]N. Feng, S.-C. Mau, N. B. Mandayam. Joint network-centric and user-centric radio resource management in a multicell system [J]. IEEE Transactions on Communications,2005,53(7):1114-1118.
[81]C. L. Li, L. Y. Li. An optimization approach for decentralized QoS-based scheduling based on utility and pricing in grid computing[J]. Concurrency and Computation: Practice & Experience.2007,19(1):107-128.
[82]C. L. Li, L. Y. Li. A Pricing Approach for Grid Resouce Scheduling with QoS Guarantees [J]. Fundamenta Informaticae,2007,76(1-2):59-73.
[83]马艳,龚斌,邹立达.基于平衡定价和成本梯度的科学工作流调度策略[J].电子学报,2010,38(10)：2416-2421.
[84]胡明生.网格环境中决策资源协同调度研究[D].武汉：华中科技大学,2006.
[85]李静.数据网格的资源管理相关策略及算法研究[D].重庆：重庆大学,2007.
[86]R. Buyya, M. Murshed, D. Abramson et al. Scheduling parameter sweep applications on global Grids:a deadline and budget constrained cost-time optimization algorithm[J]. International Journal of Software:Practice and Experience (SPE), Wiley Press, New York, USA,2005,35(5):491-512.
[87]A. Wibisono, Z. M. Zhao, A. Belloum et al. A Framework for Interactive Parameter Sweep Applications [A]. In:Proceedings of the 8th IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2008)[C]. Lyon, France:IEEE Computer Society,2008:703.
[88]J. Yu, R. Buyya. A Taxonomy of Workflow Management Systems for Grid Computing[J]. Journal of Grid Computing,2005,3(3-4):171-200.
[89]O. H. Ibarra, C. E. Kim. Heuristic Algorithms for Scheduling Independent Tasks on Nonindentical Processors [J]. Journal of the ACM,1977,24(2):280-289.
[90]V. Hamscher, U. Schwiegelshohn, A. Streit et al. Evaluation of Job-Scheduling Strategies for Grid Computing[A]. In:Proceedings of the First IEEE/ACM International Workshop (GRID 2000)[C]. Bangalore, India,2000:191-202.
[91]丁肇辉.网格元调度系统的研究和CSF4元调度器的设计与实现[D].吉林：吉林大学,2009.
[92]J.-K. Kim, S. Shivle, H. J. Siegel et al. Dynamic Mapping in a Heterogeneous Environment with Tasks Having Priorities and Multiple Deadlines[A]. In: Proceedings of the 17th International Parallel and Distributed Processing Symposium(IPDPS 2003)[C]. Nice, France:IEEE Computer Society,2003:98.
[93]R. J. Al-Ali, O. F. Rana, D. W. Walker et al. G-QoSM:Grid Service Discovery using QoS Properties [J]. Computing and Artificial Intelligence,2002,21(4):363-382.
[94]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.
[95]I. T. Foster, C. Kesselman, C. Lee et al. A Distributed Resource Management Architecture that Supports Advance Reservation and Co-Allocation[A]. In: Proceedings of the Seventh International Workshop on Quality of Service[C], London, UK,1999:27-36.
[96]Rashid J. Al-Ali, Kaizar Amin, Gregor von Laszewski et al. Analysis and Provision of QoS for Distributed Grid Applications [J]. Journal of Grid Computing.2004,2(2): 163-182.
[97]R. J. Al-Ali, K. Amin, G. V. Laszewski et al. An OGSA-Based Quality of Service Framework[J]. Lecture Notes in Computer Science,2004,3033:529-540.
[98]K. Czajkowski, I. T. Foster, C. Kesselman et al. SNAP:A protocol for negotiating service level agreements and coordinating resource management in distributed systems[J]. Lecture Notes in Computer Science,2002,2537:153-183.
[99]R. J. Al-Ali, A. Hafid, O. F. Rana et al. QoS Adaptation in Service-Oriented Grids[C]. In:Proceedings of International Workshop on Middleware for Grid Computing, Rio de Janeiro, Brazil,2003:200-210.
[100]N. Furmento, W. Lee, A. Mayer et al. ICENI:an open grid service architecture implemented with Jini[A]. In:Proceedings of ACM/IEEE Conference on Supercomputing[C]. Baltimore Maryland, USA:ACM,2002:1-10.
