{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:24:23Z","timestamp":1774365863752,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,4,13]],"date-time":"2024-04-13T00:00:00Z","timestamp":1712966400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,13]],"date-time":"2024-04-13T00:00:00Z","timestamp":1712966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"R &D Program of Beijing Municipal Education Commission","award":["KM202211417008"],"award-info":[{"award-number":["KM202211417008"]}]},{"name":"R &D Program of Beijing Municipal Education Commission","award":["KZ202211417049"],"award-info":[{"award-number":["KZ202211417049"]}]},{"name":"National Key Research and Development Program of China","award":["2022YFC3300804"],"award-info":[{"award-number":["2022YFC3300804"]}]},{"name":"Beijing Social Science Foundation Program","award":["23GLC037"],"award-info":[{"award-number":["23GLC037"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s11227-024-06085-x","type":"journal-article","created":{"date-parts":[[2024,4,13]],"date-time":"2024-04-13T04:01:27Z","timestamp":1712980887000},"page":"16546-16573","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["TimeLink: enabling dynamic runtime prediction for Flink iterative jobs"],"prefix":"10.1007","volume":"80","author":[{"given":"Xiaofei","family":"Yue","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingyang","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianming","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanbing","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,13]]},"reference":[{"key":"6085_CR1","unstructured":"Agarwal S, Kandula S, Bruno N, Wu M-C, Stoica I, Zhou J (2012) Reoptimizing data parallel computing. In: 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12), pp 281\u2013294"},{"issue":"4","key":"6085_CR2","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1007\/s10619-020-07286-y","volume":"38","author":"H Al-Sayeh","year":"2020","unstructured":"Al-Sayeh H, Hagedorn S, Sattler K (2020) A gray-box modeling methodology for runtime prediction of apache spark jobs. Distribut Parall Databases 38(4):819\u2013839. https:\/\/doi.org\/10.1007\/s10619-020-07286-y","journal-title":"Distribut Parall Databases"},{"key":"6085_CR3","doi-asserted-by":"publisher","unstructured":"Al-Sayeh H, Memishi B, Jibril MA, Paradies M, Sattler K (2022) Juggler: Autonomous cost optimization and performance prediction of big data applications. In: SIGMOD \u201922: International Conference on Management of Data, Philadelphia, PA, USA, 12\u201317 June 2022, pp 1840\u20131854. https:\/\/doi.org\/10.1145\/3514221.3517892","DOI":"10.1145\/3514221.3517892"},{"key":"6085_CR4","unstructured":"Al-Sayeh H, Memishi B, Paradies M, Sattler K-U (2020) Masha: sampling-based performance prediction of big data applications in resource-constrained clusters. In: The 1st Workshop on Distributed Infrastructure, Systems, Programming and AI (DISPA). Very Large Data Base Endowment Inc.(VLDB Endowment)"},{"issue":"4","key":"6085_CR5","first-page":"28","volume":"38","author":"P Carbone","year":"2015","unstructured":"Carbone P, Katsifodimos A, Ewen S, Markl V, Haridi S, Tzoumas K (2015) Apache flink\u2122: stream and batch processing in a single engine. IEEE Data Eng Bull 38(4):28\u201338","journal-title":"IEEE Data Eng Bull"},{"key":"6085_CR6","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.ins.2014.01.015","volume":"275","author":"CLP Chen","year":"2014","unstructured":"Chen CLP, Zhang C (2014) Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf Sci 275:314\u2013347. https:\/\/doi.org\/10.1016\/j.ins.2014.01.015","journal-title":"Inf Sci"},{"issue":"6","key":"6085_CR7","doi-asserted-by":"publisher","first-page":"1494","DOI":"10.1109\/TPDS.2021.3053241","volume":"32","author":"L Cheng","year":"2021","unstructured":"Cheng L, Wang Y, Liu Q, Epema DHJ, Liu C, Mao Y, Murphy J (2021) Network-aware locality scheduling for distributed data operators in data centers. IEEE Trans Parall Distribut Syst 32(6):1494\u20131510. https:\/\/doi.org\/10.1109\/TPDS.2021.3053241","journal-title":"IEEE Trans Parall Distribut Syst"},{"key":"6085_CR8","doi-asserted-by":"crossref","unstructured":"Dongen BF, Crooy RA, Aalst WM (2008) Cycle time prediction: When will this case finally be finished? In: On the Move to Meaningful Internet Systems: OTM 2008: OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008, Monterrey, Mexico, 