Reinforcement learning based routing for time-aware shaper scheduling in time-sensitive networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Min, Junhong | - |
dc.contributor.author | Kim, Yongjun | - |
dc.contributor.author | Kim, Moonbeom | - |
dc.contributor.author | Paek, Jeongyeup | - |
dc.contributor.author | Govindan, Ramesh | - |
dc.date.accessioned | 2023-10-20T06:40:24Z | - |
dc.date.available | 2023-10-20T06:40:24Z | - |
dc.date.issued | 2023-11 | - |
dc.identifier.issn | 1389-1286 | - |
dc.identifier.issn | 1872-7069 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/68228 | - |
dc.description.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) | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Elsevier B.V. | - |
dc.title | Reinforcement learning based routing for time-aware shaper scheduling in time-sensitive networks | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.comnet.2023.109983 | - |
dc.identifier.bibliographicCitation | Computer Networks, v.235 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 001110436600001 | - |
dc.identifier.scopusid | 2-s2.0-85170239186 | - |
dc.citation.title | Computer Networks | - |
dc.citation.volume | 235 | - |
dc.type.docType | Article | - |
dc.publisher.location | 네델란드 | - |
dc.subject.keywordAuthor | Network performance evaluation | - |
dc.subject.keywordAuthor | Network simulation | - |
dc.subject.keywordAuthor | Reinforcement learning | - |
dc.subject.keywordAuthor | Routing | - |
dc.subject.keywordAuthor | Scheduling | - |
dc.subject.keywordAuthor | Time-Aware Shaper | - |
dc.subject.keywordAuthor | TAS | - |
dc.subject.keywordAuthor | Time-Sensitive Network | - |
dc.subject.keywordAuthor | TSN | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Hardware & Architecture | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.