[101]A. Dogan, F. Ozguner. On QoS-based scheduling of a meta-task with multiple QoS demands in heterogeneous computing[A]. In:Proceedings of the 16th International Symposium on Parallel and Distributed Processing (IPDPS 2002)[C], Fort Lauderdale FL, USA:IEEE Computer Society,2002:50-55.
[102]Y. Badr, A. Abraham, F. Biennier et al. Enhancing Web Service Selection by User Preferences of Non-functional Features[A]. In:Proceedings of the 4th International Conference on Next Generation Web Services Practices[C]. Washington, DC, USA: IEEE Computer Society,2008:60-65.
[103]E. Vinek, P. P. Beran, E. Schikuta. Classification and Composition of QoS Attributes in Distributed, Heterogeneous Systems [A]. In:Proceedings of the 11th IEEE/ACM International Symposium on Cluster Cloud and Crid Computing(CCGrid 2011)[C], Newport Beach, CA, USA:IEEE,2011:424-433.
[104]E. Caron, B. Depardon, F. Desprez. Deployment of a hierarchical middleware[J]. Lecture Notes in Computer Science,6271:343-354.
[105]E. Caron, B. Depardon, F. Desprez. Multiple Services Throughput Optimization in a Hierarchical Middleware [A]. In:Proceedings of the 11th IEEE/ACM International Symposium on Cluster Cloud and Crid Computing(CCGrid 2011)[C]. Newport Beach, CA, USA:IEEE,2011:94-103.
[106]M. Maheswaran, S. Ali, H. J. Siegel et al. Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems[A]. In: Proceedings of the 8th Heterogeneous Computing Workshop[C]. San Juan, Puerto Rico:IEEE Computer Society,1999:30-44.
[107]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.
[108]D. C. Vanderster, N. J. Dimopoulos, R. J. Sobie. Metascheduling Multiple Resource Types Using the MMKP[A]. In:Proceedings of the 7th IEEE/ACM International Conference on Grid Computing (GRID 2006)[C]. Barcelona, Spain:IEEE,2006: 231-237.
[109]X. Liu, W. Wei, C. Qiao et al. Task Scheduling and Lightpath Establishment in Optical Grids[A]. In:Proceedings of the 27th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies[C], Phoenix, AZ, USA:IEEE,2008:1966-1974.
[110]S. S. Song, K. Hwang, Y. K. Kwok. Risk-resilient Heuristics and Genetic Algorithms for Security-assured Grid Job Scheduling[J]. IEEE Transactions on Computers,2006,55(6):703-719.
[111]J Yu, R. Buyya, C. K. Tham. QoS-based Scheduling of Workflow Applications on Service Grids[A]. In:Proceedings of the 15th Ⅲ Inernational Symposium on High Performance Distributed Computing (HPDC 2006)[C]. Pris, France:IEEE Computer Society,2006.
[112]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.
[113]李志洁,刘向东,段晓东.改进粒子群算法在网格资源分配中的优化[J].计算机集成制造系统,2009,15(12)：2375-2382.
[114]R. Hu, Z. Hu. A Scheduling Algorithm Aimed at Time and Cost for Meta-tasks in Grid Computing Using Fuzzy Applicability[A]. In:Proceedings of the 8th International Conference on High-Performance Computing in Asia-Pacific Region (HPC-Asia'05)[C]. Beijing, China:IEEE Computer Society,2005:564-569.
[115]蒋哲远,韩江洪,王钊.动态的QoS感知Web服务选择和组合优化模型[J].计算机学报,2009,32(5)：1014-1025.
[116]V. Fiolet, B. Toursel. A clustering method to distribute a database on a grid[J]. Future Generation Computer Systems,2007,23(8):997-1002.
[117]陈志刚,杨博.网格服务资源多维性能聚类任务调度[J].软件学报,2009,20(10)：2766-2775.
[118]Parallel Workloads Archive[EB/OL].:http://www.cs.huji.ac.il/labs/parallel/workload, January 2011.
[119]K. S. Golconda, F. Ozguner. A comparison of static QoS-based scheduling heuristics for a meta-task with multiple QoS dimensions in heterogeneous computing[A]. In: Proceeding of the 18th International Parallel and Distributed Processing Symposium (IPDPS 2004)[C]. Santa Fe, New Mexico, USA:IEEE Computer Society,2004: 106-119.
