Resource-provision scheduling in cloud datacenter

Abstract. Cloud computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way in which hardware is designed and purchased. We review the new cloud computing technologies, and indicate the main challenges for their development in future, among which resource management problem stands out and attracts our attention. Combining the current scheduling theories, we propose cloud scheduling hierarchy to deal with different requirements of cloud services. we settle the evaluation problem for on-line schedulability tests in cloud computing. We propose a concept of test reliability to express the probability that a random task set could pass a given schedulability test. The larger the probability is, the more reliable the test is. From the aspect of system, a test with high reliability can guarantee high system utilization. From the practical aspect, we develop a simulator to model MapReduce framework. This simulator offers a simulated environment directly used by MapReduce theoretical researchers. The users of SimMapReduce only concentrate on specific research issues without getting concerned about finer implementation details for diverse service models, so that they can accelerate study progress of new cloud technologies.

Keywords:

Cloud computing, Game theory Normal formulation, Types of games,

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Foster, C. Kesselman, and S. Tuecke. The anatomy of the grid - enabling scalable virtual organizations. International Journal of Supercomputer Applications, 15:2001, 2001.
D. D. Roure, M. A. Baker, N. R. Jennings, and N. R. Shadbolt. The evolution of the grid. In Proceedings of Grid Computing: Making the Global Infrastructure a Reality, pages 65–100. John Wiley & Sons, 2004.
M. Parashar and S. Hariri. Autonomic computing: An overview. In Proceedings of Unconventional Programming Paradigms, pages 247–259. Springer Verlag, 2005.
J. O. Kephart and D. M. Chess. The vision of autonomic computing. Computer, 36:41–50, January 2003.
M. Cafaro and G. Aloisio. Grids, Clouds and Virtualization. Springer-Verlag New York, Inc., 1st edition, 2010.
M. Armbrust, A. Fox, and R. Griffith. Above the clouds: A berkeley view of cloud computing. Technical Report UCB/EECS-2009-28, EECS Department, University of California, Berkeley, Feb 2009.
R. Buyya, D. Abramson, J. Giddy, and H. Stockinger. Economic models for resource management and scheduling in grid computing. Concurrency and Computation: Practice and Experience, 14:1507–1542, 2002.
Galstyan, S. Kolar, and K. Lerman. Resource allocation games with changing resource capacities. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems, pages 145–152. ACM Press, 2003.
J. Bredin, D. Kotz, D. Rus, R. T. Maheswaran, C. Imer, and T. Basar. Computational markets to regulate mobile-agent systems. Autonomous Agents and Multi-Agent Systems, 6:235– 263, 2003.
R. T. Maheswaran and T. Basar. Nash equilibrium and decentralized negotiation in auctioning divisible resources. Group Decision and Negotiation, 12:361–395, 2003.
S. Khan and I. Ahmad. Non-cooperative, semi-cooperative, and cooperative games-based grid resource allocation. Parallel and Distributed Processing Symposium, 0:101, 2006.
B. An, C. Miao, and Z. Shen. Market based resource allocation with incomplete information. In Proceedings of the 20th International Joint Conference on Artifical Intelligence, pages 1193– 1198. Morgan Kaufmann Publishers Inc., 2007.
G. Wei, A. Vasilakos, Y. Zheng, and N. Xiong. A game-theoretic method of fair resource allocation forcloud computing services. The Journal of Supercomputing, 54:1–18, 2009.
Q. Fan, Q. Wu, F. Magoul`es, N. Xiong, A. V. Vasilakos, and Y. He. Game and balance multicast architecture algorithms for sensor grid. Sensors, 9(9):7177–7202, 2009.
F. Teng and F. Magoul`es. A new game theoretical ressource allocation algorithm for cloud computing.In Proceedings of Advances in Grid and Pervasive Computing, volume 6104, pages 321–330. Springer, 2010.
R. Gibbons. A Primer in Game Theory. Pearson Higher Education, 1992.
T. W. Sandholm. Distributed rational decision making. Multiagent systems: a modern approach to distributed artificial intelligence, 37:201–258, 1999.
M. A. Gibney, N. R. Jennings, N. J. Vriend, and J.-M. Griffiths. Market-based call routing in telecommunications networks using adaptive pricing and real bidding. In Proceedings of the Third International Workshop on Intelligent Agents for Telecommunication Applications, pages 46–61. Springer-Verlag, 1999.
L. Joita, O. F. Rana, F. Freitag, I. Chao, P. Chacin, L. Navarro, and O. Ardaiz. A catallactic market for data mining services. Future Generation Computer Systems, 23(1):146–153, 2007.
B. N. Chun and D. E. Culler. User-centric performance analysis of market-based cluster batch schedulers. In Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid, page 30, Washington, DC, USA, 2002. IEEE Computer Society.
T.-M.-H. Nguyen and F. Magoul`es. Autonomic data management system in grid environment. Journal of Algorithms & Computational Technology, 3:155–177, 2009.
G. Stuer, K. Vanmechelen, and J. Broeckhove. A commodity market algorithm for pricing substitutable grid resources. Future Generation Computer Systems., 23(5):688–701, 2007.
N. Stratford and R. Mortier. An economic approach to adaptive resource management. In Proceedingsof the The Seventh Workshop on Hot Topics in Operating Systems, pages 142 - 147. IEEE Computer Society, 1999.
C. Ozturan. Resource bartering in data grids. Science of Computer Programming, 12(3):155– 168, 2004.
T. M. Lynar, R. D. Herbert, and S. Simon. Auction resource allocation mechanisms in grids of heterogeneous computers. WSEAS Transactions on Computers, 8(10):1671–1680, 2009.
R. Buyya, D. Abramson, J. Giddy, and H. Stockinger. Economic models for resource management and scheduling in grid computing. Concurrency and Computation: Practice and Experience, 14:1507–1542, 2002.
Y. kwong Kwok, S. Song, and K. Hwang. Selfish grid computing: Game-theoretic modeling and nash performance results. In Proceedings of International Symposium on Cluster Computing and the Grid, pages 9–12, 2005.
S. Yi, D. Kondo, and A. Andrzejak. Reducing costs of spot instances via checkpointing in the amazon elastic compute cloud. In Proceedings of IEEE International Conference on Cloud Computing, pages 236–243, 2010.
R. Buyya, R. Ranjan, and R. N. Calheiros. Modeling and simulation of scalable cloud computing environments and the cloudsim toolkit: Challenges and opportunities. In Proceedings of the 7thHigh Performance Computing and Simulation Conference. IEEE Computer Society, 2009.
M. Armbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, M. Zaharia, "A View of Cloud Computing", Communication of the ACM Magazine, New York, USA, pp. 50–58, 2010.
A.Kaleeswaran, V. Ramasamy, P. Vivekanandan, Dynamic scheduling of data using genetic algorithm in cloud computing, international journal of advances in engineering & technology, 2013.
M. Armbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, M. Zaharia, "A View of Cloud Computing", Communication of the ACM Magazine, New York, USA, pp. 50–58, 2010.