9\u201314 Nov 2008, Proceedings, Part I. Springer, pp 319\u2013336","DOI":"10.1007\/978-3-540-88871-0_22"},{"key":"6085_CR9","first-page":"100528","volume":"30","author":"B Everman","year":"2021","unstructured":"Everman B, Rajendran N, Li X, Zong Z (2021) Improving the cost efficiency of large-scale cloud systems running hybrid workloads-a case study of alibaba cluster traces. Sustain Comput Inf Syst 30:100528","journal-title":"Sustain Comput Inf Syst"},{"key":"6085_CR10","unstructured":"Fu S, Gupta S, Mittal R, Ratnasamy S (2021) On the use of ML for blackbox system performance prediction. In: 18th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2021, 12\u201314 Apr 2021, pp 763\u2013784"},{"key":"6085_CR11","unstructured":"Herodotou H, Lim H, Luo G, Borisov N, Dong L, Cetin FB, Babu S (2011) Starfish: a self-tuning system for big data analytics. In: Fifth Biennial Conference on Innovative Data Systems Research, CIDR 2011, Asilomar, CA, USA, January 9-12, 2011, Online Proceedings, pp 261\u2013272"},{"key":"6085_CR12","doi-asserted-by":"publisher","unstructured":"Hilman MH, Rodriguez MA, Buyya R (2018) Task runtime prediction in scientific workflows using an online incremental learning approach. In: 11th IEEE\/ACM International Conference on Utility and Cloud Computing, UCC 2018, Zurich, Switzerland, 17\u201320 Dec 2018, pp 93\u2013102. https:\/\/doi.org\/10.1109\/UCC.2018.00018","DOI":"10.1109\/UCC.2018.00018"},{"key":"6085_CR13","doi-asserted-by":"publisher","unstructured":"Hoseinyfarahabady MR, Jannesari A, Taheri J, Bao W, Zomaya AY, Tari Z (2020) Q-flink: A qos-aware controller for apache flink. In: 20th IEEE\/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020, Melbourne, Australia, 11\u201314 May 2020, pp 629\u2013638. https:\/\/doi.org\/10.1109\/CCGrid49817.2020.00-30","DOI":"10.1109\/CCGrid49817.2020.00-30"},{"key":"6085_CR14","doi-asserted-by":"publisher","unstructured":"Imran M, G\u00e9vay GE, Markl V (2020) Distributed graph analytics with datalog queries in flink. In: Software Foundations for Data Interoperability and Large Scale Graph Data Analytics\u20144th International Workshop, SFDI 2020, and 2nd International Workshop, LSGDA 2020, Held in Conjunction with VLDB 2020, Tokyo, Japan, 4 Sept 2020, Proceedings. Communications in Computer and Information Science, vol 1281, pp 70\u201383. https:\/\/doi.org\/10.1007\/978-3-030-61133-0_6","DOI":"10.1007\/978-3-030-61133-0_6"},{"key":"6085_CR15","unstructured":"Jyothi SA, Curino C, Menache I, Narayanamurthy SM, Tumanov A, Yaniv J, Mavlyutov R, Goiri I, Krishnan S, Kulkarni J, Rao S (2016) Morpheus: Towards automated slos for enterprise clusters. In: 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, Savannah, GA, USA, 2\u20134 Nov 2016, pp 117\u2013134"},{"key":"6085_CR16","doi-asserted-by":"publisher","unstructured":"Kang U, Tsourakakis CE, Faloutsos C (2009) Pegasus: A peta-scale graph mining system implementation and observations. In: 2009 Ninth IEEE International Conference on Data Mining, pp 229\u2013238. https:\/\/doi.org\/10.1109\/icdm.2009.14","DOI":"10.1109\/icdm.2009.14"},{"issue":"6","key":"6085_CR17","doi-asserted-by":"publisher","first-page":"1785","DOI":"10.1109\/TPDS.2016.2626285","volume":"28","author":"P Li","year":"2016","unstructured":"Li P, Guo S, Miyazaki T, Liao X, Jin H, Zomaya A, Wang K (2016) Traffic-aware geo-distributed big data analytics with predictable job completion time. IEEE Trans Parall Distribut Syst 28(6):1785\u20131796. https:\/\/doi.org\/10.1109\/TPDS.2016.2626285","journal-title":"IEEE Trans Parall Distribut Syst"},{"issue":"8","key":"6085_CR18","doi-asserted-by":"publisher","first-page":"716","DOI":"10.14778\/2212351.2212354","volume":"5","author":"Y Low","year":"2012","unstructured":"Low Y, Gonzalez J, Kyrola A, Bickson D, Guestrin C, Hellerstein JM (2012) Distributed graphlab: a framework for machine learning in the cloud. Proc VLDB Endow 5(8):716\u2013727. https:\/\/doi.org\/10.14778\/2212351.2212354","journal-title":"Proc VLDB Endow"},{"key":"6085_CR19","doi-asserted-by":"publisher","unstructured":"Malewicz G, Austern MH, Bik AJC, Dehnert JC, Horn I, Leiser N, Czajkowski G (2010) Pregel: a system for large-scale graph processing. