{'fraction_non_alphanumeric': 0.02075154234436343, 'fraction_numerical': 0.0011217049915872126, 'mean_word_length': 5.371428571428571, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Deep Reinforcement Learning Based Computing Offloading Decision and Task Scheduling in Internet of Vehicles

Applying MEC (Mobile Edge Computing) technology to the IoV (Internet of Vehicles) is capable of relieving the stress on the network, which can satisfy the requirements of computing applications effectively. However, the computational and storage resources of the MEC server are limited. Therefore, it is important to select the appropriate service nodes to compute the vehicle tasks and allocate multi-domain resources jointly to meet the demands of low-latency and high-speed tasks in the C-V2X (Cellular-Vehicle-to-Everything) scenario. In addition, the subtask scheduling sequence in the data transmission and the order of subtasks processed by different service nodes will make big difference on the task total completion time. In order to solve the problem of offloading multiple independent subtasks of a single vehicle, this paper proposes a task scheduling and offloading scheme based on DQN (Deep Q-Network) with the target of minimizing the task total completion time. And the proposed scheme can find the best subtask offloading sequence and the suitable service nodes under the guarantee of V2V (Vehicle-to-Vehicle) communication reliability. Besides, this paper proposes an indicator called task time overlap rate to analyze the optimization performance of the proposed algorithm, and to explore the realization mechanism of the optimal multi-domain resource allocation scheme as well. Simulation results show that the proposed scheme is able to reduce the total task completion delay effectively by dynamically adjusting the offloading strategy in comparison with the other methods, so that it can better adapt to the scenarios of different parameter settings.