论文标题
端到端网络切片的深入强化学习：挑战和解决方案
Deep Reinforcement Learning for End-to-End Network Slicing: Challenges and Solutions
论文作者
论文摘要
5G及以后有望使各种新兴用例都具有垂直行业的各种性能要求。为了成本高效地为这些用例服务，网络切片在根据特定的资源需求中动态创建虚拟端到端网络中起关键作用。网络切片可能在定义网络切片的性能的多个技术域上具有数百个可配置的参数，这使得无法使用传统的基于模型的解决方案来为网络切片编排资源。在本文中，我们讨论了如何设计和部署深层强化学习（DRL），一种无模型的方法，以解决网络切片问题。首先，我们分析网络切片问题，并提出一个符合标准的系统体系结构，该系统构建能够在5G和超越网络中基于DRL的解决方案。其次，我们对在网络切片系统中设计和部署DRL的挑战进行了深入的分析。第三，我们探讨了多种有前途的技术，即安全性和分布式DRL以及模仿学习，以自动化端到端网络切片。
5G and beyond is expected to enable various emerging use cases with diverse performance requirements from vertical industries. To serve these use cases cost-effectively, network slicing plays a key role in dynamically creating virtual end-to-end networks according to specific resource demands. A network slice may have hundreds of configurable parameters over multiple technical domains that define the performance of the network slice, which makes it impossible to use traditional model-based solutions to orchestrate resources for network slices. In this article, we discuss how to design and deploy deep reinforcement learning (DRL), a model-free approach, to address the network slicing problem. First, we analyze the network slicing problem and present a standard-compliant system architecture that enables DRL-based solutions in 5G and beyond networks. Second, we provide an in-depth analysis of the challenges in designing and deploying DRL in network slicing systems. Third, we explore multiple promising techniques, i.e., safety and distributed DRL, and imitation learning, for automating end-to-end network slicing.
