{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T16:28:26Z","timestamp":1783096106170,"version":"3.54.6"},"reference-count":69,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Smart Networks and Services Joint Undertaking"},{"name":"European Union&#x0027;s Horizon Europe research and innovation programme","award":["101096110"],"award-info":[{"award-number":["101096110"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Cloud Comput."],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1109\/tcc.2024.3437484","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T17:28:14Z","timestamp":1722619694000},"page":"1058-1073","source":"Crossref","is-referenced-by-count":6,"title":["<i>Sparkle:<\/i> Deep Learning Driven Autotuning for Taming High-Dimensionality of Spark Deployments"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6147-6908","authenticated-orcid":false,"given":"Dimosthenis","family":"Masouros","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6734-3575","authenticated-orcid":false,"given":"George","family":"Retsinas","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3151-2730","authenticated-orcid":false,"given":"Sotirios","family":"Xydis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6930-6847","authenticated-orcid":false,"given":"Dimitrios","family":"Soudris","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref3","article-title":"Fine tuning and enhancing performance of apache spark jobs","year":"2020"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2017.82"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/2628071.2628092"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-29006-5_7"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2021.3063278"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.08.011"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2015.2449299"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2990567"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1111\/geb.12146"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2898442.2898444"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/BF01442131"},{"key":"ref17","volume-title":"Spark: The Definitive Guide: Big Data Processing Made Simple","author":"Chambers","year":"2018"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPSW.2016.138"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/1327452.1327492"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3545008.3545018"},{"key":"ref22","article-title":"A brief review of domain adaptation","author":"Farahani","year":"2020"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378085"},{"key":"ref24","first-page":"2494","article-title":"To tune or not to tune? In search of optimal configurations for data analytics","volume-title":"Proc. 26th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining","author":"Fekry"},{"key":"ref25","article-title":"Tuneful: An online significance-aware configuration tuner for Big Data analytics","author":"Fekry","year":"2020"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2017.05.001"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.4324\/9780429277825"},{"key":"ref28","first-page":"1492","article-title":"Predictive entropy search for multi-objective bayesian optimization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hern\u00e1ndez-Lobato"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3381027"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3055019"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICDEW.2010.5452747"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045167"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11651"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3472456.3472518"},{"key":"ref36","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"ref37","first-page":"759","article-title":"Selecta: Heterogeneous cloud storage configuration for data analytics","volume-title":"Proc. USENIX Annu. Tech. Conf.","author":"Klimovic"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3529843"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2979812"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3546868"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00195"},{"key":"ref42","first-page":"1540","article-title":"Optimizing millions of hyperparameters by implicit differentiation","author":"Lorraine","year":"2020","journal-title":"The 23rd Int. Conf. Artif. Intell. Statist."},{"key":"ref43","article-title":"SGDR: Stochastic gradient descent with warm restarts","volume-title":"Proc. 5th Int. Conf. Learn. Representations","author":"Loshchilov"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CCBD.2016.034"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/IISA52424.2021.9555522"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3013948"},{"issue":"239","key":"ref47","article-title":"Docker: Lightweight linux containers for consistent development and deployment","volume":"2014","author":"Merkel","year":"2014","journal-title":"Linux J."},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CLOUD.2017.119"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.23919\/DATE51398.2021.9474122"},{"key":"ref50","first-page":"293","article-title":"Making sense of performance in data analytics frameworks","volume-title":"Proc. 12th USENIX Symp. Networked Syst. Des. Implementation","author":"Ousterhout"},{"key":"ref51","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Paszke"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47898-2_24"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2020.3034824"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2013.40"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3555041.3589677"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3041710.3041714"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ccgrid34548.2015"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/MSST.2010.5496972"},{"key":"ref59","first-page":"2960","article-title":"Practical Bayesian optimization of machine learning algorithms","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Snoek"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00041"},{"issue":"1","key":"ref61","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2015.7363768"},{"key":"ref63","first-page":"363","article-title":"Ernest: Efficient performance prediction for large-scale advanced analytics","volume-title":"Proc. 13th USENIX Symp. Networked Syst. Des. Implementation","author":"Venkataraman"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/HPCC-SmartCity-DSS.2016.0088"},{"key":"ref65","article-title":"Intel performance counter monitor-a better way to measure CPU utilization","author":"Willhalm","year":"2012"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3526157"},{"key":"ref67","volume-title":"Bio-Inspired Computation in Telecommunications","author":"Yang","year":"2015"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/ISPASS.2014.6844459"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/3173162.3173187"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1145\/2934664"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939829"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1145\/3555041.3589674"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1145\/3127479.3128605"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2021.3063118"}],"container-title":["IEEE Transactions on Cloud Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6245519\/10780435\/10621444.pdf?arnumber=10621444","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T02:02:05Z","timestamp":1733882525000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10621444\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":69,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tcc.2024.3437484","relation":{},"ISSN":["2168-7161","2372-0018"],"issn-type":[{"value":"2168-7161","type":"electronic"},{"value":"2372-0018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10]]}}}