IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0138424.html
   My bibliography  Save this article

Efficient Nash Equilibrium Resource Allocation Based on Game Theory Mechanism in Cloud Computing by Using Auction

Author

Listed:
  • Amin Nezarat
  • GH Dastghaibifard

Abstract

One of the most complex issues in the cloud computing environment is the problem of resource allocation so that, on one hand, the cloud provider expects the most profitability and, on the other hand, users also expect to have the best resources at their disposal considering the budget constraints and time. In most previous work conducted, heuristic and evolutionary approaches have been used to solve this problem. Nevertheless, since the nature of this environment is based on economic methods, using such methods can decrease response time and reducing the complexity of the problem. In this paper, an auction-based method is proposed which determines the auction winner by applying game theory mechanism and holding a repetitive game with incomplete information in a non-cooperative environment. In this method, users calculate suitable price bid with their objective function during several round and repetitions and send it to the auctioneer; and the auctioneer chooses the winning player based the suggested utility function. In the proposed method, the end point of the game is the Nash equilibrium point where players are no longer inclined to alter their bid for that resource and the final bid also satisfies the auctioneer’s utility function. To prove the response space convexity, the Lagrange method is used and the proposed model is simulated in the cloudsim and the results are compared with previous work. At the end, it is concluded that this method converges to a response in a shorter time, provides the lowest service level agreement violations and the most utility to the provider.

Suggested Citation

  • Amin Nezarat & GH Dastghaibifard, 2015. "Efficient Nash Equilibrium Resource Allocation Based on Game Theory Mechanism in Cloud Computing by Using Auction," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-29, October.
  • Handle: RePEc:plo:pone00:0138424
    DOI: 10.1371/journal.pone.0138424
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0138424
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0138424&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0138424?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Rajiv T. Maheswaran & Tamer Başar, 2003. "Nash Equilibrium and Decentralized Negotiation in Auctioning Divisible Resources," Group Decision and Negotiation, Springer, vol. 12(5), pages 361-395, September.
    2. Shuai Ding & Chen-Yi Xia & Kai-Le Zhou & Shan-Lin Yang & Jennifer S Shang, 2014. "Decision Support for Personalized Cloud Service Selection through Multi-Attribute Trustworthiness Evaluation," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-11, June.
    3. Ding, Shuai & Wang, Juan & Ruan, Sumei & Xia, Chengyi, 2015. "Inferring to individual diversity promotes the cooperation in the spatial prisoner’s dilemma game," Chaos, Solitons & Fractals, Elsevier, vol. 71(C), pages 91-99.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Masih Fadaki & Babak Abbasi & Prem Chhetri, 2022. "Quantum game approach for capacity allocation decisions under strategic reasoning," Computational Management Science, Springer, vol. 19(3), pages 491-512, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. N. A. Korgin & V. O. Korepanov, 2017. "Experimental Gaming Comparison of Resource Allocation Rules in Case of Transferable Utilities," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 19(02), pages 1-11, June.
    2. Rense Corten & Stephanie Rosenkranz & Vincent Buskens & Karen S Cook, 2016. "Reputation Effects in Social Networks Do Not Promote Cooperation: An Experimental Test of the Raub & Weesie Model," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-17, July.
    3. Ramesh Johari & John N. Tsitsiklis, 2004. "Efficiency Loss in a Network Resource Allocation Game," Mathematics of Operations Research, INFORMS, vol. 29(3), pages 407-435, August.
    4. Yuchen Pan & Shuai Ding & Wenjuan Fan & Jing Li & Shanlin Yang, 2015. "Trust-Enhanced Cloud Service Selection Model Based on QoS Analysis," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-19, November.
    5. Chang, Shuhua & Zhang, Zhipeng & Wu, Yu’e & Xie, Yunya, 2018. "Cooperation is enhanced by inhomogeneous inertia in spatial prisoner’s dilemma game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 419-425.
    6. Zhou, Tianwei & Ding, Shuai & Fan, Wenjuan & Wang, Hao, 2016. "An improved public goods game model with reputation effect on the spatial lattices," Chaos, Solitons & Fractals, Elsevier, vol. 93(C), pages 130-135.
    7. Deng, Zheng-Hong & Huang, Yi-Jie & Gu, Zhi-Yang & Li-Gao,, 2018. "Multigames with social punishment and the evolution of cooperation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 164-170.
    8. Antonio Fernández Anta & Chryssis Georgiou & Miguel A Mosteiro & Daniel Pareja, 2015. "Algorithmic Mechanisms for Reliable Crowdsourcing Computation under Collusion," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-22, March.
    9. Ioannis Caragiannis & Alexandros A. Voudouris, 2021. "The Efficiency of Resource Allocation Mechanisms for Budget-Constrained Users," Mathematics of Operations Research, INFORMS, vol. 46(2), pages 503-523, May.
    10. Geng, Yini & Shen, Chen & Hu, Kaipeng & Shi, Lei, 2018. "Impact of punishment on the evolution of cooperation in spatial prisoner’s dilemma game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 540-545.
    11. Sun, Xuemei & Zhang, Yiming & Ren, Xu & Chen, Ke, 2015. "Optimization deployment of wireless sensor networks based on culture–ant colony algorithm," Applied Mathematics and Computation, Elsevier, vol. 250(C), pages 58-70.
    12. Agryzkov, Taras & Tortosa, Leandro & Vicent, Jose F., 2018. "An algorithm to compute data diversity index in spatial networks," Applied Mathematics and Computation, Elsevier, vol. 337(C), pages 63-75.
    13. Shu, Gang & Du, Xia & Li, Ya, 2016. "Surrounding information consideration promotes cooperation in Prisoner’s dilemma game," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 689-694.
    14. Lang, Rongling & Li, Tao & Mo, Desen & Shi, Yongtang, 2016. "A novel method for analyzing inverse problem of topological indices of graphs using competitive agglomeration," Applied Mathematics and Computation, Elsevier, vol. 291(C), pages 115-121.
    15. Daron Acemoglu & Asuman Ozdaglar, 2007. "Competition and Efficiency in Congested Markets," Mathematics of Operations Research, INFORMS, vol. 32(1), pages 1-31, February.
    16. Muhammad Imran & Helmut Hlavacs & Inam Ul Haq & Bilal Jan & Fakhri Alam Khan & Awais Ahmad, 2017. "Provenance based data integrity checking and verification in cloud environments," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-19, May.
    17. Yanfeng Shi & Jiqiang Liu & Zhen Han & Qingji Zheng & Rui Zhang & Shuo Qiu, 2014. "Attribute-Based Proxy Re-Encryption with Keyword Search," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-24, December.
    18. Bruno Guazzelli Batista & Julio Cezar Estrella & Carlos Henrique Gomes Ferreira & Dionisio Machado Leite Filho & Luis Hideo Vasconcelos Nakamura & Stephan Reiff-Marganiec & Marcos José Santana & Regin, 2015. "Performance Evaluation of Resource Management in Cloud Computing Environments," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-21, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0138424. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.