基于博弈论的可生存网络资源管理研究
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摘要
网络可生存性是网络基本能力的保证,是网络提供服务质量(QoS)的前提和保证。随着网络业务流量日益增大以及网络业务类型多样化,研究网络存在性问题成为目前网络研究的一个热点,也为构建下一代互联网奠定基础。
     网络可生存性通常是以网络连通性以及网络性能、业务容量等性能来度量,影响网络可生存性因素很多,网络可生存性研究方面涉及问题也比较多。本文研究了影响互联网可生存性的关键部件路由器,分析了其网络资源管理方法和现存技术对网络可生存性问题的影响,提出了公平有效的网络资源分配方法,避免或控制了网络拥塞现象发生,从而提高网络可存生性的研究思路。
     本文主要从路由器队列管理技术和路由选择技术两个方面,运用博弈论思想,研究路由器网络资源管理机制,提出了基于路由器的网络资源公平有效分配方案,从而提高网络可生存性。
     具体来说,本文主要研究内容和贡献如下:
     1.研究了网络生存性的相关问题,分析了影响网络可生存性的关键部件,表明保障这些关键部件正常、有效地工作是网络可生存性的基本保证。在此研究基础上,表明针对网络关键部件路由器,寻求其公平有效地网络资源分配方法,避免或控制网络拥塞现象发生,是保证和提高网络可生存性的关键所在。
     2.介绍了博弈论相关概念和知识,着重介绍了博弈问题的解,即Nash均衡存在条件以及最优性等相关问题,分析了博弈论在路由器资源管理中的应用情况以及相关研究状况,为本文提出的解决方案奠定了基础。
     3.介绍了目前路由器资源管理的方案和思路,并针对其中的路由器队列管理方法和路由选择技术进行了分析和研究;基于博弈论思想,提出了将路由器队列管理看作是路由器网络资源管理过程的概念,构建了路由器队列管理博弈模型,确定了该博弈问题的Nash均衡条件;运用上述博弈模型的Nash均衡条件,从理论上证明了目前典型路由器队列管理技术的非均衡性,并表明这种非均衡性将导致网络资源分配的不公平。
    4.在实时业务和非实时业务等多种业务类型共存的网络环境下,研究了路由器网络资源的公平性分配问题,提出了基于博弈论思想的路由器实吋队列管理新方法,该方法提高了网络资源分配的公平性,有效控制和避免网络中拥塞现象,最终提高了网络可生存性。
     文中首先介绍了基于博弈论思想的网络资源管理方法在路由器队列管理中的研究状况;介绍了路由器队列管理博弈模型,研究了基于博弈论的路由器队列管理方法的均衡性条件以及任务流条件;其次,研究了路由器队列管理中的两种关键技术,即丢弃算法和调度算法,提出了基于博弈论路由器丢弃算法和调度算法。另外,针对目前网络,尤其是下一代网络中多媒体等实时任务广泛应用情况,研究了路由器排队算法,提出了一种适合实时特性路由器的排队算法;再次,在结合上述路由器队列管理关键技术基础上,提出了一种基于博弈论思想的路由器实时队列管理方法;最后,构建了算法实例,设计了实验模型并用实验方法仿真了算法结果,同时对算法结果进行了分析和比较。
     5.分析了路由器路由选择技术对影响网络可生存性的影响,表明了路由器将业务均衡地转发网络各个路径上,能避免大量业务集中在最短路径或处理能力强的路径上,导致网络拥塞现象,影响网络可生存性。文中针对IPv6协议中任意播路由中的均衡路由问题,提出了基于博弈论思想的优化路由算法,即均衡的路由算法,从路由角度提高网络连通性和效率,保证网络可存生性。
     文中首先介绍了网络路由均衡性选择的研究状况;介绍了合作参与者间的博弈理论;其次,构建了合作参与者间的博弈模型,提出了基于合作博弈的均衡路由算法,并用实验方法仿真了算法结果;再次,在合作博弈模型基础上,进而研究了实际网络路由状况,构建了非合作参与者间的博弈模型,提出了一基于非合作博弈的均衡路由算法,并用实验方法仿真了算法结果。
Internet survivability ensures the internet basical ability,and it is the premise and guarantee of QOS. With the increasing of traffic and many kinds of traffic,the research in Internet survivability is becoming a focus problem at present. Internet survivability is also a basic problem in the next generation internet(NGN).
    Generally, Internet survivability is measured by connection,capability and traffic efficiency. Internet survivability is affected by many factors and there are many questions involved in the research. In this paper,the router which is a major components affecting the Internet survivability is studied, the internet resource management and the influence of current technology to Internet survivability is analysed, a fair and effective method to distribute the internet resource is presented. This investigation can avoid or control the internent congestion,and improve the Internet survivability.
    The main content of this paper is: discusses the management mechanism of the router internet resource, presents a fair and effective method on the router to distribute the internet resource with game theory in two ways, that is router Quene Management and router election technology,which improve the Internet survivability.
    The importment content and contribution is described in detail as follow:
    1. This paper discusses the relative problem with Internet survivability,analyse the major parts which affect Internet survivability, indicates that the major parts running normally and effectively is the basical guarantee of Internet survivability. Based on this investigation, the paper indicates that it is the key to ensure and improve Internet survivability on the router to looking for a fair and effective method to distribute the internet resource, avoiding or controlling the internent congestion.
