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The jobs are elastic, as their \"size\", i.e., the amount of service needed for their completion, is determined by the schedulers.<\/jats:p>","DOI":"10.1145\/3529113.3529137","type":"journal-article","created":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T22:30:42Z","timestamp":1648247442000},"page":"67-68","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Elastic Job Scheduling with Unknown Utility Functions"],"prefix":"10.1145","volume":"49","author":[{"given":"Xinzhe","family":"Fu","sequence":"first","affiliation":[{"name":"LIDS, Massachusetts Institute of Technology, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eytan","family":"Modiano","sequence":"additional","affiliation":[{"name":"LIDS, Massachusetts Institute of Technology, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,3,25]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Scheduling despite inexact job-size information.\" in Proceedings of the ACM SIGMETRICS","author":"Wierman A.","year":"2008","unstructured":"A. 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Sinha. \"Forget the deadline: Scheduling interactive applications in data centers.\" in IEEE International Conference on Cloud Computing, pp: 293--300, 2015."},{"key":"e_1_2_1_4_1","volume-title":"pp: 1--9","author":"Zheng Z.","year":"2016","unstructured":"Z. Zheng , N. Shro?. \"Online multi-resource allocation for deadline sensitive jobs with partial values in the cloud.\" in IEEE INFOCOM , pp: 1--9 , 2016 . Z. Zheng, N. Shro?. \"Online multi-resource allocation for deadline sensitive jobs with partial values in the cloud.\" in IEEE INFOCOM, pp: 1--9, 2016."},{"key":"e_1_2_1_5_1","volume-title":"Online job scheduling in distributed machine learning clusters.\" in IEEE INFOCOM, pp: 495--503","author":"Bao Y.","year":"2018","unstructured":"Y. Bao , Y. Peng , C. Wu , and Z. Li . \" Online job scheduling in distributed machine learning clusters.\" in IEEE INFOCOM, pp: 495--503 , 2018 . Y. Bao, Y. Peng, C. Wu, and Z. Li. \"Online job scheduling in distributed machine learning clusters.\" in IEEE INFOCOM, pp: 495--503, 2018."},{"key":"e_1_2_1_6_1","volume-title":"Optimus: an efficient dynamic resource scheduler for deep learning clusters.\" in Proceedings of the Thirteenth EuroSys Conference, pp: 1--14","author":"Peng Y.","year":"2018","unstructured":"Y. Peng , Y. Bao , Y. Chen , C. Wu , and C. Guo . \" Optimus: an efficient dynamic resource scheduler for deep learning clusters.\" in Proceedings of the Thirteenth EuroSys Conference, pp: 1--14 , 2018 . Y. Peng, Y. Bao, Y. Chen, C. Wu, and C. Guo. \"Optimus: an efficient dynamic resource scheduler for deep learning clusters.\" in Proceedings of the Thirteenth EuroSys Conference, pp: 1--14, 2018."},{"key":"e_1_2_1_7_1","volume-title":"?Proteus: agile ml elasticity through tiered reliability in dynamic resource markets.\" in Proceedings of the Twelfth European Conference on Computer Systems, pp: 589--604","author":"Harlap A.","year":"2017","unstructured":"A. Harlap , A. Tumanov , A. Chung , G. R. Ganger , and P. B. Gibbons . ?Proteus: agile ml elasticity through tiered reliability in dynamic resource markets.\" in Proceedings of the Twelfth European Conference on Computer Systems, pp: 589--604 , 2017 . A. Harlap, A. Tumanov, A. Chung, G. R. Ganger, and P. B. Gibbons. ?Proteus: agile ml elasticity through tiered reliability in dynamic resource markets.\" in Proceedings of the Twelfth European Conference on Computer Systems, pp: 589--604, 2017."},{"key":"e_1_2_1_8_1","first-page":"400","volume":"2","author":"Or A.","year":"2020","unstructured":"A. Or , H. Zhang , and M. Freedman . \"Resource elasticity in distributed deep learning.\" in Proceedings of Machine Learning and Systems , Vol. 2 , pp: 400 -- 411 , 2020 . A. Or, H. Zhang, and M. Freedman. \"Resource elasticity in distributed deep learning.\" in Proceedings of Machine Learning and Systems, Vol. 2, pp: 400--411, 2020.","journal-title":"\"Resource elasticity in distributed deep learning.\" in Proceedings of Machine Learning and Systems"},{"key":"e_1_2_1_9_1","volume-title":"Slaq: quality-driven scheduling for distributed machine learning.\" in Proceedings of the Symposium on Cloud Computing, pp: 390--404","author":"Zhang H.","year":"2017","unstructured":"H. Zhang , L. Stafman , A. 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Yang. \"Analysis of large-scale multi-tenant GPU clusters for DNN training workloads.\" in USENIX Annual Technical Conference, pp: 947--960, 2019."},{"key":"e_1_2_1_11_1","volume-title":"Learning curve prediction with Bayesian neural networks","author":"Klein A.","year":"2016","unstructured":"A. Klein , S. Falkner , J. Springenberg , and F. Hutter . \" Learning curve prediction with Bayesian neural networks .\" 2016 . A. Klein, S. Falkner, J. Springenberg, and F. Hutter. \"Learning curve prediction with Bayesian neural networks.\" 2016."},{"issue":"1","key":"e_1_2_1_12_1","first-page":"213","volume":"23","author":"Agarwal A.","year":"2013","unstructured":"A. Agarwal , D. P. Foster , D. Hsu , S. M. Kakade , and A. Rakhlin . \"Stochastic convex optimization with bandit feedback.\" in SIAM Journal on Optimization , Vol. 23 , No. 1 , pp: 213 -- 240 , 2013 . A. Agarwal, D. P. Foster, D. Hsu, S. M. Kakade, and A. Rakhlin. \"Stochastic convex optimization with bandit feedback.\" in SIAM Journal on Optimization, Vol. 23, No. 1, pp: 213--240, 2013.","journal-title":"\"Stochastic convex optimization with bandit feedback.\" in SIAM Journal on Optimization"}],"container-title":["ACM SIGMETRICS Performance Evaluation Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3529113.3529137","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3529113.3529137","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:25Z","timestamp":1750182685000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3529113.3529137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,22]]},"references-count":12,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,3,22]]}},"alternative-id":["10.1145\/3529113.3529137"],"URL":"https:\/\/doi.org\/10.1145\/3529113.3529137","relation":{},"ISSN":["0163-5999"],"issn-type":[{"type":"print","value":"0163-5999"}],"subject":[],"published":{"date-parts":[[2022,3,22]]},"assertion":[{"value":"2022-03-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}