The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.10 - October (2008 vol.57)
pp: 1413-1422
Albert Y. Zomaya , The University of Sydney, Sydney
Riky Subrata , University of Western Australia
ABSTRACT
A grid differs from traditional high performance computing systems in the heterogeneity of the computing nodes as well as the communication links that connect the different nodes together. In grids there exist users and service providers. The service providers provide the service for jobs that the users generate. Typically the amount of jobs generated by all the users are more than any single provider can handle alone with any acceptable quality of service (QoS). As such, the service providers need to cooperate and allocate jobs among them so that each is providing an acceptable QoS to their customers. QoS is of particular concerns to service providers as it directly affects customers? satisfaction and loyalty. In this paper, we propose a game theoretic solution to the QoS sensitive, grid job allocation problem. We model the QoS based, grid job allocation problem as a cooperative game and present the structure of the Nash Bargaining Solution. The proposed algorithm is fair to all users and represents a Pareto optimal solution to the QoS objective. One advantage of our scheme is the relatively low overhead and robust performance against inaccuracies in performance prediction information.
INDEX TERMS
Load balancing and task assignment, Heterogeneous (hybrid) systems, Distributed architectures, Scheduling and task partitioning
CITATION
Albert Y. Zomaya, Riky Subrata, "A Cooperative Game Framework for QoS Guided Job Allocation Schemes in Grids", IEEE Transactions on Computers, vol.57, no. 10, pp. 1413-1422, October 2008, doi:10.1109/TC.2008.79
REFERENCES
[1] A. Akella, R. Karp, C. Papadimitriou, S. Seshan, and S. Shenker, “Selfish Behavior and Stability of the Internet: A Game-Theoretic Analysis of TCP,” Proc. ACM SIGCOMM '02, pp. 117-130, 2002.
[2] M. Avvenuti, L. Rizzo, and L. Vicisano, “A Hybrid Approach to Adaptive Load Sharing and Its Performance,” J. Systems Architecture, vol. 42, pp. 679-696, 1997.
[3] N. Bansal and M. Harchol-Balter, “Analysis of SRPT Scheduling: Investigating Unfairness,” Proc. SIGMETRICS '01, pp. 279-290, 2001.
[4] C. Boeres, A. Lima, and V.E.F. Rebello, “Hybrid Task Scheduling: Integrating Static and Dynamic Heuristics,” Proc. 15th Symp. Computer Architecture and High Performance Computing, pp. 199-206, 2003.
[5] Y.C. Chow and W.H. Kohler, “Models for Dynamic Load Balancing in a Heterogeneous Multiple Processor System,” IEEE Trans. Computers, vol. 28, pp. 354-361, 1979.
[6] R.B. Cooper, Introduction to Queueing Theory, second ed. Elsevier, 1981.
[7] M.E. Crovella and A. Bestavros, “Self-Similarity in World Wide Web Traffic Evidence and Possible Causes,” IEEE/ACM Trans. Networking, vol. 5, pp. 835-846, 1996.
[8] M.E. Crovella, M. Harchol-Balter, and C.D. Murta, “Task Assignment in a Distributed System: Improving Performance by Unbalancing Load,” Technical Report BUCS-TR-1997-018, 1997.
[9] D. Grosu and A.T. Chronopoulos, “Algorithmic Mechanism Design for Load Balancing in Distributed Systems,” IEEE Trans. Systems, Man, and Cybernetics, vol. 34, pp. 77-84, 2004.
[10] D. Grosu and A.T. Chronopoulos, “Noncooperative Load Balancing in Distributed Systems,” J. Parallel and Distributed Computing, vol. 65, pp. 1022-1034, 2005.
[11] D. Grosu, A.T. Chronopoulos, and M.Y. Leung, “Load Balancing in Distributed Systems: An Approach Using Cooperative Games,” Proc. 16th Int'l Parallel and Distributed Processing Symp., pp. 501-510, 2002.
[12] M. Harchol-Balter, “Job Placement with Unknown Duration and No Preemption,” Performance Evaluation Rev., vol. 28, pp. 3-5, 2001.
[13] M. Harchol-Balter and A.B. Downey, “Exploiting Process Lifetime Distributions for Dynamic Load Balancing,” ACM Trans. Computer Systems, vol. 15, pp. 253-285, 1997.
[14] R. Jain, The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation and Modelling. Wiley-Interscience, 1991.
[15] H. Kameda, J. Li, C. Kim, and Y. Zhang, Optimal Load Balancing in Distributed Computer Systems. Springer, 1997.
[16] C. Kim and H. Kameda, “An Algorithm for Optimal Static Load Balancing in Distributed Computer Systems,” IEEE Trans. Computers, vol. 41, pp. 381-384, 1992.