    2. This paper introduces the relative concept and Nash on emphasis of the game theory, including the relative problem of Nash equilibrium survival condition and the best information. In this paper, the game theory appliance and relative research status
    in router resource management is analysed, and the basical solving scheme of this paper is established.
    3. This paper introduces the scheme of current router resource management, analyses the router Quene Management and the router path selection technology.Up to the game theory, this paper presents the concept that the router Quene Management is considered as a process of router internet resource management, builds the router Quene Management game model, and conforms the Nash equilibrium condition of the game. Due to the Nash equilibrium condition of the game model, the unequilibrium of the current typical router Quene Management technology is proved in theory, and this is considered that will lead to unfair resource distribution.
    4. This paper discusses the fair router internet resource distribution in internet with real time traffic and nonreal time traffic, and presents the new method of router queen management based the game theory. This method can improve the fair internet resource distribution,control and avoid effectively internet traffic, and improve the Internet survivability finally.
    Firstly, this paper introduces the research of internet resource management by game theory in router Quene Management, and also introduces the router Quene Management game theory model, studys the equilibrium condition and task condition of the router Quene Management with game theory. Secondly, two important technologies that is the drop and schedule arithmetic in the router queue management is discussed, and the drop and schedule arithmetic by game theory is presented. Moreover, this paper presents a real time router quene method for internet with real time traffic and non real time traffic. Thirdly, based the research of the major technologies, a real time router queue arithmetic based game theory is presented. At last, this arithmetic and the experiment mode are built,this experiment result is compared with the other typical methods.
    5. In this paper, the influence of the router path selection to the internet survivability is analysed, and the conclusion indicates that the equilibrium router path selection can avoid more traffic in the shortest path, which may lead to internet congestion and affect internet survivability. To the equilibrium router in Ipv6 anycast,This paper optimize the router arithmetic, that is a equilibrium router choice
    method,which can improve internet connection and efficiency, ensure the internet survivability,
    In this paper, the research of the equilibrium router choice is introduced and the basic of the cooperative player game theory is studeid firstly. Secondly,the game model is built, a cooperative player game arithmetic on the equilibrium router is presented, and the experiment indicates the performance of this arithmetic.Thirdly, based on the cooperative player model,the internet router in practice is studied, a noncooperative player model is built, an equilibrium router path selection arithmetic with this model is presented, and the experiment indicates the performance of this arithmetic.
引文
[1] R.J. Ellison. Survivable Network Systems: An Emerging Discipline. Tech. Report CMU/SEI-97-TR-013. Pittsburgh Perm.:Software Engineering Institute, Carnegie Mellon University, Nov. 1997.
    
