Reuse-based online joint routing and scheduling optimization mechanism in deterministic networks

被引:2
|
作者
Yang, Sijin [1 ]
Zhuang, Lei [1 ]
Lan, Julong [2 ,3 ]
Zhang, Jianhui [2 ,3 ]
Li, Bingkui [1 ]
机构
[1] Zhengzhou Univ, Sch Comp & Artificial Intelligence, Zhengzhou 450001, Peoples R China
[2] PLA Strateg Support Force Informat Engn Univ, Zhengzhou 450002, Peoples R China
[3] Song Shan Lab, Zhengzhou 450018, Peoples R China
关键词
Deterministic networks; Time-sensitive networking; Online traffic scheduling; Reusability; Deep reinforcement learning; Dynamic threshold; ALGORITHM; TSN;
D O I
10.1016/j.comnet.2023.110117
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Deterministic networks plan the entire network traffic and calculate the scheduling time to meet the critical traffic requirements of specific domains, enabling real-time and deterministic interaction of massive data. However, in dynamic industrial automation scenarios where devices undergo changes, existing mechanisms face challenges in quickly responding to dynamic transmission demand changes caused by rapid traffic migration. To address this issue, this paper proposes a reuse-based online scheduling mechanism that utilizes dynamic path planning of flows and coordinated scheduling of time slots to achieve deterministic transmission of dynamic flows. In the offline phase, the mechanism proposes a backbone link selection and a scalable intelligent routing strategy, constructs a set of routing and scheduling co-design constraints, and generates an offline scheduling table using an iterative scheduling algorithm. In the online scheduling phase, a reuse based online scheduling algorithm is proposed to achieve rapid scheduling and deterministic transmission of dynamic real-time flows. It utilizes the offline scheduling results and the period offset of migrated flows. The reuse of offline scheduling results reduces computation time and expands the solution space. Experimental results demonstrate that the proposed mechanism achieves a maximum increase in scheduling success rate of 37.3% and reduces time costs by up to 66.6% compared to existing online scheduling algorithms.
引用
收藏
页数:12
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