{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:29:22Z","timestamp":1779294562843,"version":"3.51.4"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"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":["IEEE Trans. Netw."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1109\/ton.2024.3523506","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T19:18:14Z","timestamp":1735931894000},"page":"954-965","source":"Crossref","is-referenced-by-count":1,"title":["Autoscaling via Online Optimization With Switching Cost Constraints"],"prefix":"10.1109","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6403-5822","authenticated-orcid":false,"given":"Zai","family":"Shi","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Tan","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3148149"},{"key":"ref2","first-page":"1574","article-title":"Smoothed online convex optimization in high dimensions via online balanced descent","volume-title":"Proc. 31st Conf. On Learn. Theory","author":"Chen"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-26300-2_12"},{"key":"ref4","first-page":"1953","article-title":"A Bayesian approach for bandit online optimization with switching cost","volume-title":"Proc. 39th Conf. Uncertainty Artif. Intell.","author":"Shi"},{"key":"ref5","first-page":"8370","article-title":"A Bayesian approach for stochastic continuum-armed bandit with long-term constraints","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Shi"},{"issue":"1","key":"ref6","first-page":"2503","article-title":"Trading regret for efficiency: Online convex optimization with long term constraints","volume":"13","author":"Mahdavi","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref7","first-page":"33589","article-title":"A unifying framework for online optimization with long-term constraints","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Castiglioni"},{"key":"ref8","first-page":"20636","article-title":"Online optimization with memory and competitive control","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Shi"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10228998"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IGCC.2012.6322266"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2964791.2901464"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2015.7403279"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2745844.2745854"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.1988.194354"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2020.3040249"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3410048.3410054"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3508037"},{"key":"ref18","first-page":"14520","article-title":"Leveraging predictions in smoothed online convex optimization via gradient-based algorithms","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3579442"},{"key":"ref20","first-page":"9377","article-title":"Optimal robustness-consistency tradeoffs for learning-augmented metrical task systems","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Christianson"},{"key":"ref21","first-page":"2241","article-title":"Consistent online optimization: Convex and submodular","volume-title":"Proc. 22nd Int. Conf. Artif. Intell. Statist.","volume":"89","author":"Jaghargh"},{"key":"ref22","first-page":"3477","article-title":"Minimax regret of switching-constrained online convex optimization: No phase transition","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Chen"},{"key":"ref23","first-page":"28636","article-title":"Online convex optimization with continuous switching constraint","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2021.3053910"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2017.07.012"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2016.03.001"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2015.2424876"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CLOUD.2015.91"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/IWQOS.2011.5931341"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CGC.2013.35"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/1851476.1851516"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2015.2453971"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3322205.3311087"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2012.2226216"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s10270-017-0584-y"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2015.08.006"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1287\/moor.2023.1364"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/11731139_89"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-8853-9"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1561\/9781680831719"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1137\/070708111"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611971453"},{"key":"ref44","article-title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting","author":"Oreshkin","year":"2019","journal-title":"arXiv:1905.10437"},{"key":"ref45","volume-title":"N-Beats Pytorch Module","year":"2023"}],"container-title":["IEEE Transactions on Networking"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10723154\/11039001\/10819633.pdf?arnumber=10819633","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T17:37:40Z","timestamp":1750268260000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10819633\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":45,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/ton.2024.3523506","relation":{},"ISSN":["2998-4157"],"issn-type":[{"value":"2998-4157","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6]]}}}