    [2] R.J. Ellison . A Case Study in Survivable Network System Analysis. Tech. Report CMU/SEI-98-TR-014.Pittsburgh Penn.: Software Engineering Institute, Carnegie Mellon University, Sept. 1998.
    
    [3] R.C. Summers. Secure Computing: Threats and Safeguards. New York : McGraw-Hill, 1997.
    
    [4] V. Mendiratta. Assessing the Reliability Impacts of Software Fault-Tolerance Mechanisms. Proceedings of the The Seventh International Symposium on Software Reliability Engineering (ISSRE '96). 1996, pp 99-103.
    
    [5] Cankaya, H.C.. A survivability assessment tool for restorable networks. 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology, IEEE. 2000, pp. 319-324.
    
    [6] Louca, S., Pitsillides. On network survivability algorithms based on trellis graph transformations. International Symposium on Computers and Communications, IEEE. 1999, pp. 1008-1023.
    
    [7] Jiang,T.Z.. A new definition of survivability of Communication networks. Military Communications Conference: Military Communications in a Changing World, IEEE. 1991, pp 2007-2012.
    
    [8]Hagin, A.A.. Performability, reliability, and survivability of communication networks: systems of methods and models for evaluation. Proceedings of the 14th International Conference on Distribueted Computing Systems, IEEE, 1994, pp 912-916.
    
    [9] Jha,S.,Wing, J.M.. Survivability analysis of networks systems. Proceedings of the 23~(rd) International Conference on Software Engineering, IEEE. 2001,pp.872-874.
    
    [10]Liew, S.C., Lu,K.W., A framework for network survivability characterization, IEEE. 1992. pp441-451.
    
    [11]Ellison, R.J., Linger.. Surviavale network system analysis: a case study. IEEE Software. 1999, Jul-Aug,Volume 16,IEEE. pp.58-62.
    
    [12]Moitra ,Soumyo D., Suresh L. . A Simulation Model for Managing Survivalility of Networked Information Systems. SEI. 2001.
    [13] A. S. Tanenbaum. Comuputer Networks. 4th. New Jersey: Prentice Hall PTR, 2003, pp384-417.
    [14] S. Floyd. Congestion Control Principles. Request for Comments 2914. Internet Engineering Task Force. 2000, Sep.
    [15] 罗万明,林闯,阎宝平.TCP/IP拥塞控制研究.计算机学报.2001,24(1):1-18.
    [16] D. Fudenberg, J. Tirole. Game Theory. MIT Press. 1991.
    [17] Roger B. Myerson, Game Theory: Analysis of Conflict. Harvard University Press. 1991.
    [18] A. R. Greenwald. Learning to Play Network Games: Does Rationality Yield Nash Equilibrium?. PhD thesis. Department of Computer Science, New York University. 1999, May.
    [19] 张培刚.微观经济学的产生和发展.第九章:博弈论的由来及其新近的发展和应用.湖南人民出版社.1997.pp 373-421.
    [20] John von Neumann, Oskar Morgenstern.Theory of Games and Economic Behavior. John Wiley & Sons. 1964.
    [21] J. F. Nash.The bargaining Problem. Econometrica. 1950, 18: 155-162.
    [22] J. F. Nash. Noncooperative games. Annals of Mathematics. 1951, 54: 289-295.
    [23] 张维迎.博弈论与信息经济学.上海:上海人民出版社.1996.
    [24] W.Stallings.High-speed Networks:TCP/IP and ATM Design Principles,(中译本).北京:电子工业出版社.1999.
    [25] Sudipta Rakshit, Ratan K. Guha. Fair Bandwidth Sharing in Distributed Systems: A Game-Theoretic Approach. IEEE TRANSACTIONS ON COMPUTERS. 2005, VOL. 54, NO. 11.
    [26] Li Chunlin, Li Layuan. A Utility-based Two Level Market Solution For Optimal Resource Allocation In Computational Grid. Proceedings of the 2005 International Conference on Parallel Processing (ICPP'05). 2005.
    [27] O"zgu"r Erc. Market-Based Resource Allocation for Content Delivery in the Internet. IEEE TRANSACTIONS ON COMPUTERS.2003, VOL.52,NO. 12.
    [28] Zhonghang Xia. A Distributed Admission Control Model for QoS Assurance in Large-Scale Media Delivery Systems. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS. 2005, VOL. 16, NO. 12.
    [29] Haitao Lin, Mainak Chatterjee. ARC: An Integrated Admission and Rate Control Framework for Competitive Wireless CDMA Data Networks Using Noncooperative Games. IEEE TRANSACTIONS ON MOBILE COMPUTING.2005,VOL. 4, NO. 3.
    
