A High Reliable Computing Offloading Strategy Using Deep Reinforcement Learning for IoVs in Edge Computing

In view of the serious problems of increasing delay energy consumption and decreasing service quality caused by complex network state and massive computing data in Internet of vehicles (IOT), a high reliable computing offloading strategy based on edge computing is proposed in this paper. Firstly, th...

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Vydané v:Journal of grid computing Ročník 19; číslo 2
Hlavní autori: Wang, Kun, Wang, Xiaofeng, Liu, Xuan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.06.2021
Springer Nature B.V
Predmet:
Complexity
Computation offloading
Computer Science
Computing costs
Edge computing
Energy consumption
Internet
Internet of Vehicles
Machine learning
Management of Computing and Information Systems
Mobile computing
Network control
Optimization
Processor Architectures
Quality of service architectures
Resource allocation
Resource management
Software-defined networking
Time lag
User Interfaces and Human Computer Interaction
Vehicles
Computing offloading
Privacy protection
Deep reinforcement learning algorithm
Internet of vehicles
Software defined network
Edge computing
ISSN:1570-7873, 1572-9184
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Shrnutí:In view of the serious problems of increasing delay energy consumption and decreasing service quality caused by complex network state and massive computing data in Internet of vehicles (IOT), a high reliable computing offloading strategy based on edge computing is proposed in this paper. Firstly, the architecture of the Internet of vehicles is designed. The real-time business of terminal vehicles is directly unloaded to the mobile edge computing (MEC) equipment for processing, which reduces the high transmission delay of data on the core network. The combination of software defined network (SDN) and MEC is used to provide flexible network control and centralized resource management for the Internet of vehicles. Then, according to the computing model, communication model and privacy protection model, a joint computing offload and resource allocation strategy is proposed. Finally, taking the shortest time delay and minimum computing cost as the optimization objectives, Q-learning is used to solve the problem to achieve the optimization of unloading strategy, that is, the optimal allocation of communication and computing resources, and the system security is the best. Based on the Matlab simulation platform, the system model is built to carry out the experimental test. The results show that compared with other strategies, the unloading strategy can achieve fast convergence and reduce the system overhead effectively, and the computation complexity, data size and the number of computation nodes have the least impact on the delay.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1570-7873
1572-9184
DOI:10.1007/s10723-021-09542-6