The Community for Technology Leaders
RSS Icon
Issue No.06 - June (2013 vol.24)
pp: 1203-1212
Hamid Mohammadi Fard , University of Innsbruck, Innsbruck
Radu Prodan , University of Innsbruck, Innsbruck
Thomas Fahringer , University of Innsbruck, Innsbruck
The ultimate goal of cloud providers by providing resources is increasing their revenues. This goal leads to a selfish behavior that negatively affects the users of a commercial multicloud environment. In this paper, we introduce a pricing model and a truthful mechanism for scheduling single tasks considering two objectives: monetary cost and completion time. With respect to the social cost of the mechanism, i.e., minimizing the completion time and monetary cost, we extend the mechanism for dynamic scheduling of scientific workflows. We theoretically analyze the truthfulness and the efficiency of the mechanism and present extensive experimental results showing significant impact of the selfish behavior of the cloud providers on the efficiency of the whole system. The experiments conducted using real-world and synthetic workflow applications demonstrate that our solutions dominate in most cases the Pareto-optimal solutions estimated by two classical multiobjective evolutionary algorithms.
Dynamic scheduling, Games, Processor scheduling, Heuristic algorithms, Game theory, Optimization, truthful mechanism, Workflow scheduling, multicloud environment, game theory, reverse auction
Hamid Mohammadi Fard, Radu Prodan, Thomas Fahringer, "A Truthful Dynamic Workflow Scheduling Mechanism for Commercial Multicloud Environments", IEEE Transactions on Parallel & Distributed Systems, vol.24, no. 6, pp. 1203-1212, June 2013, doi:10.1109/TPDS.2012.257
[1] N. Nisan, T. Roughgarden, E. Tardos, and V. Vazirani, Algorithmic Game Theory. Cambridge Univ. Press, 2007.
[2] D. Fudenberg and J. Tirole, A Standard Text for Graduate Game Theory. MIT Press, 1991.
[3] N. Nisan and A. Ronen, "Algorithmic Mechanism Design," Proc. Symp. Theory of Computing (STOC), pp. 129-140, 1999.
[4] A. Archer and E. Tardos, "Truthful Mechanisms for One-Parameter Agents," Proc. 42nd IEEE Symp. Foundations of Computer Science, pp. 482-491, 2001.
[5] H.M. Fard, R. Prodan, G. Moser, and T. Fahringer, "A Bi-Criteria Truthful Mechanism for Scheduling of Workflows in Clouds," Proc. Third IEEE Int'l Conf. Cloud Computing Technology and Science (Cloudcom '11), pp. 599-605, 2011.
[6] K. Keahey, M. Tsugawa, A. Matsunaga, and J. Fortes, "Sky Computing," IEEE Internet Computing, vol. 13, no. 5, pp. 43-51, Sept./Oct. 2009.
[7] R. Buyya, R. Ranjan, and R.N. Calheiros, "InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services!" Proc. 10th Int'l Conf. Algorithms and Architectures for Parallel Processing (ICA3PP), 2010.
[8] S.K. Nair, S. Porwal, T. Dimitrakos, A.J. Ferrer, J. Tordsson, T. Sharif, C. Sheridan, M. Rajarajan, and A.U. Khan, "Towards Secure Cloud Bursting, Brokerage and Aggregation," Proc. Eighth IEEE European Conf. Web Services (ECOWS '10), pp. 189-196, 2010.
[9] I. Houidi, M. Mechtri, W. Louati, and D. Zeghlache, "Cloud Service Delivery across Multiple Cloud Platforms," Proc. IEEE Int'l Conf. Services Computing (SCC), pp. 741-742, July 2011.
[10] R. Buyya, D. Abramson, and S. Venugopal, "The Grid Economy," Proc. IEEE, vol. 93, no. 3, pp. 698-714, Mar. 2005.
[11] M.P. Wellman, W.E. Walsh, P.R. Wurman, and J.K. MacKie-Mason, "Auction Protocols for Decentralized Scheduling," Games & Economic Behavior, vol. 35, nos. 1/2, pp. 271-303, 2001.
[12] Y.-K. Kwok, K. Hwang, and S. Song, "Selfish grids: Gametheoretic Modeling and Nas/Psa Benchmark Evaluation," IEEE Trans. Parallel and Distributed Systems, vol. 18, no. 5, pp. 621-636, May 2007.
[13] D. Grosu and A.T. Chronopoulos, "Algorithmic Mechanism Design for Load Balancing in Distributed Systems," IEEE Trans. Systems, Man and Cybernetics, Part B, vol. 34, no. 1, pp. 77-84, Feb. 2004.
[14] A. Danak and S. Mannor, "Bidding Efficiently in Repeated Auctions with Entry and Observation Costs," Proc. IEEE Int'l Conf. Game Theory for Networks, pp. 299-307, May 2009.
[15] A. Danak and S. Mannor, "Efficient Bidding in Dynamic Grid Markets," IEEE Trans. Parallel and Distributed Systems, vol. 22, no. 9, pp. 1483-1496, Sept. 2011.
[16] N. Garg, D. Grosu, and V. Chaudhary, "Antisocial Behavior of Agents in Scheduling Mechanisms," IEEE Trans. System, Man, Cybernetics, Part A: Systems and Humans, vol. 37, no. 6, pp. 946-954, Nov. 2007.