    [30] Preetam Ghosh, Nirmalya Roy. Apricing strategy for job allocation in mobile grids using a non-cooperative bargaining theory framework. J. Parallel Distrib. Comput. 2005,pp1366- 1383.
    
    [31]Daniel Grosu. Load Balancing in Distributed Systems: An Approach Using Cooperative Games. Proceedings of the International Parallel and Distributed Processing Symposium (IPDPS.02). 2002.
    
    [32] V. Srinivasan, P. Nuggehalli, C. F. Chiasserini. Cooperation in Wireless Ad Hoc Networks. IEEE INFOCOM 2003.
    
    [33] E.Koutsoupias,C.Papadimitriou. Worst-case equilibria. In Proc. of the 16~(th) Annual Symp.on Theoretical Aspects of Computer Science. 1999, pp404-413.
    
    [34] A.A.Economides. A Unified Game-Theoretci Methodology for the Joint Load Sharing. Routing and Congestion Control Problem. Phd thesis.Department of Computer Eng., University of Southerm California. Auguest 1990.
    
    [35] T. Roughgarden, E. Tardos. How Bad Is Selfish Routing? Journal of the ACM. 2002, 49(2): 236-259.
    
    [36] S. Shenker. Making Greed Work in Networks: A Game-Theoretic Analysis of Switch Service Disciplines. In Proceedings of SIGCOMM. London,UK.1994,pp 47-57.
    
    [37] R. Garg, A. Kamra, V. Khurana. A Game-Theoretic Approach Towards Congestion Control in Communication Networks. In Proceedings of ACM SIGCOMM Computer Communications Review. 2002, 32(3): 47-61.
    
    [38] M.Allman,V.Paxson,W.Stevnes. TCP Congestion Control. IETF RFC2581.1999.
    
    [39] W.Stevens. TCP Slow Start, Congestion Avoidance, Fast Retransmit, and Fast Recovery Alogrithms. IETF RFC2001. 1997.
    
    [40] S.Floyd,K.Fall. Promoting the use of End-to-End congestion control in the Internet. IEEE/ACM Transaction on Networking. 1997.
    
    [41] S.Floyd. A Report on Some Recent Developments in TCP Congestion Control. IEEE Communication Magazine. 2001.
    
    [42] M.Mathis, J.Mahdavi. TCP Selective Acknowledgment Options. IETF RFC2018,1996.
    
    [43] S.Floyd, J.Mahdavi, M.Mathis. An Extension to the Selective Acknowledgement (SACK) Option for TCP. IETF RFC2883. 2000.
    
