{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T03:21:18Z","timestamp":1730344878319,"version":"3.28.0"},"reference-count":38,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T00:00:00Z","timestamp":1692835200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T00:00:00Z","timestamp":1692835200000},"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":[[2023,8,24]]},"DOI":"10.23919\/wiopt58741.2023.10349831","type":"proceedings-article","created":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T19:18:56Z","timestamp":1703272736000},"page":"175-182","source":"Crossref","is-referenced-by-count":0,"title":["DOLL: Distributed OnLine Learning Using Preemptible Cloud Instances"],"prefix":"10.23919","author":[{"given":"Harry","family":"Jiang","sequence":"first","affiliation":[{"name":"Carnegie Mellon University,Department of Electrical and Computer Engineering,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoxi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlee","family":"Joe-Wong","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University,Department of Electrical and Computer Engineering,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3064176.3064182"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155448"},{"key":"ref3","article-title":"Bamboo: Making preemptible instances resilient for affordable training of large dnns","author":"Thorpe","year":"2022","journal-title":"arXiv preprint"},{"journal-title":"Google Cloud Platform","article-title":"Preemptible virtual machines","year":"2021","key":"ref4"},{"journal-title":"Amazon EC2","article-title":"Amazon ec2 spot instances","year":"2021","key":"ref5"},{"key":"ref6","first-page":"3403","article-title":"Towards flexible device participation in federated learning","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR","author":"Ruan","year":"2021"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2020.2980272"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1504\/IJAIP.2018.089494"},{"key":"ref9","first-page":"119","article-title":"Ekya: Continuous learning of video analytics models on edge compute servers","volume-title":"NSDI","author":"Bhardwaj","year":"2022"},{"journal-title":"Apache","year":"2021","key":"ref10"},{"key":"ref11","article-title":"Tech talk: Machine learning at scale using distributed stream processing","author":"Topolnik","year":"2021","journal-title":"Hazelcast Webinar"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2946884"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2987550.2987576"},{"key":"ref14","article-title":"Dynamic mini-batch sgd for elastic distributed training: Learning in the limbo of resources","author":"Lin","year":"2019","journal-title":"arXiv preprint"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPSW52791.2021.00144"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/jsait.2021.3103770"},{"key":"ref17","article-title":"Real time machine learning with python","author":"Saucedo","year":"2020","journal-title":"Eu-roPython"},{"volume-title":"Doll: Distributed online learning using preemptible cloud instances","year":"2023","author":"Jiang","key":"ref18"},{"issue":"1","key":"ref19","first-page":"165","article-title":"Optimal distributed online prediction using mini-batches","volume":"13","author":"Dekel","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2968813"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3021381"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2970170"},{"key":"ref23","article-title":"Accelerating stochastic gradient descent using predictive variance reduction","volume":"26","author":"Johnson","year":"2013","journal-title":"NeurIPS"},{"key":"ref24","article-title":"Saga: A fast incremental gradient method with support for non-strongly convex composite objec-tives","volume":"27","author":"Defazio","year":"2014","journal-title":"NeurIPS"},{"key":"ref25","article-title":"Variance-reduced stochastic gradient descent on streaming data","volume":"31","author":"Jothimurugesan","year":"2018","journal-title":"NeurIPS"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2847722"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3005268"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/comst.2023.3330953"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/2901318.2901319"},{"journal-title":"EC2","article-title":"Spot instance advisor","year":"2021","key":"ref30"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2262376"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.2307\/2333008"},{"key":"ref33","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198572237.001.0001","volume-title":"Probability and random processes","author":"Grimmett","year":"2001"},{"journal-title":"Microsoft Azure","article-title":"Use spot VMs with batch","year":"2021","key":"ref34"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7496.003.0015"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/369"},{"journal-title":"The cifar-10 dataset","author":"Krizhevsky","key":"ref38"}],"event":{"name":"2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)","start":{"date-parts":[[2023,8,24]]},"location":"Singapore, Singapore","end":{"date-parts":[[2023,8,27]]}},"container-title":["2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10349702\/10349703\/10349831.pdf?arnumber=10349831","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T01:21:25Z","timestamp":1705022485000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10349831\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,24]]},"references-count":38,"URL":"https:\/\/doi.org\/10.23919\/wiopt58741.2023.10349831","relation":{},"subject":[],"published":{"date-parts":[[2023,8,24]]}}}