Cross-Domain Deterministic Networking Architecture and DRL Flow Scheduling

被引：0
|
作者：
Tan W. [1 ,2 ]
Wu B. [2 ]
Wang S. [2 ,3 ]
机构：
[1] School of Cyber Science and Engineering, Southeast University, Nanjing
[2] Purple Mountain Laboratories for Network and Communication Security, Nanjing
[3] State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing
来源：
Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2023年 / 46卷 / 03期
关键词：
deep reinforcement learning; deterministic networking; flow scheduling; large-scale cross-domain communication;
D O I：
10.13190/j.jbupt.2022-197
中图分类号：
学科分类号：
摘要：
A deterministic cross-domain transmission architecture and a deep reinforcement learning (DRL)-based scheduling algorithm are proposed to address the cross-domain transmission problem in time-sensitive networks. The deterministic cross-domain transmission architecture is a wide-area deterministic networking architecture that integrates cyclic queuing forwarding with deterministic Internet Protocol. By defining a cross-domain cycle mapping function, a time-slot-based deterministic transmission channel is established to ensure bounded transmission delay. DRL states, actions, and rewards are detined in the DRL-based time-slot path joint online scheduling algorithm, and the scheduling target is to maximize the total earning value of all scheduled flows with different earning values. Experimental results demonstrate that the proposed cross-domain transmission architecture and scheduling algorithm can ensure end-to-end deterministic transmission, significantly improve the earning value of traffic scheduling, and guarantee the transmission of important traffic. © 2023 Beijing University of Posts and Telecommunications. All rights reserved.
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页码：37 / 42
页数：5
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