    [44] J. Nagle. On Packet Switches with Infinite Storage. IEEE Transactions on Communication. 1987, 4(35): 435-438.
    [45] M. Shreedhar, G. Varghese. Efficient Fair Queuing Using Deficit Round Robin. In Proceedings of SIGCOMM'95 Conf., ACM. 1995, 231-243.
    [46] J. C. R. Bennett, H. Zhang. WF2Q: Worst-case Fair Weighted Fair Queuing. In Proceedings of INFOCOM. San Fransisco, California. 1996, 120-128.
    [47] J. Postel. Transmission Control Proltocol. IETF RFC791. 1981.
    [48] J. Nagel. Congestion Control in IP/TCP Internetworks. IETF RFC890. 1984.
    [49] M. Allman, V. Paxson, W. Stenvens. TCP Congestion Control. IETF RFC2581. 1999.
    [50] S. Floyd, V. Jacobson. Random Early Detection Gateways for Congestion Avoidance. ACM/IEEE Transactions on Networking. 1993, 1(4): 397-413.
    [51] S. Floyd. TCP and explicit congestion notification. ACM Computer Communication Review. 1994, 24(5): 10-23.
    [52] S. Floyd, K. Fall. Promoting the use of end-to-end congestion control in the Internet. IEEE/ACM Transactions on Networking. 1999, 7(4): 458-472.
    [53] S. Athuraliya, S. H. Low. REM: Active Queue Management. IEEE Network. 2001, 15(3): 48-53.
    [54] T. Alpcan, T. Basar. A Game-Theoretic Framework for Congestion Control in General Topology Networks. IEEE CDC'02: 1218~1224.
    [55] V. Srinivasan, P. Nuggehalli. Cooperation in Wireless Ad Hoe Networks. IEEE INFOCOM 2003.
    [56] C. U. Saraydar, N. B. Mandayam. Efficient Power Control via Pricing in Wireless Data Networks.IEEE Wireless Communications and Networking Conference 1999.
    [57] (法)休特马.克里斯琴.因特网路由技术.陶文星译.北京:清华大学出版社.西蒙与舒斯特国际出版公司,1998.
    [58] ApplebyS., Steward S.Mobile Software Agents for Control in Telecomunications Networks. BT Technology Jouranal. 1994,12(2): 104-113.
    [59] Bonabeau E, Dorigo M, Theraulaz . Inspiration for Optimization form Social Insect Behavior. Nature. 2000, 406(July): 39-42.
    [60] Shin K G, Chou C C.A distributed route selection scheme for establishing real time channels[C].In:Proceedings of the 6the IFIP International Con on High Performance Networking, Istanbul, Turkey. 1995: 319-329.
    [61] Chen S, Nahrstedt K.On Finding Multi-Constrained Paths[C]. In: IEEE ICC'98. 1998.
    [62] Yuan Xin, Liu Xingming. Heuristic Algorithms for Multi Constranited Quality of Service Routing. IEEE INFOCOM 2001, anchourange, Alaska. April 2001: 844-853.
    [63] Korkmaz T. A randomized algorithm for finding a path subject to multiple QoS contraints[C]. Proc of the IEEE GLOBECOM'99 Conference-Symposium on Global Internet.Appliacationa and Technology. 1999: 1694-1698.
    
    [64] ZHAO Jian, WU Jie-yi,GU Guan-qun. A class of network quality of service based unicast routing algorithm[J]. Joumal of China Insititue of Communications, 2001,22(1):30-41.
    
    [65] Bauer Fred,Varma Anujan. Degree constrained multicasting in point to point networks[C]. Proc IEEE INFOCOM'95,Boston, Massachusettes. 1995,369-376.
    
    [66] SHI Jian, ZOU ling. The Application of Genetic Algorithm in Multicast Routing[J]. ACTA ELECTRONICA SINICA.2000,28.88-89.
    
    [67] H.Miura, and M.Yamamoto. Server Selection Policy in Acitve Anycast, IEICE Transactions on Communications.2001,Vol.E84-B.No. 10.
    
    [68] S.Yu,W.Zhou,and Y.Wu. Research on Network Anycast. Proceedings of the Fifth International Conference on Algorithms and Architectures for Parallel Processing. 2002.
    
    [69] C. Douligeris, R. Mazumdar. On Pareto optimal flow control in an integrated environment.Proc. of the 25th Allerton Conference on Communication.Control and Computing. Oct 1987.
    
    [70]Y. A. Korilis, A. A. Lazar.On the existence of equilibria in noncooperative optimal flow control. Journal of the ACM. 1995, 42(3). 584-613.
    
    [71]S. Shenker. Making greed work in networks: A game-theoretic analysis of switch service disciplines.IEEE/ACM Transactions on Networking. 1995,vol. 3.
    
