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A3C-based load-balancing solution for computation offloading in SDN-enabled vehicular edge computing networks
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DOI：10.1007/s12083-022-01412-6.png)
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摘要
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In the Internet of vehicles (IoV), various applications emerging to provide users with safe, reliable, and comfortable driving services have caused explosive demand for computing capability. It is very challenging for vehicles with limited resources to meet the real-time demand. Mobile Edge Computing (MEC) is a convenient solution that utilizes computing resources at the network edge to enhance the capability of vehicles. With the help of MEC, vehicles can offload parts of tasks to nearby MEC servers to reduce the response cost. However, with the increase of vehicles, the unbalanced load among servers may lead to the unbalanced resource allocation and the degradation of task completion ratio. To address this problem, we first propose an SDN-enabled vehicular edge computing network architecture to facilitate the management of IoV with a global view. Then, the joint optimization problem of computation offloading and load balancing is formulated as a sequential decision problem to minimize the system cost in terms of time and energy. An A3C-based solution is proposed to solve the offloading decisions. Further, we develop the simple computation resource allocation scheme and calculation method of offloading ratio, respectively. Finally, the simulation results demonstrate the superior performance of our proposed algorithm compared to the benchmark solutions.
Keyword:
MEC
IoV
SDN
Computation offloading
Load balancing
Deep Reinforcement Learning
期刊
IF:
2.6
论文数:
2.2K
被引数:
2.9K
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引用论文
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