{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T07:06:09Z","timestamp":1766732769570,"version":"3.37.3"},"reference-count":48,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2019YFB1802800"],"award-info":[{"award-number":["2019YFB1802800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"PCL Future Greater-Bay Area Network Facilities for Large-Scale Experiments and Applications","award":["PCL2018KP001"],"award-info":[{"award-number":["PCL2018KP001"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["ZYGX2016J217"],"award-info":[{"award-number":["ZYGX2016J217"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2682019CX61"],"award-info":[{"award-number":["2682019CX61"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Netw. Serv. Manage."],"published-print":{"date-parts":[[2020,9]]},"DOI":"10.1109\/tnsm.2020.2989187","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T20:08:09Z","timestamp":1587499689000},"page":"1879-1895","source":"Crossref","is-referenced-by-count":3,"title":["Grouper: Accelerating Hyperparameter Searching in Deep Learning Clusters With Network Scheduling"],"prefix":"10.1109","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3373-728X","authenticated-orcid":false,"given":"Pan","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5219-1780","authenticated-orcid":false,"given":"Hongfang","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2448-8915","authenticated-orcid":false,"given":"Gang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1287\/moor.22.3.513"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.2986616"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2018.07.035"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2012.03.040"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59250-3_2"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.orl.2010.04.011"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2016.2525767"},{"key":"ref36","article-title":"Configuration guidelines for diffserv service classes","author":"chan","year":"2006","journal-title":"RFC 4594 Internet Engineering Task Force"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2017.8056946"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2009.07.001"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2017.2669216"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-77050-3_8"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3341617.3326135"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2019.02.031"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3230543.3230569"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/2640087.2644155"},{"article-title":"Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems","year":"2015","author":"chen","key":"ref15"},{"key":"ref16","first-page":"265","article-title":"Tensorflow: A system for large-scale machine learning","author":"abadi","year":"2016","journal-title":"Proc of USENIX Symp on Operating Systems Design and Implementation (OSDI)"},{"key":"ref17","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"bergstra","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref18","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref19","first-page":"240","article-title":"Non-stochastic best arm identification and hyperparameter optimization","author":"jamieson","year":"2016","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/1402946.1402967"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5_4"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/2829988.2787508"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3135974.3135994"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/BF01581271"},{"article-title":"Hyperband: A novel bandit-based approach to hyperparameter optimization","year":"2016","author":"li","key":"ref5"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.14778\/3282495.3282499"},{"key":"ref7","first-page":"595","article-title":"Gandiva: Introspective cluster scheduling for deep learning","author":"xiao","year":"2018","journal-title":"Proc USENIX Symp Oper Syst Design Implementation (OSDI)"},{"article-title":"Analysis of large-scale multi-tenant GPU clusters for DNN training workloads","year":"2019","author":"jeon","key":"ref2"},{"key":"ref9","first-page":"17","article-title":"Eiffel: Efficient and flexible software packet scheduling","author":"saeed","year":"2019","journal-title":"Proc USENIX Symp Netw Syst Design Implem (NSDI)"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2018.00059"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3127479.3127490"},{"article-title":"Multi-tenant GPU clusters for deep learning workloads: Analysis and implications","year":"2018","author":"jeon","key":"ref20"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737460"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-61310-2_23"},{"key":"ref22","first-page":"181","article-title":"Poseidon: An efficient communication architecture for distributed deep learning on GPU clusters","author":"zhang","year":"2017","journal-title":"Proc of USENIX Annual Technical Conf (USENIX)"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/2619239.2626315"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.11.002"},{"key":"ref42","first-page":"527","article-title":"Network simulations with the ns-3 simulator","author":"henderson","year":"2008","journal-title":"SIGCOMM Demonstration"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8486340"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2019.07.026"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/2740070.2626322"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3190508.3190517"},{"key":"ref26","article-title":"Improved approximation algorithms for scheduling with release dates","author":"goemans","year":"1997","journal-title":"Assoc Comput Mach"},{"key":"ref43","first-page":"485","article-title":"Tiresias: A GPU cluster manager for distributed deep learning","author":"gu","year":"2019","journal-title":"Proc USENIX Symp Netw Syst Design Implem (NSDI)"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2019.2905560"}],"container-title":["IEEE Transactions on Network and Service Management"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4275028\/9188035\/09075260.pdf?arnumber=9075260","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T17:05:56Z","timestamp":1651079156000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9075260\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9]]},"references-count":48,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tnsm.2020.2989187","relation":{},"ISSN":["1932-4537","2373-7379"],"issn-type":[{"type":"electronic","value":"1932-4537"},{"type":"electronic","value":"2373-7379"}],"subject":[],"published":{"date-parts":[[2020,9]]}}}