    [72]K. Park, M. Sitharam. Quality of Service provision in noncooperative networks: heterogeneous preferences, multi-dimensional Qos vectors, and burstiness. Proc. 1st International Conference on Information and Computation Economies. 1998.
    
    [73] R. Gibbens and F. Kelly, Resource pricing and the evolution of congestion control, 1998.
    
    [74] F. Kelly, A. Maulloo, D. Tan. Rate control in communication networks: shadow prices. proportional fairness and stability.in Journal of the Operational Research Society. 1998, vol. 49.
    
    [75] A. Akella, R. Karp, C. Papadimitrou. Selfish behavior and stability of the Internet: A game-theoretic analysis of TCP,ACM SIGCOMM. 2002.
    
    [76] R. Pan, B. Prabhakar, K. Psounis. CHOKe - A stateless queue management scheme for approximating fair bandwidth allocation, in INFOCOM.2000.
    
    [77]D.Dutta,A.Goel. Oblivious AQM and Nash Equilibria. IEEE INFOCOM. 2003,April.
    [78]Juan A.Almendral. Oblivious Router Polices and Nash Equilibrium. IEEE Symposium on Computers and Communications .2004,June.
    
    [79]A. K. Parekh, R. G. Gallager. A Generalized Processor Sharing Approach to Flow Control - the Signle Node Case. In Proceedings of INFOCOM, 1992.
    
    [80] Leoczkey J. Fixed priority scheduling of periodic task sets with arbitrary deadlines. 11~(th) IEEE Real-Time Systems Symposium.IEEE Computer Society Press. 1990. 201-209.
    
    [81]Kao B, Molina HqAdelberg B. On building soft real-time systems. In the third workshop on Parallel and Distributed Real-time Systems. Santa Barbara.CA. USA. 1995.
    
    [82]Chang E, Zakor A. Scalable video coding using 3-D sub-band velocity coding and multi-ratequantization. IEEE international conference on Acoustics, speech, and signal processing,Minneapolis, Minnesota, 1993
    
    [83]Liu C L, Layland J. Scheduling algorithms for multiprogramming in real-time systems.Journal of the ACM. 1973, 20(1).46-61.
    
    [84]Leung J, Whitehead J. On the complexity of fixed-priority scheduling of periodic, real-time tasks. Performance Evaluation, 1982,2(12). 237-250.
    
    [85]Audsley N C. Optimal priority assignment and feasibility of static priority tasks with arbitrary start times. YCS 164, Dept. Computer Science.University of York. 1991.
    
    [86] Audsley N, Burns A, Tindell K. Applying new scheduling theory to static priority preemptive scheduling. Software Engineering. 1993,8(5).284-292.
    
    [87] MercerW .An introduction real-time operating system: scheduling theory. School Of Computer Science. Carnegie Mellon University.
    
    [88] A. Chandra, M. Adler. Deadline fair scheduling: Bridging the theory and practice of proportionate-fair scheduling in multiprocessor servers. In Proc. of the 7th IEEE Real-Time Technology and Applications Symposium. 2001,May.3-14.
    
    [89] Goosssens J, Devillers R. Feasibility intervals for the deadline driven scheduler with arbitrary deadlines. The 6th International Conference on Real-Time Computing Systems and Applications, Hong Kong, China. 1999
    
    [90] Cherto H, Cheto M. Some results of the earliest deadline scheduling algorithm. IEEE Transactions on Software Engineering. 1989, 15(10). 1261-1269.
    
    [91] Spuri M. Holistic analysis for deadline scheduled real-time distributed systems. TechniqueReport. INRIA.1996, No 2873.
    
    [92] George L, Rivierre N, Sprui M. Preemptive and non-preemptive Real-time uniprocessor scheduling. Technique Report.WRIA. 1996, No 2966.
    
    [93] Baruah S K, Howell R R. Feasibility problems for recurring tasks on one processor. Theor.Comput. Sci. 1993,118(1)3-20.
    
    [94] Spuui M. Analysis of Deadline scheduling real-time systems. Technique Report, WRIA.1996,No 2722.
    
