Reinforcement learning based routing for time-aware shaper scheduling in time-sensitive networksopen access
- Authors
- Min, Junhong; Kim, Yongjun; Kim, Moonbeom; Paek, Jeongyeup; Govindan, Ramesh
- Issue Date
- Nov-2023
- Publisher
- Elsevier B.V.
- Keywords
- Network performance evaluation; Network simulation; Reinforcement learning; Routing; Scheduling; Time-Aware Shaper; TAS; Time-Sensitive Network; TSN
- Citation
- Computer Networks, v.235
- Journal Title
- Computer Networks
- Volume
- 235
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/68228
- DOI
- 10.1016/j.comnet.2023.109983
- ISSN
- 1389-1286
1872-7069
- Abstract
- To guarantee real-time performance and quality-of-service (QoS) of time-critical industrial systems, time-aware shaper (TAS) in time-sensitive networking (TSN) controls frame transmission times in a bridged network using a scheduled gate control mechanism. However, most TAS scheduling methods generate schedules based on pre-configured routes without exploring alternatives for better schedulability, and methods that jointly consider routing and scheduling require enormous runtime and computing resources. To address this problem, we propose a TSN Scheduler with Reinforcement Learning-based Routing (TSLR) that identifies improved load balanced routes for higher schedulability with acceptable complexity using distributional reinforcement learning. We evaluate TSLR through TSN simulations and compare it against state-of-the-art algorithms to demonstrate that TSLR effectively improves TAS schedulability and link utilization in TSN with lower complexity. Specifically, TSLR shows a more than 66% increase in schedulability compared to the other algorithms, and TSLR's scheduling time is reduced by more than 1 h. It also shows flows’ transmission latency is less than 25% of their latency deadline requirement and reduces maximum link utilization by approximately 50%. © 2023 The Author(s)
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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