Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (5): 270-276.doi: 10.11896/jsjkx.201000005

• Computer Network • Previous Articles     Next Articles

Deep Reinforcement Learning-based Collaborative Computation Offloading Scheme in VehicularEdge Computing

FAN Yan-fang, YUAN Shuang, CAI Ying, CHEN Ruo-yu   

  1. School of Computer,Beijing Information Science & Technology University,Beijing 100101,China
  • Received:2020-10-03 Revised:2021-01-05 Online:2021-05-15 Published:2021-05-09
  • About author:FAN Yan-fang,born in 1979,Ph.D,is a member of China Computer Federation.Her main research interests include information security,vehicular networking and edge computing.
  • Supported by:
    National Natural Science Foundation of China(61672106),Natural Science Foundation of Beijing(L192023),Foundation of Beijing Information Science & Technology University(2025028),Graduate Science and Technology Innovation Project of Beijing Information Science & Technology University and Open Project of Beijing Key Laboratory of Internet Culture and Digital Dissemination Research.

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Abstract: Vehicular edge computing (VEC) is a key technology that can realize low latency and high reliability of internet of vehicles.Users offload computing tasks to mobile edge computing (MEC) servers,which can not only solve the problem of insufficient computing capability of vehicles,but also reduce the energy consumption and the latency of communication service.How-ever,the contradiction between the mobility of vehicles and the static deployment of edge servers in highway scenarios poses a challenge to the reliability of computing offloading.To solve this problem,this paper designs a collaborative deep reinforcement learning-based scheme for vehicles to adapt to the dynamic high-speed environment by combining the computing resources of MEC servers and neighboring vehicles.Simulation results show that compared with the scheme without vehicle collaboration,this scheme can reduce the delay and the failure rate of offloading.

Key words: Collaborative computing, Computation offloading, Deep reinforcement learning, Mobile edge computing, Vehicular edge computing

CLC Number: 

  • TN929.5
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