    [95] T.Casavant, J.G.. Kuhl. A taxonomy of scheduling in general-purpose distributed computing systems.IEEE Trans. Software Eng.1998, February. 14(2):141-154.
    
    [96] A.N.Tantawi, D.Towsley. Optimal static load balancing in distributed computer systems.Jouranal of the ACM. 1985, Aplil. 32(2):445-465.
    
    [97] C.Kim, H.Kameda. An algorithm for optimal static load balancing in distributed computer systems. IEEE Trans. Comput. 1992, March . 41(3):381-384.
    
    [98] J.Li,H.Kameda. A decomposition algorithm for optimal static load balancing in tree hierarchy network configuration. IEEE Trans. Parallel and Distributed Syst. 1994, May .5(5):540-548.
    
    [99] J.Li ,H.Kameda. Optimal static load balancing in star network configurations with two-way traffic. J. of Parallel and Distributed Computing. 1994, December.23(3): 364-375.
    
    [100]X.Tang, S.T.Chanson. Optimizing static job scheduling in a network of heterogeneous computers. In Proc. Of the Intl. Conf. on Parallel Processing. 2000, August.pp 373-382.
    
    [101]Y.C.Chow, W.H.Kohler. Models for dynamic load balancing in a heterogeneous multiple processor system. IEEE Trans. Comput. 1979, May. C-28(5):354-361.
    
    [102] F.Bonomi and A.Kumar. Adaptive optimal load balancing in a nonhomogeneous multiserver system with a central job scheduler. IEEE Trans. Comput. 1990, October .39(10): 1232-1250.
    
    [103] S.Shenker,A.Weinrib.The optimal control of heterogeneous queueing systems: A paradigm for load-sharing and routing.IEEE Trans. Comput. 1989, December. 38(12):1724-1735.
    
    [104] H.C.Lin ,C.S.Raghavendra. A dynamic load-balancing policy with a central job dispatcher. IEEE Trans. Software Eng. 1992, February. 18(2):148-158.
    
    [105]K.K.Goswami,M.Devarakonda. Prediction-based dynamic load-sharing Heuristics . IEEE Trans. Parallel and Distributed Syst. 1993, June .4(6):638-648.
    
    [106]P.Kulkami,I.Sengupta. A new approach for load balancing using differerntial load measurement. In Proc. of Intl. Conf. on Information Technology: Coding and Computing.2000, March .pp355-359.
    [107] I.Ahmad, A.Ghafoor. Semi-distributed load balancing for massively patallel multicomputer systems. IEEE Trans. Software Eng. 1991, October. 17(10): 987- 1003.
    
    [108]V.Kumar, A.Y.Grama, N..R.Vempaty. Scalable load balancing techniques for parallel computers. J.Parallel and Distributed Computing. 1994, July .22(1):60-79.
    
    [109] M.Schaar,K.Efe, L.Delcambre L.N.Bhuyan. Load balancing with network cooperation. In Proc. Of the 11~(th) IEEE Intl.Conf. on Distributed Computing Systems. 1991, May .pp 328-335.
    
    [110] M.H.Willebeek-Le Mair, A.P.Reeves. Strategies for dynamic load balancing on highly parallel computers. IEEE Trans. Parallel and Distributed Syst. 1993, September. 4(9):979-993.
    
    [111] J.A.Stankovic. An application of Bayesian decision theory to decentralized control of job scheduling. IEEE Trans. 1985, Febrauary. C-34(2):117-130.
    
    [112] K.M.Yu,S.J.Wu , T.P Hong. A load balancing algorithm using prediction. In Proc. Of the 2nd Aizy Intl. Symp. On Parallel Algorithms/Architecture Synthesis. 1997, March. pp 159-165.
    
    [113] S.H.Lee , C.S.Hwang. A dynamic load balancing approach using genetic algorithm in distributed systems. In Proc. Of the IEEE Intl. Conf. on Evolutonary Computation. 19984,May.pp 639-640.
    
