在 tsn 中动态分配优先权的强化学习方法

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引用次数: 0

摘要

本文探讨了在时间敏感型网络中使用强化学习动态分配优先级的可能性。本文提出的方法旨在优化网络中的时间限制管理过程。利用强化学习技术,系统可根据网络流量的要求独立调整优先级。为实现这一目标,我们提出了两种基于 TSN 标准的配置方案:集中式和分布式。在考虑了这些方案后,我们将确定它们在满足接近实时的要求和确保严格的服务质量保证方面的必要限制,同时考虑到适用于时间敏感环境的限制。这项工作还揭示了使用额外设备(中央控制器)重新分配优先级的必要性。
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REINFORCEMENT LEARNING METHOD FOR DYNAMIC ALLOCATION OF PRIORITIES IN TSN
This article explores the possibility of using reinforcement learning to dynamically assign priorities in time-sensitive networks. The presented approach purposefully optimizes the process of managing time constraints in the network. Using reinforcement learning techniques, the system independently adjusts priorities depending on the requirements of network traffic. To achieve this goal, two configu ration schemes based on TSN standards are proposed: centralized and distributed. Having considered these schemes, we will identify their limitations necessary in meeting requirements close to real time and ensuring strict quality of service guarantees, taking into account the restrictions applied to a time-sensitive environment. The work also reveals the need to use additional equipment, a centralized controller, to reallocate priorities.
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