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  • Open Access


    A New Partial Task Offloading Method in a Cooperation Mode under Multi-Constraints for Multi-UE

    Shengyao Sun1,2, Ying Du3, Jiajun Chen4, Xuan Zhang5, Jiwei Zhang6,*, Yiyi Xu7

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2879-2900, 2023, DOI:10.32604/cmc.2023.037483

    Abstract In Multi-access Edge Computing (MEC), to deal with multiple user equipment (UE)’s task offloading problem of parallel relationships under the multi-constraints, this paper proposes a cooperation partial task offloading method (named CPMM), aiming to reduce UE's energy and computation consumption, while meeting the task completion delay as much as possible. CPMM first studies the task offloading of single-UE and then considers the task offloading of multi-UE based on single-UE task offloading. CPMM uses the critical path algorithm to divide the modules into key and non-key modules. According to some constraints of UE-self when offloading tasks, it gives priority to non-key… More >

  • Open Access


    Adaptive Partial Task Offloading and Virtual Resource Placement in SDN/NFV-Based Network Softwarization

    Prohim Tam1, Sa Math1, Seokhoon Kim1,2,*

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2141-2154, 2023, DOI:10.32604/csse.2023.030984

    Abstract Edge intelligence brings the deployment of applied deep learning (DL) models in edge computing systems to alleviate the core backbone network congestions. The setup of programmable software-defined networking (SDN) control and elastic virtual computing resources within network functions virtualization (NFV) are cooperative for enhancing the applicability of intelligent edge softwarization. To offer advancement for multi-dimensional model task offloading in edge networks with SDN/NFV-based control softwarization, this study proposes a DL mechanism to recommend the optimal edge node selection with primary features of congestion windows, link delays, and allocatable bandwidth capacities. Adaptive partial task offloading policy considered the DL-based recommendation to… More >

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