    [114] S.Dierkes. Load balancing with a fuzzy-decision algorithm. Information Sciences. 1997. 97(1-2):159-177.
    
    [115] W.Obeloer, C.Grewe, H.Pals. Load management with mobile agents. In Proc. Of the 24~(th) Euromicro Conf., volume2. 1998, August. ppl005-1012.
    
    [116]Y.C.Chow,W.H.Kohler. Models for dynamic load balancing in heterogeneous multiple processor system. IEEE Trans.Comput. 1979, May. C-28(5):354-361.
    
    [117]A.Orad, R.Rom. Competitive routing in multiuser communication networks. IEEE/ACM Trand. Networking. 1993, Octorber. 1(5):510-5f21.
    
    [118] E.Altman, T.Basar. Routing in two parallel links: Game-theoretic distributed algorithms. J.Parallel and Distributed Computing. 2001,September.61(9):1367-1381.
    
    [119] E.Koutsoupias and C.Papadimitriou. Worst-case equilibria. In Proc. of the 16~(th) Annual Symp.on Theoretical Aspects of Computer Science,pages 404-413,1999.
    
    [120] T.Rougharden, E.Tardos. How bad is selfish routing? In Proc. of the 41th IEEE Symp. on Foundation of Computer Science. 2000,November. pp93-102.
    
    [121] Y.A.Korilis, A,A,Lazar.Achieving network optima using Stackelberg routing strategies. IEEE/ACM Trans. Networking. 1997, February .5(1): 161-173.
    [122] A.A.Economides. A Unified Game-Theoretci Methodology for the Joint Load Sharing. Routing and Congestion Control Problem. Phd thesis, Department of Computer Eng., University of Southerm California. 1990, Auguest.
    
    [123]A.Muthoo.Bargaining Theory with Applications. Cambridge Univ. Press, Cambridge.U.K. 1999.
    
    [124] J.Nash.The bargaining problem.Econometrica. 1950, April. 18(2):155-162.
    
    [33] C.H.Papadimitriou and M.Yannakakis. On the approximability of trade-offs and optimal access of web sources. In Proc. of the 41th IEEE Symp. on Foundations of Computer Science, pages 86-92,November 2000.
    
    [125] A.Stefanescu , M.V.Stefanescu. The arbitrated solution for multi-objective convex programming. Rev.Roum. Math.Pure Appl. 1984.29:593-598.
    
    [126]H.Yaiche,R.R.Mazumdar,and C.Rosenberg. A game theoretic framework for bandwidth allocation and pricing in broadband networks. IEEE/ACM Trans. Networking. 2000,October. 8(5):667-678.
    
    [127]D.G.Luenberger. Linear and Nonlinear Programming. Addison-Wesley, Reading, Mass. 1984.
    
    [128] R.K.Jain,D.M.Chiu,and W.R.Hawe. A quantitative measure of fariness and discrimination for resource allocation in shared computer system. Technical Report DED-TR-30. Digital Equipment Corporation. Eastern Research Lan. 1984.
    
    [129]Y.C.Chow,W.H.Kohler. Models for dynamic load balancing in a heterogeneous multiple processor system. IEEE Trans. Comput. 1979, May. C-28(5):354-361.
    
    [130] X.Tang ,S.T.Chanson. Optimizing static job scheduling in a network of heterogeneous computers. In Proc. Of the Intl. Conf. on Parallel Processing. 2000 ,August .pp 373-382.
    
    [131]S.A.Banawan, J.Zahorjan. Load sharing in heterogeneous queueing systems. In INFOCOM'89, Proc. of the 8th Ann. Jonit Conf. of the IEEE Computer and Communications Societies. 1989, April .volume2, pages 731-739.
    
    [132]A.N.Tantawi,D.Towsley. Optimal static load balancing in distributed computer systems.Jouranal of the ACM. 1985, Aplil. 32(2):445-465.
    
    [133]Y.C.Chow, W.H.Kohler. Models for dynamic load balancing in a heterogeneous multiple processor system. IEEE Trans. Comput. 1979, May .C-28(5):354-361.

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