• DocumentCode
    172824
  • Title

    Core-Selecting Auctions for Dynamically Allocating Heterogeneous VMs in Cloud Computing

  • Author

    Haoming Fu ; Zongpeng Li ; Chuan Wu ; Xiaowen Chu

  • fYear
    2014
  • fDate
    June 27 2014-July 2 2014
  • Firstpage
    152
  • Lastpage
    159
  • Abstract
    In a cloud market, the cloud provider provisions heterogeneous virtual machine (VM) instances from its resource pool, for allocation to cloud users. Auction-based allocations are efficient in assigning VMs to users who value them the most. Existing auction design often overlooks the heterogeneity of VMs, and does not consider dynamic, demand-driven VM provisioning. Moreover, the classic VCG auction leads to unsatisfactory seller revenues and vulnerability to a strategic bidding behavior known as shill bidding. This work presents a new type of core-selecting VM auctions, which are combinatorial auctions that always select bidder charges from the core of the price vector space, with guaranteed economic efficiency under truthful bidding. These auctions represent a comprehensive three-phase mechanism that instructs the cloud provider to judiciously assemble, allocate, and price VM bundles. They are proof against shills, can improve seller revenue over existing auction mechanisms, and can be tailored to maximize truthfulness.
  • Keywords
    cloud computing; pricing; resource allocation; virtual machines; VCG auction; auction-based allocations; cloud computing; combinatorial auctions; core-selecting VM auctions; core-selecting auctions; economic efficiency; heterogeneous VM allocation; price vector space; resource pool; shill bidding; strategic bidding behavior; Cost accounting; Dynamic scheduling; Economics; Educational institutions; Resource management; Vectors; Virtual machining; Auction; Cloud Computing; Virtual Machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cloud Computing (CLOUD), 2014 IEEE 7th International Conference on
  • Conference_Location
    Anchorage, AK
  • Print_ISBN
    978-1-4799-5062-1
  • Type

    conf

  • DOI
    10.1109/CLOUD.2014.30
  • Filename
    6973736