Edge Computing Resource Allocation Orchestration System for Autonomous Vehicles
Edge computing is the key to building 5G Networks and Future 2030 Networks. Edge computing extends the cloud computing paradigm by placing resources close to the network edges to cope with the upcoming growth of connected devices. Future applications: health monitoring and predictive services within the framework of the Smart City, Internet of things (IoT), vehicular ad hoc network, autonomous vehicles present a new set of strict requirements, such as low latency. In this paper, we develop a set of methods for managing and orchestrating new intelligent services in a new network and computing infrastructure. In addition, we consider a new prototype using an orchestration system for managing the autonomous vehicles’ resources in comparison with the existing approaches to the design of high-load networks. This orchestration platform is based on independent Docker containers that running the orchestration system. The main goal of our proposed system is to build an efficient network architecture with a minimum delay to process the information based on neural networks. Finally, simulation results proved that the proposed system can significantly not only reduce the overall network load but also increase the quality of the transmitted stream across the network in comparison with traditional network architectures.
An Overview of Deep Reinforcement Learning
As a new machine learning method, deep reinforcement learning has made important progress in various fields of people's production and life since it was proposed. However, there are still many difficulties in function design and other aspects. Therefore, further research on deep reinforcement learning is of great significance for promoting the progress of the whole science and society. Based on the basic theory of deep learning, this paper introduces the basic theory, research method, main network model and successful application in various fields of deep reinforcement learning.