网格资源定价机制和交易策略研究
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摘要
随着网格研究的不断深入,面对日益复杂的网格应用需求,在资源有限的条件下,如何对资源进行分配和管理,满足各种应用的不同服务质量要求,提高资源利用率,成为网格研究的一个热点问题。
本文在对现有各类网格经济模型研究分析和性能比较基础上,通过深入理解每种经济模型的运行机制,分析了各经济模型的优缺点,并对基于博弈理论的网格资源定价机制和交易策略进行了归纳总结和比较。针对网格资源松弛预留问题,通过在定价策略中引入松弛时间,提出一种用于网格资源松弛预留的连续双向拍卖模型。模型中买方通过剩余时间和剩余资源量出价,卖方则根据负载情况要价,当系统剩余时间和剩余资源量减少时买方提高出价,而当系统中负载减少时,卖方适当降低要价。
仿真实验结果表明,对于具有费用约束的网格任务,本文提出的模型与基于传统预留策略的连续双向拍卖模型相比,可增加约21%的资源总收益和15%的资源利用率。
With the development of grid computing, the QoS requirements of grid applications have become more and more complex. How to manage the distributed resources and provide satisfying QoS for grid application is always the key issue in grid environments, especially when the capability of grid resources is limited by various factors.
In this paper, the popular computing economic models are studied in detail first. Then, the grid resource pricing mechanisms and trading strategies that based on game theory were summarized and compared. Finally, a continuous double auction model for the relaxed reservation of grid resource is proposed. The concept of relaxed time is introduced into the pricing strategy of the model.The buyers determine their price by the amount of remaining time and remaining resources.The sellers ask price under the workload of the system.The bid will increase when the remaining time and remaining resources are reducing, and when the workload of the grid system is reducing the ask price will reduce.The simulation result show that the model proposed is better than the continuous double auction model based on the conventional reservation strategy.The profit of resource provider increases by 21% and the rate of resource utilization increases by 15%.
本文在对现有各类网格经济模型研究分析和性能比较基础上,通过深入理解每种经济模型的运行机制,分析了各经济模型的优缺点,并对基于博弈理论的网格资源定价机制和交易策略进行了归纳总结和比较。针对网格资源松弛预留问题,通过在定价策略中引入松弛时间,提出一种用于网格资源松弛预留的连续双向拍卖模型。模型中买方通过剩余时间和剩余资源量出价,卖方则根据负载情况要价,当系统剩余时间和剩余资源量减少时买方提高出价,而当系统中负载减少时,卖方适当降低要价。
仿真实验结果表明,对于具有费用约束的网格任务,本文提出的模型与基于传统预留策略的连续双向拍卖模型相比,可增加约21%的资源总收益和15%的资源利用率。
With the development of grid computing, the QoS requirements of grid applications have become more and more complex. How to manage the distributed resources and provide satisfying QoS for grid application is always the key issue in grid environments, especially when the capability of grid resources is limited by various factors.
In this paper, the popular computing economic models are studied in detail first. Then, the grid resource pricing mechanisms and trading strategies that based on game theory were summarized and compared. Finally, a continuous double auction model for the relaxed reservation of grid resource is proposed. The concept of relaxed time is introduced into the pricing strategy of the model.The buyers determine their price by the amount of remaining time and remaining resources.The sellers ask price under the workload of the system.The bid will increase when the remaining time and remaining resources are reducing, and when the workload of the grid system is reducing the ask price will reduce.The simulation result show that the model proposed is better than the continuous double auction model based on the conventional reservation strategy.The profit of resource provider increases by 21% and the rate of resource utilization increases by 15%.
引文
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[2]I. Foster,C. Kesselman,S. Tuecke.The Anatomy of the Grid:Enabling Scalable Virtual Organizations [J]. Internal Journal of High Performance Computing Applications,2001,15(3):200-222.
[3]I. Foster,C. Kesselman,J.M.Nick,et al.The Physiology of the Grid:An Open Grid Services Architecture for Distributed Systems Integration [R].Globus Project,2002.
[4]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 [C].Proceedings of IEEE/ACM International Symposium on Cluster Computing and the Grid,Rio de Janeiro,2007:840-845.
[5]H, Duque,J. Montagnat,J.mPierson, et al.DM2:A Distributed Medical Data Manager for Grids [C]. Proceedings of IEEE/ACM International Symposium on Cluster Computing and the Grid,Tokyo,2003:606-611.
[6]Europe DataGrid.http://www.eu-datagrid.org/
[7]T. Mori, F. Siebenlist. OGSATM Basic Security Profile 1.0-Core[S]. Open Grid Services Architecture Working Group, GFD-R-P86,Open Grid Forum,2006.
[8]袁禄来,曾国荪,姜黎立,蒋昌俊.网格环境下基于新人模型的动态级调度[J].计算机学报,2006,29(6)：1241-1244.
[9]Y. M.Matsumoto,S. Data,T. Kaishima, et al.A Grid Application for an Evaluation of Brain Function using Independent Component Analysis(ICA)[C]. Proceedings of IEEE/ACM International Symposium on Cluster Computing and the Grid,Berlin,2002:871-872.
[10]V. Breton,A.E. Solomonides,R.H. McClatchey.A Perspective on the Healthgrid Initiative [C]. Proceedings of IEEE/ACM International Symposium on Cluster Computing and the Grid,Chicago,2004:434-439.
[11]金海,孙傲冰,郑然,何儒汉,章勤,吴松.医学图像网格基于语义的信息集成方法[J].软件学报,2007,18(8)：2049-2062.
[12]J.M. Pierson,L. Brunie,C. Dhaenens, et al.A. Tchounikine. Grid for Geno-Medicine:A Glimpse on the GGM Project [C]. Proceedings of IEEE/ACM International Symposium on Cluster Computing and the Grid,Cardiff,2005:527-528.
