Email this article 
Open Access
Subscription or Fee Access
A Framework for the Resource Allocation in Cloud Computing
Abstract
It is favorable that the goods, services or work are appropriate and that they are procured at the best possible cost to meet the needs of the purchaser in terms of quality, quantity and time. Here our project present a cloud resource procurement approach which not only automates the selection of an appropriate cloud vendor but also implements dynamic pricing. Our project discusses on three possible mechanisms cloud-dominant strategy incentive compatible (C-DSIC), cloud-Bayesian incentive compatible (C-BIC), and cloud optimal (C-OPT). C-DSIC achieves efficient allocation and individual rationality but it is not budget balanced. C-BIC achieves budget balancing and allocation efficiency but not individual rationality. C-OPT mechanism is to address the limitations of both the C-DSIC and C-BIC mechanisms. In C-OPT, the cloud vendor can neither overbid nor underbid. If the cloud vendor overbid then incentive is not paid. On the other hand, if it is underbids, then it will not be the winner.
Keywords
References
Y. Yang, Y. Zhou, L. Liang, D. He, and Z. Sun, “A Sevice-Oriented Broker for Bulk Data Transfer in Cloud Computing,” Proc. Ninth Int’l Conf. Grid and Cooperative Computing (GCC), pp. 264-269, Nov. 2010.
M. Mihailescu and Y.M. Teo, “Dynamic Resource Pricing on Federated Clouds,” Proc. IEEE/ACM 10th Int’l Conf. Cluster, Cloud and Grid Computing (CCGRID ’10), pp. 513-517, 2010
Z.-J. Li, X.-D. Liu, X.-D. Duan, and C.-R. Wang, “Optimal Solution for Grid Resource Allocation Using Particle Swarm Optimiza- tion,” Proc. Third Int’l Conf. Multimedia and Ubiquitous Eng. (MUE ’09), pp. 339-346, June. 2009.
F. Li and D. Qi, “Research on Grid Resource Allocation Algorithm Based on Fuzzy Clustering,” Proc. Second Int’l Conf. Future Generation Comm. and Networking (FGCN ’08), vol. 2, pp. 162-166, Dec. 2008.
W.-Y. Lin, G.-Y. Lin, and H.-Y. Wei, “Dynamic Auction Mechan- ism for Cloud Resource Allocation,” Proc. IEEE/ACM 10th Int’l Conf. Cluster, Cloud and Grid Computing (CCGRID ’10), pp. 591-592, 2010.
M. Bichler, J. Kalagnanam, K. Katircioglu, A.J. King, R.D. Lawrence, H.S. Lee, G.Y. Lin, and Y. Lu, “Applications of Flexible Pricing in Business-to-Business Electronic Commerce,” IBM Systems J., vol. 41, no. 2, pp. 287-302, 2002.
S.J. Chapin, D. Katramatos, J.F. Karpovich, and A.S. Grimshaw, “The Legion Resource Management System,” Proc. Job Scheduling Strategies for Parallel Processing, pp. 162-178, 1999.
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, vol. 14, nos. 13-15, pp. 1507-1542, 2002.
P. Ghosh, N. Roy, S.K. Das, and K. Basu, “A Pricing Strategy for Job Allocation in Mobile Grids Using a Non-Cooperative Bargaining Theory Framework,” J. Parallel and Distributed Comput- ing, vol. 65, no. 11, pp. 1366-1383, 2005.
S. Penmatsa and A. Chronopoulos, “Price-Based User-Optimal Job Allocation Scheme for Grid Systems,” Proc. Int’l Symp. Parallel and Distributed Processing, p. 396, Apr. 2006.
N. Nisan, T. Roughgarden, E. Tardos, and V.V. Vazirani, Algorithmic Game Theory. Cambridge Univ. Press, 2007.
C. d’Aspremont and L.-A. Grard-Varet, “Incentives and Incom- plete Information,” J. Public Economics, vol. 11, no. 1, pp. 25-45, 1979.
R. Gautam, N. Hemachandra, Y. Narahari, H. Prakash, D. Kulkarni, and J. Tew, “Optimal Auctions for Multi-Unit Procure- ment with Volume Discount Bids,” Int’l J. Operational Research, vol. 6, no. 1, pp. 70-91, 2009.
B. Katzman, J. Reif, and J.A. Schwartz, “The Relation between Variance and Information Rent in Auctions,” Int’l J. Industrial Organization, vol. 28, no. 2, pp. 127-130, 2010.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.