The digital transformation is pushing the existing network technologies towards new horizons, enabling new applications (e.g., vehicular networks). As a result, the networking community has seen a noticeable increase in the requirements of emerging network applications. One main open challenge is the need to accommodate control systems to highly dynamic network scenarios. Nowadays, existing network optimization technologies do not meet the needed requirements to effectively operate in real time. Some of them are based on hand-crafted heuristics with limited performance and adaptability, while some technologies use optimizers which are often too time-consuming. Recent advances in Deep Reinforcement Learning (DRL) have shown a dramatic improvement in decision-making and automated control problems. Consequently, DRL represents a promising technique to efficiently solve a variety of relevant network optimization problems, such as online routing. In this paper, we explore the use of state-of-the-art DRL technologies for real-time routing optimization and outline some relevant open challenges to achieve production-ready DRL-based solutions.
翻译:数字转型正在将现有的网络技术推向新的视野,使新的应用(例如车辆网络)得以实现。因此,网络界看到新兴网络应用的需求明显增加。一个主要的公开挑战就是需要将控制系统适应高度动态的网络情景。如今,现有的网络优化技术无法满足实时有效运行所需的要求。其中一些技术基于手工制作的超速技术,其性能和适应性都有限,而有些技术则使用通常太费时的优化工具。深强化学习(DRL)最近的进展显示决策和自动化控制问题有了显著改善。因此,DRL是一种有希望的方法,可有效解决各种相关的网络优化问题,例如在线线路设置。我们在本文件中探索将最新技术DRL技术用于实时路程优化,并概述一些相关的公开挑战,以实现适合生产的DRL解决方案。