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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| Vydáno v: | Journal of grid computing Ročník 19; číslo 2 |
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Springer Netherlands
01.06.2021
Springer Nature B.V |
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| ISSN: | 1570-7873, 1572-9184 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 15 |
| Author | Liu, Xuan Wang, Kun Wang, Xiaofeng |
| Author_xml | – sequence: 1 givenname: Kun surname: Wang fullname: Wang, Kun email: eekunwang@126.com organization: College of Physics and Electronic Engineering, Shanxi University – sequence: 2 givenname: Xiaofeng surname: Wang fullname: Wang, Xiaofeng organization: College of Physics and Electronic Engineering, Shanxi University – sequence: 3 givenname: Xuan surname: Liu fullname: Liu, Xuan organization: Shanxi Electric Power Company Maintenance Branch, State Grid |
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| Keywords | Computing offloading Privacy protection Deep reinforcement learning algorithm Internet of vehicles Software defined network Edge computing |
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