Edge Computing Resources Reservation in Vehicular Networks: A Meta-Learning Approach
Edge Computing Resources Reservation in Vehicular Networks: A Meta-Learning Approach
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DOI：
10.1109/tvt.2020.2983445
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发表时间：
2020-03
影响因子：
6.8
通讯作者：
Dawei Chen;Yin-Chen Liu;Baekgyu Kim;Jiang Xie;C. Hong;Zhu Han
中科院分区：
文献类型：
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作者：
Dawei Chen;Yin-Chen Liu;Baekgyu Kim;Jiang Xie;C. Hong;Zhu Han
随着自主车辆技术的发展，执行任务变得更加消耗内存和计算密集型。同时，某些任务是延迟敏感的，如协同感知，路径规划，协同同时定位和地图，实时行人检测等，由于车辆内部有限的计算资源和有限的传输带宽，边缘计算可以是一种有效的方式来协助任务的执行。从商业的角度考虑，预订或订阅成本比真实的时间要求便宜。为了最小化消费边缘服务的费用，理想的情况是根据需要保留尽可能多的资源。然而，由于道路地图的多样性，不同的时间范围（如高峰时间和非高峰时间）以及各种任务类型，车辆网络的配置在实践中是可变的，这使得找出适用于任何情况的通用机器学习模型具有挑战性。因此，为了准确预测不同场景下边缘节点的资源消耗，我们提出了一种基于元学习的两阶段方法，根据数据库中提取的元特征自适应地选择合适的机器学习算法。此外，由于边缘资源消耗数据集的不足，我们在游戏引擎Unity中编程生成了曼哈顿地区的3D模型。同时，我们改变了不同的路线图和车辆数量等因素，以更接近实践。在评估部分，我们采用均方根误差，平均绝对百分比，平均GEH作为评估指标来评估每个模型的性能。并对总成本和浪费进行了定量分析。最终，我们可以发现，所提出的基于元学习的方法优于非元学习的方法。
With the development of autonomous vehicular technologies, the execution tasks become more memory-consuming and computation-intensive. Simultaneously, a certain portion of tasks are latency-sensitive, such as collaborative perception, path planning, collaborative simultaneous localization and mapping, real-time pedestrian detection, etc. Because of the limited computation resources inside vehicles and restricted transmission bandwidth, edge computing can be an effective way to assist with the tasks execution. Considering from the perspective of business, the reservation or subscription cost is cheaper than real time requests. In order to minimize the expense of consuming edge services, the desirable situation is to reserve the resources as much as needed. However, the configuration of vehicular network is variational in practice due to the diversity of road maps, different time range like peak time and off-peak time, and the various task types, which makes it challenging to figure out a general machine learning model that is suitable for any case. Therefore, to predict the resource consumption in edge nodes accurately in different scenarios, we propose a two-stage meta-learning based approach to adaptively choose the appropriate machine learning algorithms based on the meta-features extracted on database. Besides, due to the deficiency of dataset for edge resource consumption, we program in game engine unity to generate the 3D model of Manhattan area. Meanwhile, we change the factors like different road maps and number of vehicles so as to get closer to practices. In the evaluation part, we adopt root mean square error, mean absolute percentage, and mean GEH as evaluation metrics to assess the performance of each model. Also, a quantitative analysis for the total cost and waste is also conducted. Eventually, we can find that the proposed meta-learning based method outperforms the non-meta ones.