Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article

Equilibrium in cloud computing market

Published: 01 October 2015 Publication History

Abstract

The emerging market of public and private cloud infrastructure benefits the end users, as it reduces their costs, introduces new efficient services and provides variety of products. End-users primary deal with private clouds or brokers, which serve all the users needs, and if needed, buy extra capacity from public cloud. In this paper we study such a three-level market structure. Our goal is to find Nash equilibrium of prices in this market as well as market influence on the end users. We observe that new resources at public clouds positively affect the market from the end-user perspective. Additionally our observation indicates that the switching cost plays an important role in achieving the optimal point in average market price value, and thus reduction of the switching costs will benefit end-users even more. Thus the results imply that standardization of the interfaces and interoperability between various clouds increases market efficiency.

References

[1]
P. Wright, Y.L. Sun, T. Harmer, A. Keenan, A. Stewart, R. Perrott, A constraints-based resource discovery model for multi-provider cloud environments, J. Cloud Comput. Adv. Syst. Appl., 1 (2012) 1-6.
[2]
E. Brynjolfsson, P. Hofmann, J. Jordan, Cloud computing and electricity: beyond the utility model, Commun. ACM, 53 (2010) 32-34.
[3]
G. Garrison, S.H. Kim, R.L. Wakefield, Success factors for deploying cloud computing, Commun. ACM, 55 (2012) 62-68.
[4]
Y. Raivio, O. Mazhelis, K. Annapureddy, R. Mallavarapu, P. Tyrväinen, Hybrid cloud architecture for short message services, in: Proceedings of the 2nd International Conference on Cloud Computing and Services Science, SciTePress, 2012, pp. 489-500.
[5]
A. Ali-Eldin, M. Kihl, J. Tordsson, E. Elmroth, Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control, in: Proceedings of the 3rd Workshop on Scientific Cloud Computing Date, ACM, New York, NY, USA, 2012, pp. 31-40.
[6]
F. Chang, J. Ren, R. Viswanathan, Optimal resource allocation in clouds, in: Proceedings of the 3rd International Conference on Cloud Computing, IEEE Computer Society, Washington, DC, USA, 2010, pp. 418-425.
[7]
H. Kllapi, E. Sitaridi, M.M. Tsangaris, Y. Ioannidis, Schedule optimization for data processing flows on the cloud, in: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, ACM, New York, NY, USA, 2011, pp. 289-300.
[8]
S. Chaisiri, B.-S. Lee, D. Niyato, Optimization of resource provisioning cost in cloud computing, IEEE Trans. Serv. Comput., 5 (2012) 164-177.
[9]
J. Hamilton, Cloud computing economies of scale, keynote at AWS Genomics & Cloud Computing Workshop, Seattle, WA, 08.06.2010, 2010. Available from http://www.mvdirona.com/jrh/TalksAndPapers/JamesHamilton_GenomicsCloud20100608.pdf.
[10]
J. Weinman, Mathematical proof of the inevitability of cloud computing, online report, 08.01.2011, 2011. Available from http://www.joeweinman.com/Resources/JoeWeinmanInevitabilityOfCloud.pdf.
[11]
R. Buyya, S. Pandey, C. Vecchiola, Market-oriented cloud computing and the cloudbus toolkit, CoRR abs/1203.5196.
[12]
C. Vecchiola, R.N. Calheiros, D. Karunamoorthy, R. Buyya, Deadline-driven provisioning of resources for scientific applications in hybrid clouds with aneka, Future Gener. Comput. Syst., 28 (2012) 58-65.
[13]
M. Mazzucco, D. Dyachuk, R. Deters, Maximizing cloud providers' revenues via energy aware allocation policies, in: Proceedings of the 3rd International Conference on Cloud Computing, IEEE Computer Society, Washington, DC, USA, 2010, pp. 131-138.
[14]
G. Raj, An efficient broker cloud management system, in: Proceedings of the International Conference on Advances in Computing and Artificial Intelligence, ACM, New York, NY, USA, 2011, pp. 72-76.
[15]
O. Rogers, D. Cliff, A financial brokerage model for cloud computing, J. Cloud Comput., 1 (2012) 1-12.
[16]
D. Fudenberg, J. Tirole, Game theory. 1991, 1991.
[17]
D. Ardagna, B. Panicucci, M. Passacantando, A game theoretic formulation of the service provisioning problem in cloud systems, in: Proceedings of the 20th International Conference on World Wide Web, ACM, New York, NY, USA, 2011, pp. 177-186.
[18]
G. Wei, A.V. Vasilakos, Y. Zheng, N. Xiong, A game-theoretic method of fair resource allocation for cloud computing services, J. Supercomput., 54 (2010) 252-269.
[19]
N. Rao, S. Poole, F. He, J. Zhuang, C. Ma, D. Yau, Cloud computing infrastructure robustness: A game theory approach, in: 2012 International Conference on Computing, Networking and Communications, ICNC, 2012, pp. 34-38.
[20]
R. Pal, P. Hui, On the economics of cloud markets, CoRR abs/1103.0045.
[21]
Y. Ge, Y. Zhang, Q. Qiu, Y.-H. Lu, A game theoretic resource allocation for overall energy minimization in mobile cloud computing system, in: Proceedings of the 2012 ACM/IEEE International Symposium on Low Power Electronics and Design, ACM, New York, NY, USA, 2012, pp. 279-284.
[22]
F. Teng, F. Magoules, A new game theoretical resource allocation algorithm for cloud computing, in: Lecture Notes in Computer Science, vol. 6104, Springer, Berlin Heidelberg, 2010, pp. 321-330.
[23]
C. Lee, J. Suzuki, A. Vasilakos, Y. Yamamoto, K. Oba, An evolutionary game theoretic approach to adaptive and stable application deployment in clouds, in: Proceedings of the 2nd Workshop on Bio-inspired Algorithms for Distributed Systems, ACM, New York, NY, USA, 2010, pp. 29-38.
[24]
D. McFadden, Conditional logit analysis of qualitative choice behavior, Front. Econom. (1973) 105-142.
[25]
D. Monderer, L. Shapley, Potential games, Games Econom. Behav., 14 (1996) 124-143.

Cited By

View all
  • (2018)A Game-Theoretic Model of Virtual Operators Competition in a Two-Sided Telecommunication MarketAutomation and Remote Control10.1134/S000511791804014879:4(737-756)Online publication date: 1-Apr-2018

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Performance Evaluation
Performance Evaluation  Volume 92, Issue C
October 2015
72 pages

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 October 2015

Author Tags

  1. Cloud computing
  2. Game-theory
  3. Market model
  4. Private cloud
  5. Public cloud

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 03 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2018)A Game-Theoretic Model of Virtual Operators Competition in a Two-Sided Telecommunication MarketAutomation and Remote Control10.1134/S000511791804014879:4(737-756)Online publication date: 1-Apr-2018

View Options

View options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media