Deep Reinforcement Learning for Offloading and Resource Allocation in Vehicle Edge Computing and Networks

By Yi Liu, Huimin Yu, Shengli Xie, Yan Zhang. IEEE Transactions on Vehicular Technology, 2019

472 citations recorded by the source corpus.

Mobile Edge Computing (MEC) is a promising technology to extend the diverse services to the edge of Internet of Things (IoT) system. However, the static edge server deployment may cause “service hole” in IoT networks in which the location and service requests of the User Equipments (UEs) may be dynamically changing. In this paper, we firstly explore a vehicle edge computing network architecture in which the vehicles can act as the mobile edge servers to provide computation services for nearby UEs. Then, we propose as vehicle-assisted offloading scheme for UEs while considering the delay of the computation task. Accordingly, an optimization problem is formulated to maximize the long-term utility of the vehicle edge computing network. Considering the stochastic vehicle traffic, dynamic computation requests and time-varying communication conditions, the problem is further formulated as a semi-Markov process and two reinforcement learning methods: $Q$-learning based method and deep reinforcement learning (DRL) method, are proposed to obtain the optimal policies of computation offloading and resource allocation. Finally, we analyze the effectiveness of the proposed scheme in the vehicular edge computing network by giving numerical results.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Information Systems

View Paper: https://www.semanticscholar.org/paper/6b02cc4b1f723192be61ff083de02ea2e8f9ad3f

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Citations in the corpus

Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems (2020, methodology): “Some existing works such as [17]–[19] have proposed DRL-based algorithms for the MEC system, while they focused on centralized ofﬂoading algorithms.”
Resource Scheduling in Edge Computing: A Survey (2021, background): “[49] Things-edge UE VES and FES Offloading strategy; resource allocation Consider the stochastic vehicle traffic, dynamic computation requests and time-varying communication conditions Reinforcement learning”
Lyapunov-Guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks (2020, methodology): “Similar to [11]–[15], the estimation of state-action value function in the critic network is difficult when the num-”