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Reinforcement Learning for Time-Aware Shaping (IEEE 802.1Qbv) in Time-Sensitive Networks.

, , , , and . ETFA, page 1-4. IEEE, (2023)

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Applying and Optimizing Trajectory Approach for Performance Evaluation of AFDX Avionics Network., , and . ETFA, page 1-8. IEEE, (2009)Link Between Real-Time Scheduling and Time-Triggered Networks., , , , , and . RTSS, page 397-410. IEEE, (2023)Reinforcement Learning for Time-Aware Shaping (IEEE 802.1Qbv) in Time-Sensitive Networks., , , , and . ETFA, page 1-4. IEEE, (2023)Configuring the IEEE 802.1Q Time-Aware Shaper with Deep Reinforcement Learning., , , , , and . NOMS, page 1-7. IEEE, (2024)Worst-Case Backlog Evaluation of Avionics Switched Ethernet Networks with the Trajectory Approach., , and . ECRTS, page 78-87. IEEE Computer Society, (2012)Forward end-to-end delay analysis extension for FP/FIFO policy in AFDX networks., , , and . ETFA, page 1-8. IEEE, (2017)Tighter buffer dimensioning in AFDX networks., , , and . SIGBED Review, 13 (4): 37-42 (2016)Antinomy between schedulability and quality of control using a feedback scheduler., , , , and . RTNS, page 171. ACM, (2014)Worst-case end-to-end delay analysis of an avionics AFDX network., , and . DATE, page 1220-1224. IEEE Computer Society, (2010)Applying trajectory approach to AFDX avionics network (short paper), , and . http://www.ieee.org/, IEEE, (juillet 2009)