[13]D. Sulakhe,A. Rodriguez,M. Wilde, et al.Using Multiple Grid Resources for Bioinformatics Applications in GADU [C]. Proceedings of IEEE/ACM International Symposium on Cluster Computing and the Grid Workshops,Singapore,2006.
[14]J. Zhu,A. Guo,Z.H. Lu, et al.Analysis of the bioinformatics Grid Technique Applications in China[C]. Proceedings of IEEE/ACM International Symposium on Cluster Computing and the Grid Workshops,Singapore,2006.
[15]W.W. Li,S. Krishnan,K. Mueller, et al.Building Cyberinfrastructure for Bioinformatics Using Service Oriented Architecture [C]. Proceedings of IEEE/ACM International Symposium on Cluster Computing and the Grid Workshops,Singapore,2006.
[16]V. Kasam, J. Salzemann,N. Jacq, et al.Large Scale Deployment of Molecular Docking Application on Computational Grid infrastructures for combating Malaria [C]. Proceedings of IEEE/ACM International Symposium on Cluster Computing and the Grid, Rio de Janeiro,2007:691-700.
[17]S. Dong,N.T. Karonis,G.E. Karniadakis.Grid Solutions for Biological and Physical Cross-Site Simulations on the TeraGrid [C]. Proceedings of International Symposium on Parallel and Distributed Processing Symposium,Rhodes Island,2006.
[18]R.Pennington. Terascale Cluster and the TeraGrid [C], Proceedings of High Performance Conference in Asia, India,2002:407-413.
[19]TeraGrid. http://www.teragrid.org/.
[20]何儒汉,金海,廖振松,章勤ChinaGrid图像处理网格平台的语义信息服务研究[J].计算机研究与发展,2006,43(5)：821-827.
[21]J. Tian,K.K. Ma. Super-resolution Imaging Using Grid Computing[C]. Proceedings of IEEE/ACM International Symposium on Cluster Computing and the Grid, Rio de Janeiro,2007:293-300.
[22]S. Hastings,T. Kurc.S. Langella, et al.Image Processing for the Grid:A Toolkit for Building Grid-enabled Image Processing Applications [C]. Proceedings of IEEE/ACM International Symposium on Cluster Computing and the Grid,Tokyo,2003:36-43.
[23]N. Chrisochoides,A. Fedorov,A. Kot, et al.Toward Real-Time Image Guided Neurosurgery Using Distributed and Grid Computing [C]. Proceedings of IEEE/ACM Conference on Supercomputing,Florida,2006:37-50.
[24]I. Foster,C. Kesselman.Globus:A Metacomputing Infrastructure Toolkit [J]. International Journal of Supercomputer Applications,1996,11(2):115-128.
[25]The Globus Alliance.http://www.globus.org/.
[26]I. Foster,H. Kishimoto,A. Savva, et al.The Open Grid Services Architecture[S].GFD-I.30,Global Grid Forum,2005.
[27]Information Power Grid. http://www.ipg.nasa.gov/.
[28]NPACI Grid. http://npacigrid.npaci.edu/.
[29]Large-hadron Collide Grid. http://lcg.web.cern.ch/LCG/.
[30]廖军,刘锦德,秦志光.全球信息网格与服务计算[J].计算机应用,2006,26(6)：1241-1244.
[3l]NCBioGrid. http://www.ncbiogrid.org/.
[32]徐志伟,李晓林,游赣梅.织女星信息网格的体系结构研究[J].计算机研究与发展,2002,39(8)：948-951.
[33]徐志伟,李伟.织女星网格的体系结构研究[J].计算机研究与发展,2002,39(8)：923-929.
[34]ChinaGrid. http://www.chinagrid.edu.cn/.
[35]Yang Xuesheng,Liu Yongzhu,Chen Dehui.China Meteorological Application Grid [J]. Chinese Academy of Meteorological Sciences Annual Report,2005:12-13.
[36]龚强.发展我国地理空间信息网格技术的探讨.地理信息世界,2005,3(5)：47-51.
[37]K. Czajkowski, I. Foster, C. kesselman. Resource Co-Allocation in Computational Grids [C]. Proceedings of International Symposium on High Performance Distributed Computing,California,1999:219-228.
[38]I. Foster, C. kesselman,C. Lee, et al. A Distributed Resource Management Architecture that Supports Advance Reservation and Co-Allocation [C]. Proceedings of International Workshop on Quality of Service,London,1999:27-36.
[39]R.J.Al-Ali, O.F. Rana, D.W. Walker, et al.G-QoSM:Grid Service Discovery Using QoS Properties [J]. Journal of Computing and Informatics,Special Issue on Grid Computing,2002,21(6):363-382.
[4O]R.J.Al-Ali, A. ShaikhAli,O.F. Rana, et al.Supporting QoS-Based Discovery in Service-Oriented Grid [C]. Proceedings of the International Parallel and Distributed Processing Symposium,France,2003.
[41]R.J.Al-Ali, A. Hafid,O.F. Rana, et al.QoS Adaptation in Service-Oriented Grid [C]. Proceedings of the International Workshop on Middleware for Grid Computing.Rio de Janeiro,2003.
[42]R.J.Al-Ali, K. Amin, G.V. Laszewski, et al. Analysis and Provision of QoS for Distributed Grid Applications [J]. Journal of Grid Computing,2004,2(2):163-182.
[43]L.O. Burchard, M. Hovestadt,O. Kao, et al. The Virtual Resource Manager:An Architecture for SLA-aware Resource Management [C]. Proceedings of IEEE/ACM International Symposium on Cluster Computing and the Grid,Chicago,2004:126-133.
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