A self-adaptive approach to service deployment under mobile edge computing for autonomous driving. (May 2019) - Xiong, Wei; Lu, Zhihui; Li, Bing; Wu, Zhao; Hang, Bo; Wu, Jie; Xuan, Xiaohua
Abstract: Mobile edge computing for autonomous driving needs to manage heterogeneous resources and process large amounts of data or multi-purpose payload. There needs to be deploying, scheduling and migrating tasks on edge nodes to ensure the reliability of tasks or maximize the utilization of resources. However, applying autonomous learning methods on autonomous driving is exceptionally difficult, due to the complexity of multi-dimensional context and the sensitivity to hyperparameters. In this paper, we propose a learning approach to quality-of-service (QoS) prediction of services via multi-dimensional context, and develop a stable approach for service deployment that requires minimal hyperparameter tuning and a modest number of trials to learn multilayer neural network policies. This approach can automatically trades off exploration against exploitation by automatically tuning hyperparameter based on maximum entropy reinforcement learning. We then demonstrate that this approach achieves state-of-the-art performance on Autoware benchmark environments. Highlights: This paper is an extension of our icws2017 paper: Xiong, W., Wu, Z., Li, B., & Gu, Q., A Learning Approach to QoS Prediction via Multi-Dimensional Context, 2017 IEEE International Conference on Web Services. We propose a learning approach to quality-of-service (QoS) prediction of services via multi-dimensional context. We propose a self-adaptive approach for service deployment under Mobile Edge Computing. We conduct comprehensive experiments on a real-world system, demonstrating the effectiveness of our approach.