{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:29:18Z","timestamp":1778693358175,"version":"3.51.4"},"reference-count":129,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T00:00:00Z","timestamp":1702684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Knowledge Foundation of Sweden"},{"name":"Bavarian State Ministry of Education and Culture, Science and Art"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Network"],"abstract":"<jats:p>Time-Sensitive Networking (TSN) is a set of Ethernet standards aimed to improve determinism in packet delivery for converged networks. The main goal is to provide mechanisms that enable low and predictable transmission latency and high availability for demanding applications such as real-time audio\/video streaming, automotive, and industrial control. To provide the required guarantees, TSN integrates different traffic shaping mechanisms including 802.1Qbv, 802.1Qch, and 802.1Qcr, allowing for the coexistence of different traffic classes with different priorities on the same network. Achieving the required quality of service (QoS) level needs proper selection and configuration of shaping mechanisms, which is difficult due to the diversity in the requirements of the coexisting streams under the presence of potential end-system-induced jitter. This paper discusses the suitability of the TSN traffic shaping mechanisms for the different traffic types, analyzes the TSN network configuration problem, i.e., finds the optimal path and shaper configurations for all TSN elements in the network to provide the required QoS, discusses the goals, constraints, and challenges of time-aware scheduling, and elaborates on the evaluation criteria of both the network-wide schedules and the scheduling algorithms that derive the configurations to present a common ground for comparison between the different approaches. Finally, we analyze the evolution of the scheduling task, identify shortcomings, and suggest future research directions.<\/jats:p>","DOI":"10.3390\/network3040026","type":"journal-article","created":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T12:57:56Z","timestamp":1702904276000},"page":"585-624","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["TSN Network Scheduling\u2014Challenges and Approaches"],"prefix":"10.3390","volume":"3","author":[{"given":"Hamza","family":"Chahed","sequence":"first","affiliation":[{"name":"Department of Computer Science, Karlstad University, 656 34 Karlstad, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9446-8143","authenticated-orcid":false,"given":"Andreas","family":"Kassler","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Karlstad University, 656 34 Karlstad, Sweden"},{"name":"Intelligent Networks and Systems, Deggendorf Institute of Technology, 94469 Deggendorf, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,16]]},"reference":[{"key":"ref_1","unstructured":"(2023, October 01). 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