[120]R. K. Jain, D.-M. Chiu, W. R. Hawe. A Quantitative Measure of Fairness and Discrimination for Rresource Allocation in Shared Computer Systems[R]. CoRR, 1998.
[121]Jia Yu, Rajkumar Buyya. A Taxonomy of Scientif Workflow Systems for Grid Computing[J]. SIGMOD Record,2005,34(3):44-49.
[122]T. Andrews, F. Curbera, H. Dholakia et al. Business Process Execution Language forWeb Services[R]. Technical report, BEA Systems, et al.,2003.
[123]T. Fahringer, J. Qin, S. Hainzer. Specification of Grid Workflow Applications with AGWL:An Abstract Grid Workflow Language[A]. In:Proceedings of the 5th International Symposium on Cluster Computing and the Grid(CCGrid 2005)[C]. Cardiff, UK:IEEE Computer Society,2005:676-685.
[124]A. Hoheisel. User tools and languages for graph-based Grid Workflows[J]. Concurrency and Computation:Practice & Experience,2006,18(10):1101-1113.
[125]H. N. Zhao, R. Sakellariou. Scheduling Multiple DAGs onto Heterogeneous Systems[A]. In:Proceedings of the 20th International Parallel and Distributed Processing Symposium (IPDPS 2006)[C]. Rhodes Island, Greece:IEEE,2006: 1-14.
[126]R. Ranjan, M. Rahman, R. Buyya. A Decenralized and Cooperaive Worklow Scheduling Algorithm[A]. In:Proceedings of the 8th International Symposium on Cluster Computing and the Grid(CCGrid 2008)[C]. Lyon, France:IEEE Computer Society,2008:1-8.
[127]M. Wieczorek, S. Podlipnig, R. Prodan et al. Bi-criteria Scheduling of Scientific Workflows for the Gird[A]. In:Proceedings of the 8th International Symposium on Cluster Computing and the Grid(CCGrid 2008)[C], Lyon, France:IEEE Computer Society,2008:9-16.
[128]Y. Zhang, A. Mandal, C. Koelbel et al. Combined Fault Tolerance and Scheduling Techniques for Workflow Applications on Computational Grids[A]. In:Proceedings of the 9th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2009)[C]. Shanghai, China:IEEE Computer Society,2009:244-251.
[129]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.
[130]M. Wieczorek, R. Prodan, A. Hoheisel. Taxonomies of the Multi-criteria Gird Workflow Scheduling Problem[R]. CoreGRID Technical Report Number TR-0106.
[131]DAGMan (Directed Acyclic Graph Manager)[EB/OL].: http://www.cs.wisc.edu/condor/dagman/,2010-6-25.
[132]E. Deelman, G. Singh, M. H. Su et al. Pegasus:a Framework for Mapping Complex Scientific Workflows onto Distributed Systems[J]. Scientific Programming,2005, 13(3):219-237.
[133]I. Taylor, M. Shields, I. Wang. Resource Management of Triana P2P Services[J]. Grid Resource Management,2004:451-462.
[134]A. S. MCGough, L. Young, A. Afzal et al. Workflow Enactment in ICENI. In:UK e-Science All Hands Meeting, Nottingham, UK:IOP Publishing Ltd,2004:894-900.
[135]T. M. Oinn, M. Addis, J. Ferris et al. Taverna:a tool for the composition and enactment of bioinformatics workflows[J]. Bioinformatics,2004,20(17): 3045-3054.
[136]F. Berman, A. A. Chien, K. D. Cooper et al. The GrADS Project:Software Support for High-Level Grid Application Development[J]. International Journal of High Performance Computing Applications,2001,15(4):327-344.
[137]J. W. Cao, S. A. Jarvis, S. Saini et al. GridFlow:Workflow Management for Grid Computing[A]. In:Proceedings of the 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2003)[C]. Tokyo, Japan:IEEE Computer Society,2003:198-205.
[138]D. W. Erwin, D. F. Snelling. UNICORE:A Grid Computing Environment[J]. Lecture Notes in Computer Science,2001,2150:825-834.
[139]J. Yu, R. Buyya. A Novel Architecture for Realizing Grid Workflow using Tuple Spaces[A]. In:Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing (GRID 2004)[C]. Pittsburgh, PA, USA:IEEE Computer Society,2004: 302-309.
