University of Oulu

H. Yu, T. Taleb and J. Zhang, "Deep Reinforcement Learning-Based Deterministic Routing and Scheduling for Mixed-Criticality Flows," in IEEE Transactions on Industrial Informatics, vol. 19, no. 8, pp. 8806-8816, Aug. 2023, doi: 10.1109/TII.2022.3222314

Deep reinforcement learning-based deterministic routing and scheduling for mixed-criticality flows

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Author: Yu, Hao1; Taleb, Tarik1; Zhang, Jiawei2
Organizations: 1Center of Wireless Communications, The University of Oulu, 90570 Oulu, Finland
2State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.1 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-09-13


Deterministic networking has recently drawn much attention by investigating deterministic flow scheduling. Combined with artificial intelligent (AI) technologies, it can be leveraged as a promising network technology for facilitating automated network configuration in the Industrial Internet of Things (IIoT). However, the stricter requirements of the IIoT have posed significant challenges, that is, deterministic and bounded latency for time-critical applications. This article incorporates deep reinforcement learning (DRL) in cycle specified queuing and forwarding and proposes a DRL-based deterministic flow scheduler (Deep-DFS) to solve the deterministic flow routing and scheduling problem. Novel delay aware network representations, action masking and criticality aware reward function design are proposed to make deep-DFS more scalable and efficient. Simulation experiments are conducted to evaluate the performances of deep-DFS, and the results show that deep-DFS can schedule more flows than the other benchmark methods (heuristic- and AI-based methods).

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Series: IEEE transactions on industrial informatics
ISSN: 1551-3203
ISSN-E: 1941-0050
ISSN-L: 1551-3203
Volume: 19
Issue: 8
Pages: 8806 - 8816
DOI: 10.1109/TII.2022.3222314
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the European Unions Horizon 2020 Research and Innovation Program through the Charity and Accordion projects under Grant 101016509 and Grant 871793, in part by the Academy of Finland 6Genesis project under Grant 318927, and in part by the Academy of Finland IDEA-MILL Project under Grant 352428.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
352428 (Academy of Finland Funding decision)
Copyright information: © The Author(s) 2023. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see