[17] N. Andelman, Y. Azar, and M. Sorani, "Truthful Approximation Mechanisms for Scheduling Selfish Related Machines," Proc. 22nd Symp. Theoretical Aspects of Computer Science, 2005.
[18] H. Izakian, A. Abraham, and B.T. Ladani, "An Auction Method for Resource Allocation in Computational Grids," Future Generation Computer Systems, vol. 26, no. 2, pp. 228-235, Feb. 2010.
[19] K. Rzadca, D. Trystram, and A. Wierzbicki, "Fair Game-Theoretic Resource Management in Dedicated Grids," Proc. Int'l Symp. Cluster Computing and the Grid, pp. 343-350, 2007.
[20] R. Prodan, M. Wieczorek, and H.M. Fard, "Double Auction-Based Scheduling of Scientific Applications in Distributed Grid and Cloud Environments," J. Grid Computing, vol. 9, no. 4, pp. 531-548, 2011.
[21] N. Jain, I. Menache, J. Naor, and J. Yaniv, "A Truthful Mechanism for Value-Based Scheduling in Cloud Computing," Proc. Int'l Conf. Algorithmic Game Theory (SAGT), pp. 178-189, 2011.
[22] H. Topcuouglu, S. Hariri, and M.-y. Wu, "Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing," IEEE Trans. Parallel and Distributed Systems, vol. 13, no. 3, pp. 260-274, Mar. 2002.
[23] R.T. Marler and J.S. Arora, "Survey of Multi-Objective Optimization Methods in Engineering," Structural and Multidisciplinary Optimization, vol. 26, no. 6, pp. 369-395, Apr. 2004.
[24] R. Sakellariou, H. Zhao, E. Tsiakkouri, and M. Dikaiakos, "Scheduling Workflows with Budget Constraints," Proc. CoreGRID Workshop Integrated Research in GRID Computing, S. Gorlatch and M. Danelutto, eds., pp. 189-202, 2007.
[25] J. Yu, R. Buyya, and C.K. Tham, "Cost-Based Scheduling of Scientific Workflow Applications on Utility Grids," Proc. First Int'l Conf. e-Science and Grid Computing, pp. 140-147, 2005.
[26] J. Yu, M. Kirley, and R. Buyya, "Multi-Objective Planning for Workflow Execution on Grids," Proc. Eighth Int'l Conf. Grid Computing, pp. 10-17, 2007.
[27] H.M. Fard, R. Prodan, J.J.D. Barrionuevo, and T. Fahringer, "A Multi-Objective Approach for Workflow Scheduling in Heterogeneous Computing Environments," Proc. 12th IEEE/ACM Int'l Symp. Cluster, Cloud and Grid Computing (CCGrid '12), pp. 300-309, 2012.
[28] M.A. Salehi and R. Buyya, "Adapting Market-Oriented Scheduling Policies for Cloud Computing," Proc. 10th Int'l Conf. Algorithms and Architectures for Parallel Processing (ICA3PP), pp. 351-362, May 2010.
[29] M. Mao and M. Humphrey, "Auto-Scaling to Minimize Cost and Meet Application Deadlines in Cloud Workflows," Proc. Int'l Conf. High Performance Computing, Networking, Storage and Analysis (SC '11), 2011.
[30] M. Armbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, "Above the Clouds: A Berkeley View of Cloud Computing," Technical Report No. UCB/EECS-2009-28, Univ. of California at Berkley, USA, Feb. 2009.
[31] M. Wieczorek, A. Hoheisel, and R. Prodan, "Towards a General Model of the Multi-Criteria Workflow Scheduling on the Grid," Future Generation Computer Systems, vol. 25, no. 3, pp. 237-256, Mar. 2009.
[32] P. Blaha, K. Schwarz, G. Madsen, D. Kvasnicka, and J. Luitz, "WIEN2k: An Augmented Plane Wave plus Local Orbitals Program for Calculating Crystal Properties," Inst. of Physical and Theoretical Chemistry, TU Vienna, 2001.
[33] A. Sulistio, U. Cibej, S. Venugopal, B. Robic, and R. Buyya, "A Toolkit for Modelling and Simulating Data Grids: An Extension to GridSim," J. Concurrency and Computation: Practice and Experience, vol. 20, no. 13, pp. 1591-1609, Sept. 2008.
[34] T. Fahringer, R. Prodan, R. Duan, F. Nerieri, S. Podlipnig, J. Qin, M. Siddiqui, H.L. Truong, A. Villazon, and M. Wieczorek, "Askalon: A Grid Application Development and Computing Environment," Proc. sixth IEEE/ACM Int'l Conf. Grid Computing, pp. 122-131, 2005.
[35] E. Zitzler, M. Laumanns, and L. Thiele, "SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization" Proc. Int'l Conf. Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN '01), pp. 95-100, 2002.
[36] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II," IEEE Trans. Evolutionary Computation, vol. 6, no. 2, pp. 182-197, Apr. 2002.
[37] J.J. Durillo, A.J. Nebro, and E. Alba, "The jMetal Framework for Multi-Objective Optimization: Design and Architecture," Proc. IEEE Congress on Evolutionary Computation (CEC '10), pp. 4138-4325, July 2010.
[38] E. Zitzler and L. Thiele, "Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach," IEEE Trans. Evolutionary Computation, vol. 3, no. 4, pp. 257-271, Nov. 1999.
36 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool