• DocumentCode
    3600004
  • Title

    Strategy-Proof Auction Mechanism with Group Price for Virtual Machine Allocation in Clouds

  • Author

    Yonglong Zhang ; Bin Li ; Zhiqiu Huang ; Jin Wang ; Junwu Zhu ; Huanfeng Peng

  • Author_Institution
    Sch. of Comput. Sci. & Technol., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China
  • fYear
    2014
  • Firstpage
    60
  • Lastpage
    68
  • Abstract
    Market mechanism constitutes an efficient scheme for the allocation of cloud-based computing resources with the view of virtual machines. However, most of the existing mechanisms commonly use fixed price model and ignore flexible price model for the cloud providers. In this paper, we formulate the problem of virtual machine allocation in clouds as a combinatorial auction problem and propose a mechanism with group price to solve it, in which the cloud provider can express the discount price for each kind of traded virtual machine instances. We investigate the theoretical properties of the proposed mechanism including individual rationality, ex-post budget balance, and truthfulness. Extensive simulation results show that the proposed mechanism yields the allocation efficiency and computational tractability while generating higher revenue for the cloud providers than the mechanism with fixed price.
  • Keywords
    cloud computing; pricing; resource allocation; virtual machines; cloud-based computing resources; combinatorial auction problem; discount price; ex-post budget balance; group price; individual rationality; revenue; strategy-proof auction mechanism; truthfulness; virtual machine allocation; Computational complexity; Computational modeling; Cost accounting; Electronic mail; Mechanical factors; Resource management; Virtual machining; Cloud Computing; Greedy Heuristic; Group Price; Strategy-proof Mechanism; Virtual Machine Allocation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Cloud and Big Data (CBD), 2014 Second International Conference on
  • Print_ISBN
    978-1-4799-8086-4
  • Type

    conf

  • DOI
    10.1109/CBD.2014.17
  • Filename
    7176073