[17] H.W. Kuhn and A.W. Tucker, “Nonlinear Programming,” Proc. Second Berkeley Symp. Math. Statistics and Probability, pp. 481-492, 1951.
[18] Y.K. Kwok, K. Hwang, and S. Song, “Selfish Grids: Game-Theoretic Modeling and NAS/PSA Benchmark Evaluation,” IEEE Trans. Parallel and Distributed Systems, vol. 18, pp. 621-636, 2007.
[19] A. Legrand and C. Touati, “Non-Cooperative Scheduling of Multiple Bag-of-Task Applications,” Proc. IEEE INFOCOM '07, pp. 427-435, 2007.
[20] W.E. Leland, M.S. Taqqu, W. Willinger, and D.V. Wilson, “On the Self-Similar Nature of Ethernet Traffic (Extended Version),” IEEE/ACM Trans. Networking, vol. 2, pp. 1-15, 1994.
[21] H.-C. Lin and C.S. Raghavendra, “A Dynamic Load-Balancing Policy with a Central Job Dispatcher (LBC),” IEEE Trans. Software Eng., vol. 18, pp. 148-158, 1992.
[22] M.O. Lorenz, “Methods of Measuring the Concentration of Wealth,” Publications of the Am. Statistical Assoc., vol. 9, pp. 209-219, 1905.
[23] K. Lu, R. Subrata, and A.Y. Zomaya, “Towards Decentralized Load Balancing in a Computational Grid Environment,” Proc. First Int'l Conf. Grid and Pervasive Computing, pp. 466-477, 2006.
[24] D.G. Luenberger, Linear and Nonlinear Programming, second ed. Addison-Wesley, 1984.
[25] R. Mahajan, M. Rodrig, D. Wetherall, and J. Zahorjan, “Experiences Applying Game Theory to System Design,” Proc. ACM SIGCOMM '04, pp. 183-190, 2004.
[26] A. Muthoo, Bargaining Theory with Applications. Cambridge Univ. Press, 1999.
[27] J. Nash, “The Bargaining Problem,” Econometrica, vol. 18, pp. 155-162, 1950.
[28] I.A. Rai, G. Urvoy-Keller, and E.W. Biersack, “Analysis of LAS Scheduling for Job Size Distributions with High Variance,” Proc. ACM SIGMETRICS '03, pp. 218-228, 2003.
[29] K. Ranganathan, M. Ripeanu, A. Sarin, and I. Foster, “Incentive Mechanisms for Large Collaborative Resource Sharing,” Proc. IEEE Int'l Symp. Cluster Computing and the Grid, pp. 1-8, 2004.
[30] A. Riska, E. Smirni, and G. Ciardo, “Analytic Modeling of Load Balancing Policies for Tasks with Heavy-Tailed Distributions,” Proc. Second Int'l Workshop Software and Performance, pp. 147-157, 2000.
[31] T. Roughgarden and É. Tardos, “How Bad Is Selfish Routing?” J.ACM, vol. 49, pp. 236-259, 2002.
[32] N.G. Shivaratri, P. Krueger, and M. Singhal, “Load Distributing for Locally Distributed Systems,” Computer, pp. 33-44, 1992.
[33] A. Stefanescu and M.V. Stefanescu, “The Arbitrated Solution for Multi-Objective Convex Programming,” Revue Roumaine de Mathematiques Pures et Appliquées, vol. 29, pp. 593-598, 1984.
[34] R. Subrata, A.Y. Zomaya, and B. Landfeldt, “Artificial Life Techniques for Load Balancing in Computational Grids,” J.Computer and System Sciences, vol. 73, pp. 1176-1190, 2007.
[35] R. Subrata, A.Y. Zomaya, and B. Landfeldt, “Game Theoretic Approach for Load Balancing in Computational Grids,” IEEE Trans. Parallel and Distributed Systems, in press.
[36] C. Touati, E. Altman, and J. Galtier, “Generalized Nash Bargaining Solution for Bandwidth Allocation,” Computer Networks, vol. 50, pp. 3242-3263, 2006.
[37] R. Wolski, J.S. Plank, T. Bryan, and J. Brevik, “G-Commerce: Market Formulations Controlling Resource Allocation on the Computational Grid,” Proc. IEEE Int'l Parallel and Distributed Processing Symp., 2001.
[38] R. Wolski, N.T. Spring, and J. Hayes, “The Network Weather Service: A Distributed Resource Performance Forecasting Service for Metacomputing,” J. Future Generation Computer Systems, vol. 15, pp. 757-768, 1998.
[39] H. Yaiche, R.R. Mazumdar, and C. Rosenberg, “A Game Theoretic Framework for Bandwidth Allocation and Pricing in Broadband Networks,” IEEE/ACM Trans. Networking, vol. 8, pp. 667-678, 2000.
6 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool