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- W4360996481 abstract "In order to achieve the pursuit of deterministic delay and unified network transmission in the industrial automation field, the time-sensitive networking group has developed a stream reservation mechanism to reserve the required bandwidth for time-sensitive traffic. In the distributed network configuration model, how to configure per-hop delay guarantee properly has become a key problem to restrict network performance, the reason is that an excessive loose or tight guarantee will reduce the streams that can be added to the network. Therefore, this paper proposes a stream reservation method based on reinforcement learning which can dynamically adjust the per-hop delay guarantee to improve the acceptance of streams. For this, it firstly defines the optimization problem of network configuration in the reservation process and give the per-hop delay model. Then the operation of the algorithm on bridge's data plane and control plane is simulated. We evaluate the performance of the algorithm with acceptance rate and bandwidth utilization in different scenarios, and the results demonstrate the effectiveness of the proposed algorithm." @default.
- W4360996481 created "2023-03-30" @default.
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- W4360996481 date "2022-11-11" @default.
- W4360996481 modified "2023-09-29" @default.
- W4360996481 title "Reinforcement Learning-Based Stream Reservation for Distributed Time Sensitive Networks" @default.
- W4360996481 doi "https://doi.org/10.1109/icct56141.2022.10073138" @default.
- W4360996481 hasPublicationYear "2022" @default.
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