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Sen. Netw."],"published-print":{"date-parts":[[2024,9,30]]},"abstract":"<jats:p>Flow scheduling plays a pivotal role in enabling Time-Sensitive Networking (TSN) applications. Current flow scheduling mainly adopts a centralized scheme, posing challenges in adapting to dynamic network conditions and scaling up for larger networks. To address these challenges, we first thoroughly analyze the flow scheduling problem and find the inherent locality nature of time scheduling tasks. Leveraging this insight, we introduce the first distributed framework for IEEE 802.1Qbv TSN flow scheduling. In this framework, we further propose a multi-agent flow scheduling method by designing Deep Reinforcement Learning (DRL)-based route and time agents for route and time planning tasks. The time agents are deployed on field devices to schedule flows in a distributed way. Evaluations in dynamic scenarios validate the effectiveness and scalability of our proposed method. It enhances the scheduling success rate by 20.31% compared to state-of-the-art methods and achieves substantial cost savings, reducing transmission costs by 410\u00d7 in large-scale networks. Additionally, we validate our approach on edge devices and a TSN testbed, highlighting its lightweight nature and ease of deployment.<\/jats:p>","DOI":"10.1145\/3676848","type":"journal-article","created":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T11:11:10Z","timestamp":1720091470000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Towards Distributed Flow Scheduling in IEEE 802.1Qbv Time-Sensitive Networks"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7940-5590","authenticated-orcid":false,"given":"Miao","family":"Guo","sequence":"first","affiliation":[{"name":"College of Control Science and Engineering, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1505-6766","authenticated-orcid":false,"given":"Shibo","family":"He","sequence":"additional","affiliation":[{"name":"College of Control Science and Engineering, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2153-811X","authenticated-orcid":false,"given":"Chaojie","family":"Gu","sequence":"additional","affiliation":[{"name":"College of Control Science and Engineering, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8655-8130","authenticated-orcid":false,"given":"Xiuzhen","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Control Science and Engineering, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3155-3145","authenticated-orcid":false,"given":"Jiming","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Control Science and Engineering, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2125-0749","authenticated-orcid":false,"given":"Tao","family":"Gao","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co Ltd, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7325-0356","authenticated-orcid":false,"given":"Tongtong","family":"Wang","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co Ltd, Beijing China"}]}],"member":"320","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2950887"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICC45855.2022.9882280"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3105750"},{"key":"e_1_3_4_5_2","doi-asserted-by":"crossref","unstructured":"Sandeep Chinchali Pan Hu Tianshu Chu Manu Sharma Manu Bansal Rakesh Misra Marco Pavone and Sachin Katti. 2018. 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