[140]T. Fahringer, R. Prodan, R. Duan et al. ASKALON:A Grid Application Development and Computing Environment [A]. In:Proceedings of the 6th IEEE/ACM International Conference on Grid Computing (GRID 2005)[C]. Seattle, Washington, USA:IEEE,2005:122-131.
[141]Java CoG Kit Karajan Guide[EB/OL].:http://wiki.cogkit.org/wiki/Karajan, 2010-10-20.
[142]B. Ludascher, I. Altintas, C. Berkley et al. Scientific Workflow Management and the KEPLER System[J]. Concurrency and Computation:Practice & Experience, Special Issue on Workflow in Grid Systems,2006,18(10):1039-1065.
[143]F. Suter, F. Desprez, H. Casanova. From Heterogeneous Task Scheduling to Heterogeneous Mixed Parallel Scheduling[J]. Lecture Notes in Computer Science, 2004,3149:230-237.
[144]M. Wieczorek, R. Prodan, T. Fahringer. Scheduling of Scientific Workflows in the ASKALON Grid Environment [J]. ACM SIGMOD Record,2005,34(3):56-62.
[145]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.
[146]Y. Zhao, M. Wilde, I. T. Foster et al. Grid Middleware Service for Virtual Data Discovery, Composition and Integration[A]. In:Proceedings of the 2nd Workshop on Middleware for Grid Computing[C]. Toronto,Ontario,Canada:ACM,2004:57-62.
[147]A. O'Brien, S. Newhouse, J. Darlington. Mapping of Scientific Workflow within the E-Protein Project to Distributed Resources. In:UK e-Science All Hands Meeting, Nottingham, UK,2004:404-409.
[148]GRACE:Grid Application Coordination, Collaboration and Execution[EB/OL].: http://www.cs.uh.edu/-gracce,2011-3-20.
[149]O. Regev, N. Nisan. The POPCORN market Online markets for computational resource [J]. Decision Support Systems,2000,28(1-2):177-189.
[150]Z. G. Hu, P. Xiao. A novel resource co-allocation model with constraints to budget and deadline in computation grid[J]. Journal of Central South University of Technology,2009,16(3):458-466.
[151]郑美光,胡志刚,张凯.网格环境下基于序贯博弈的性能-效率平衡型优化[J]. 华南理工大学学报,2010,38(1)：92-96,107.
[152]胡周军,胡志刚,丁长松.基于博弈论健壮性增强的资源分配模型[J].系统工程理论与实践,2009,29(8)：102-110.
[153]李志洁,程春田,黄飞雪等.一种基于序贯博弈的网格资源分配策略[J].软件学报,2006,17(11)：2373-2383.
[154]R. Buyya. Economic-based Distributed Resource Management and Scheduling for Grid Computing[D]. Australia:Monash University,2002.
[155]S. K. Garg, S. Venugopal, R. Buyya. A Meta-scheduler with Auction Based Resource Allocation for Global Grids [A]. In:Proceedings of the 14th International Conference on Parallel and Distributed Systems (ICPADS 2008)[C]. Melbourne, Victoria, Australia:IEEE,2008:187-194.
[156]Y. K. Kwok, S. Song, K. Hwang. Selfish grid computing:game theoretic modeling and NAS performance results[A]. In:Proceeding of the 5th International Symposium on Cluster Computing and the Grid(CCGrid 2005)[C]. Cardiff, UK:IEEE Computer Society,2005:1143-1150.
[157]K. Rzadca, D. Trystram, A. Wierzbicki. Fair Game-Theoretic Resource Management in Dedicated Grids[A]. In:Proceeings of the seventh International Symposium on Cluster Computing and the Grid (CCGrid 2007)[C]. Rio de Janeiro, Brazil:IEEE Computer Society,2007:343-350.
[158]S. U. Khan, I. Ahmad. Non-cooperative, semi-cooperative, and cooperative games-based grid resource allocation[A]. In:Proceeding of the 20th International Parallel and Distributed Processing Symposium(IPDPS 2006)[C]. Rhodes Island, Greece:IEEE,2006:1-10.
[159]A. Iosup, O. O.Sonmez, S. Anoep et al. The performance of bags-of-tasks in large-scale distributed systems [A]. In:Proceedings of the 17th International Symposium on High-Performance Distributed Computing(HPDC-17 2008)[C]. Boston, MA, USA:ACM,2008:97-108.
[160]中南大学网格计算平台[EB/OL].:http://hpc.csu.edu.cn/Default.aspx,2011-6-15.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |