{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:41:34Z","timestamp":1781714494250,"version":"3.54.5"},"reference-count":53,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T00:00:00Z","timestamp":1684281600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T00:00:00Z","timestamp":1684281600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,17]]},"DOI":"10.1109\/infocom53939.2023.10228875","type":"proceedings-article","created":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T17:40:43Z","timestamp":1693330843000},"page":"1-10","source":"Crossref","is-referenced-by-count":63,"title":["DeepScheduler: Enabling Flow-Aware Scheduling in Time-Sensitive Networking"],"prefix":"10.1109","author":[{"given":"Xiaowu","family":"He","sequence":"first","affiliation":[{"name":"Tsinghua University,School of Software and BNRist"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangwen","family":"Zhuge","sequence":"additional","affiliation":[{"name":"Tsinghua University,School of Software and BNRist"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Dang","sequence":"additional","affiliation":[{"name":"Tsinghua University,Global Innovation Exchange"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wang","family":"Xu","sequence":"additional","affiliation":[{"name":"Tsinghua University,Global Innovation Exchange"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Yang","sequence":"additional","affiliation":[{"name":"Tsinghua University,School of Software and BNRist"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS54860.2022.00072"},{"key":"ref12","article-title":"Timing and Synchronization for Time-Sensitive Applications","year":"2020","journal-title":"IEEE Std 802 1As"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICNP52444.2021.9651955"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCWorkshops50388.2021.9473879"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3452296.3472902"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3000405"},{"key":"ref11","article-title":"Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention","author":"zhang","year":"2021"},{"key":"ref10","article-title":"Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method","author":"hu","year":"2017"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2997465.2997470"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3139258.3139289"},{"key":"ref19","article-title":"Convolutional networks on graphs for learning molecular fingerprints","volume":"28","author":"duvenaud","year":"2015","journal-title":"Proc of the NeurIPS"},{"key":"ref18","article-title":"Stream Reservation Protocols (SRP) Enhancements and Performance Improvements","year":"2018","journal-title":"IEEE Std 802 1Q"},{"key":"ref51","article-title":"Deep Reinforcement Learning meets Graph Neural Networks: Exploring a routing optimization use case","author":"almasan","year":"2020"},{"key":"ref50","article-title":"Ranked reward: Enabling self-play reinforcement learning for combinatorial optimization","author":"laterre","year":"2018","journal-title":"Proc of the NeurIPS"},{"key":"ref46","first-page":"272","article-title":"Online Routing and Scheduling for Time-Sensitive Networks","author":"huang","year":"2021","journal-title":"Proc of IEEE ICDCS"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2865760"},{"key":"ref48","article-title":"Neural Combinatorial Optimization with Reinforcement Learning","author":"bello","year":"2017"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM46510.2021.9685850"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/SIES.2015.7185055"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/DASC.2018.8569837"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3163411"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3314206.3314208"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5723"},{"key":"ref8","article-title":"Pointer Networks","volume":"28","author":"vinyals","year":"2015","journal-title":"Proc of the NeurIPS"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155434"},{"key":"ref9","article-title":"Attention, learn to solve routing problems!","author":"kool","year":"2019","journal-title":"Proc of the ICLR"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2997465.2997494"},{"key":"ref3","article-title":"Enhancements for Scheduled Traffic","year":"2015","journal-title":"IEEE Std 802 1Q"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/WFCS47810.2020.9114415"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ETFA.2018.8502515"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ETFA.2015.7301436"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2012.738106"},{"key":"ref34","article-title":"Socs with hardware and software programmability","year":"2021"},{"key":"ref37","article-title":"TSN Profile for Industrial Automation","year":"2018","journal-title":"IEC\/IEEE Std 60802"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1126\/science.286.5439.509"},{"key":"ref31","article-title":"Fast graph representation learning with PyTorch Geometric","author":"fey","year":"2019","journal-title":"ICLR Workshop on Representation Learning on Graphs and Manifolds"},{"key":"ref30","first-page":"8024","article-title":"Pytorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"Proc of the NeurIPS"},{"key":"ref33","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014"},{"key":"ref32","article-title":"Rectifier nonlinearities improve neural network acoustic models","author":"maas","year":"2013","journal-title":"ICML Workshop on Deep Learning for Audio Speech and Language Processing"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2018.2888703"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s12599-014-0334-4"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCN52240.2021.9522239"},{"key":"ref38","article-title":"Github repository of z3prover","year":"2021"},{"key":"ref24","first-page":"5998","article-title":"Attention is All you Need","author":"vaswani","year":"2017","journal-title":"Proc of the NeurIPS"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-335-6.50027-1"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2020.07.063"},{"key":"ref20","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2017","journal-title":"Proc of the ICLR"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"ref21","first-page":"1263","article-title":"Neural message passing for quantum chemistry","author":"gilmer","year":"2017","journal-title":"Proc of the ICML"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/BF00992696","article-title":"Simple statistical gradient-following algorithms for connectionist reinforcement learning","volume":"8","author":"williams","year":"1992","journal-title":"Machine Learning"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511686948"},{"key":"ref29","article-title":"Prioritized experience replay","author":"schaul","year":"2016","journal-title":"ICLR (Poster)"}],"event":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","location":"New York City, NY, USA","start":{"date-parts":[[2023,5,17]]},"end":{"date-parts":[[2023,5,20]]}},"container-title":["IEEE INFOCOM 2023 - IEEE Conference on Computer Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10228851\/10228852\/10228875.pdf?arnumber=10228875","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T17:44:19Z","timestamp":1695059059000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10228875\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,17]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/infocom53939.2023.10228875","relation":{},"subject":[],"published":{"date-parts":[[2023,5,17]]}}}