An online auction framework for dynamic resource provisioning in cloud computing

Performance Evaluation Review

Volumn 42, Issue 1, 2014, Pages 71-83

  Shi, Weijie ^a   Zhang, Linquan ^b   Wu, Chuan ^a   Li, Zongpeng ^b   Lau, Francis C M ^a  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b UNIVERSITY OF CALGARY   (Canada)

Author keywords

Cloud computing; Combinatorial auction; Online algorithms; Pricing; Resource allocation; Truthful mechanisms

Indexed keywords

ALGORITHMS; APPROXIMATION ALGORITHMS; CLOUD COMPUTING; COSTS; DYNAMICS; ECONOMICS; ELECTRONIC COMMERCE; OPTIMIZATION; RESOURCE ALLOCATION;

COMBINATORIAL AUCTION; COMPUTATIONALLY EFFICIENT; DUAL-FITTING ALGORITHM; DYNAMIC RESOURCE PROVISIONING; ON-LINE ALGORITHMS; PRICING AND RESOURCE ALLOCATION; TRACE DRIVEN SIMULATION; TRUTHFUL MECHANISMS;

DISTRIBUTED COMPUTER SYSTEMS;

EID: 84955561154     PISSN: 01635999     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2637364.2591980     Document Type: Conference Paper

References (30)

1. Amazon Elastic Compute Cloud. http://aws.amazon. com/ec2/.
2. Amazon EC2 Spot Instances. http://aws.amazon. com/ec2/spot-instances/.
3. Google Cluster Data, https://code.google.com/p/googleclusterdata/.
4. Windows Azure: Microsoft's Cloud Platform. http://www.windowsazure.com/.
5. H. Ballani, P. Costa, T. Karagiannis, and A. Rowstron. Towards predictable datacenter networks. In ACM SIGCOMM Computer Communication Review, volume 41, pages 242-253. ACM, 2011.
6. N. Bansal, K.-W. Lee, V. Nagarajan, and M. Zafer. Minimum congestion mapping in a cloud. In Proceedings of the 30th annual ACM SIGACT-SIGOPS symposium on Principles of distributed computing, pages 267-276. ACM, 2011.
7. N. Buchbinder, K. Jain, and J. S. Naor. Online primal-dual algorithms for maximizing ad-auctions revenue. In Proceedings of the 15th Annual European Symposium on Algorithms, 2007.
8. N. Cherfi and M. Hifi. A column generation method for the multiple-choice multi-dimensional knapsack problem. Computational Optimization and Applications, 46(1):51-73, 2010.
9. H. Fu, Z. Li, and C. Wu. Core-selecting auction design for dynamically allocating heterogeneous vms in cloud computing. Submitted to INFOCOM 2014.
10. G. Gens and E. Levner. Complexity of approximation algorithms for combinatorial problems: a survey. ACM SIGACT News, 12(3):52-65, 1980.
11. P. Godfrey, M. Schapira, A. Zohar, and S. Shenker. Incentive compatibility and dynamics of congestion control. In ACM SIGMETRICS Performance Evaluation Review, pages 95-106, 2010.
12. R. Lavi and N. Nisan. Competitive analysis of incentive compatible on-line auctions. In Proceedings of the 2nd ACM Conference on Electronic Commerce, pages 233-241. ACM, 2000.
13. R. Lavi and C. Swamy. Truthful and near-optimal mechanism design via linear programming. In Proc. of FOCS, pages 595-604, 2005.
14. D. Lehmann, L. I. Oćallaghan, and Y. Shoham. Truth revelation in approximately efficient combinatorial auctions. Journal of the ACM (JACM), 49, 2002.
15. R. T. B. Ma, S. C. M. Lee, J. C. S. Lui, and D. K. Y. Yau. A game theoretic approach to provide incentive and service differentiation in p2p networks. In ACM SIGMETRICS Performance Evaluation Review, pages 189-198, 2004.
16. X. Meng, V. Pappas, and L. Zhang. Improving the scalability of data center networks with traffic-aware virtual machine placement. In INFOCOM, 2010 Proceedings IEEE, pages 1-9. IEEE, 2010.
17. A. Mu'Alem and N. Nisan. Truthful approximation mechanisms for restricted combinatorial auctions. Games and Economic Behavior, 64(2):612-631, 2008.
18. N. Nisan. Algorithmic game theory. Cambridge University Press, 2007.
19. D. Niu, C. Feng, and B. Li. Pricing cloud bandwidth reservations under demand uncertainty. In ACM SIGMETRICS Performance Evaluation Review, pages 151-162, 2012.
20. M. H. Rothkopf, A. Pekeč, and R. M. Harstad. Computationally manageable combinational auctions. Management science, 44(8):1131-1147, 1998.
21. G. Shanmuganathan, A. Gulati, and P. Varman. Defragmenting the cloud using demand-based resource allocation. In ACM SIGMETRICS Performance Evaluation Review, pages 67-80, 2013.
22. W. Shi, L. Zhang, C. Wu, Z. Li, and F. C. M. Lau. An online auction framework for dynamic resource provisioning in cloud computing. Technical report, 2014. http://i.cs.hku.hk/~cwu/papers/sigmetrics14-techreport.pdf.
23. W. Vickrey. Counterspeculation, auctions, and competitive sealed tenders. The Journal of Finance, 16(1):8-37, 1961.
24. Q. Wang, K. Ren, and X. Meng. When cloud meets ebay: Towards effective pricing for cloud computing. In Proc. of IEEE INFOCOM, pages 936-944, 2012.
25. W. Wang, B. Liang, and B. Li. Revenue maximization with dynamic auctions in iaas cloud markets. In Proc. IEEE ICDCS, 2013.
26. C. Wu, Z. Li, X. Qiu, and F. C. M. Lau. Auction-based p2p vod streaming: Incentives and optimal scheduling. ACM Transactions on Multimedia Computing, Communications and Applications, 2012.
27. S. Zaman and D. Grosu. Combinatorial auction-based mechanisms for vm provisioning and allocation in clouds. In IEEE/ACM CCGrid, pages 729-734, 2012.
28. H. Zhang, B. Li, H. Jiang, F. Liu, A. V. Vasilakos, and J. Liu. A framework for truthful online auctions in cloud computing with heterogeneous user demands. In Proc. of IEEE INFOCOM, 2013.
29. L. Zhang, Z. Li, and C. Wu. Dynamic resource provisioning in cloud computing: A randomized auction approach. In Proc. of IEEE INFOCOM, 2014.
30. Y. Zhu, B. Li, and Z. Li. Truthful spectrum auction design for secondary networks. In Proc. of IEEE INFOCOM, 2012.