In the era of Internet of Things (IoT), various computation-intensive applications emerge and bring great challenges to IoT devices with limited computation capability. Mobile edge computing (MEC) provides rich computing resources for IoT devices and improves applications’ execution efficiency. In this paper, we model applications as directed acyclic graphs (DAG) and target to minimize applications’ execution latency in heterogeneous MEC networks. To solve this problem, a Deep Q-Network (DQN)-based task scheduling (DQNTS) algorithm is proposed by utilizing deep reinforcement learning (DRL). By modeling the task scheduling process as a Markov decision process (MDP) and designing its critical elements, satisfying scheduling decisions are obtained. Simulation results show that the proposed algorithm achieves lower execution latency than the compared algorithms and it is adaptable to different MEC network environments.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Reinforcement Learning-Based Task Scheduling in Heterogeneous MEC Networks


    Beteiligte:
    Shang, Ying (Autor:in) / Li, Jinglei (Autor:in) / Qin, Meng (Autor:in) / Yang, Qinghai (Autor:in)


    Erscheinungsdatum :

    2022-06-01


    Format / Umfang :

    549133 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Deep Reinforcement Learning Framework for Joint Resource Allocation in Heterogeneous Networks

    Zhang, Yong / Kang, Canping / Teng, YingLei et al. | IEEE | 2019


    Task Assignment of UAV Swarms Based on Deep Reinforcement Learning

    Bo Liu / Shulei Wang / Qinghua Li et al. | DOAJ | 2023

    Freier Zugriff


    Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling

    Wang, Xiucheng / Ma, Longfei / Li, Haocheng et al. | IEEE | 2022