• Login
    View Item 
    •   Shocker Open Access Repository Home
    • Engineering
    • Electrical Engineering and Computer Science
    • EECS Faculty Scholarship
    • EECS Research Publications
    • View Item
    •   Shocker Open Access Repository Home
    • Engineering
    • Electrical Engineering and Computer Science
    • EECS Faculty Scholarship
    • EECS Research Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Optimal auction for delay and energy constrained task offloading in mobile edge computing

    Date
    2020-12-24
    Author
    Mashhadi, Farshad
    Salinas Monroy, Sergio A.
    Bozorgchenani, Arash
    Tarchi, Daniele
    Metadata
    Show full item record
    Citation
    Mashhadi, Farshad; Salinas Monroy, Sergio A.; Bozorgchenani, Arash; Tarchi, Daniele. 2020. Optimal auction for delay and energy constrained task offloading in mobile edge computing. Computer Networks, vol. 183:art no. 107527
    Abstract
    Mobile edge computing has emerged as a promising paradigm to complement the computing and energy resources of mobile devices. In this computing paradigm, mobile devices offload their computing tasks to nearby edge servers, which can potentially reduce their energy consumption and task completion delay. In exchange for processing the computing tasks, edge servers expect to receive a payment that covers their operating costs and allows them to make a profit. Unfortunately, existing works either ignore the payments to the edge servers, or ignore the task processing delay and energy consumption of the mobile devices. To bridge this gap, we propose an auction to allocate edge servers to mobile devices that is executed by a pair of deep neural networks. Our proposed auction maximizes the profit of the edge servers, and satisfies the task processing delay and energy consumption constraints of the mobile devices. The proposed deep neural networks also guarantee that the mobile devices are unable to unfairly affect the results of the auctions. Our extensive simulations show that our proposed auction mechanism increases the profit of the edge servers by at least 50% compared to randomized auctions, and satisfies the task processing delay and energy consumption constraints of mobile devices.
    Description
    Click on the DOI link to access the article (may not be free).
    URI
    https://doi.org/10.1016/j.comnet.2020.107527
    https://soar.wichita.edu/handle/10057/19554
    Collections
    • EECS Research Publications

    Browse

    All of Shocker Open Access RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsBy TypeThis CollectionBy Issue DateAuthorsTitlesSubjectsBy Type

    My Account

    LoginRegister

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    DSpace software copyright © 2002-2023  DuraSpace
    DSpace Express is a service operated by 
    Atmire NV