Task Data Offloading and Resource Allocation in Fog Computing with Multi-Task Delay Guarantee

Mithun Mukherjee, Suman Kumar, Qi Zhang, R. Matam, Constandinos X. Mavromoustakis, Yunrong Lv, George Mastorakis

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    Abstract

    With the emergence of delay-sensitive task completion, computational offloading becomes increasingly desirable due to the end-users limitations in performing computation-intense applications. Interestingly, fog computing enables computational offloading for the end-users towards delay-sensitive task provisioning. In this paper, we study the computational offloading for the multiple tasks with various delay requirements for the end-users, initiated one task at a time in end-user side. In our scenario, the end-user offloads the task data to its primary fog node. However, due to the limited computing resources in fog nodes compared to the remote cloud server, it becomes a challenging issue to entirely process the task data at the primary fog node within the delay deadline imposed by the applications initialized by the end-users. In fact, the primary fog node is mainly responsible for deciding the amount of task data to be offloaded to the secondary fog node and/or remote cloud. Moreover, the computational resource allocation in term of CPU cycles to process each bit of the task data at fog node and transmission resource allocation between a fog node to the remote cloud are also important factors to be considered. We have formulated the above problem as a Quadratically Constraint Quadratic Programming (QCQP) and provided a solution. Our extensive simulation results demonstrate the effectiveness of the proposed offloading scheme under different delay deadlines and traffic intensity levels.

    OriginalsprogEngelsk
    Artikelnummer8839780
    TidsskriftIEEE Access
    Vol/bind7
    Sider (fra-til)152911-152918
    Antal sider8
    ISSN2169-3536
    DOI
    StatusUdgivet - 2019

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