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, Indianapolis, Indiana, USA, 6\u201310 June 2010, pp 135\u2013146. https:\/\/doi.org\/10.1145\/1807167.1807184","DOI":"10.1145\/1807167.1807184"},{"key":"6085_CR20","unstructured":"Metric Reporters of Apache Flink. https:\/\/nightlies.apache.org\/flink\/flink-docs-release-1.18\/docs\/deployment\/metric_reporters\/"},{"issue":"1","key":"6085_CR21","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1109\/TCC.2017.2732344","volume":"8","author":"T Pham","year":"2020","unstructured":"Pham T et al (2020) Predicting workflow task execution time in the cloud using a two-stage machine learning approach. IEEE Trans Cloud Comput 8(1):256\u2013268. https:\/\/doi.org\/10.1109\/TCC.2017.2732344","journal-title":"IEEE Trans Cloud Comput"},{"issue":"14","key":"6085_CR22","doi-asserted-by":"publisher","first-page":"1678","DOI":"10.14778\/2556549.2556553","volume":"6","author":"AD Popescu","year":"2013","unstructured":"Popescu AD, Balmin A, Ercegovac V, Ailamaki A (2013) Predict: towards predicting the runtime of large scale iterative analytics. Proc VLDB Endow 6(14):1678\u20131689. https:\/\/doi.org\/10.14778\/2556549.2556553","journal-title":"Proc VLDB Endow"},{"key":"6085_CR23","doi-asserted-by":"crossref","unstructured":"Reiss C, Tumanov A, Ganger GR, Katz RH, Kozuch MA (2012) Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In: Proceedings of the Third ACM Symposium on Cloud Computing, pp 1\u201313","DOI":"10.1145\/2391229.2391236"},{"key":"6085_CR24","unstructured":"Stanford Large Network Dataset Collection. http:\/\/snap.stanford.edu\/data\/"},{"key":"6085_CR25","unstructured":"The KONECT Project. http:\/\/konect.cc\/"},{"key":"6085_CR26","doi-asserted-by":"publisher","unstructured":"Tumanov A, Zhu T, Park JW, Kozuch MA, Harchol-Balter M, Ganger GR (2016) Tetrisched: global rescheduling with adaptive plan-ahead in dynamic heterogeneous clusters. In: Proceedings of the Eleventh European Conference on Computer Systems, EuroSys 2016, London, United Kingdom, April 18\u201321, 2016, pp 1\u201316. https:\/\/doi.org\/10.1145\/2901318.2901355","DOI":"10.1145\/2901318.2901355"},{"key":"6085_CR27","doi-asserted-by":"publisher","unstructured":"Vavilapalli VK, Murthy AC, Douglas C, Agarwal S, Konar M, Evans R, Graves T, Lowe J, Shah H, Seth S, Saha B, Curino C, O\u2019Malley O, Radia S, Reed BC, Baldeschwieler E (2013) Apache hadoop YARN: yet another resource negotiator. In: ACM Symposium on Cloud Computing, SOCC \u201913, Santa Clara, CA, USA, 1\u20133 Oct 2013, pp 5\u20131516. https:\/\/doi.org\/10.1145\/2523616.2523633","DOI":"10.1145\/2523616.2523633"},{"key":"6085_CR28","unstructured":"Venkataraman S, Yang Z, Franklin MJ, Recht B, Stoica I (2016) Ernest: efficient performance prediction for large-scale advanced analytics. In: 13th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2016, Santa Clara, CA, USA, 16\u201318 Mar 2016, pp 363\u2013378"},{"issue":"4","key":"6085_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3331449","volume":"10","author":"I Verenich","year":"2019","unstructured":"Verenich I, Dumas M, Rosa ML, Maggi FM, Teinemaa I (2019) Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring. ACM Trans Intell Syst Technol (TIST) 10(4):1\u201334","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"6085_CR30","doi-asserted-by":"publisher","unstructured":"Wang K, Khan MMH (2015) Performance prediction for apache spark platform. In: 17th IEEE International Conference on High Performance Computing and Communications, HPCC 2015, 7th IEEE International Symposium on Cyberspace Safety and Security, CSS 2015, and 12th IEEE International Conference on Embedded Software and Systems, ICESS 2015, New York, NY, USA, 24\u201326 Aug 2015, pp 166\u2013173. https:\/\/doi.org\/10.1109\/HPCC-CSS-ICESS.2015.246","DOI":"10.1109\/HPCC-CSS-ICESS.2015.246"},{"key":"6085_CR31","doi-asserted-by":"publisher","unstructured":"Wen Z, Wang Y, Liu F (2022) Stepconf: Slo-aware dynamic resource configuration for serverless function workflows. In: IEEE INFOCOM 2022\u2014IEEE Conference on Computer Communications, London, United Kingdom, 2\u20135 May 2022, pp 1868\u20131877. https:\/\/doi.org\/10.1109\/INFOCOM48880.2022.9796962","DOI":"10.1109\/INFOCOM48880.2022.9796962"},{"key":"6085_CR32","doi-asserted-by":"publisher","unstructured":"Xiao Y, Yao Y, Zhu F, Chen K (2021) Simulation runtime prediction approach based on stacking ensemble learning. In: Proceedings of the 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2021, Online Streaming, 7\u20139 July 2021, pp 42\u201349. https:\/\/doi.org\/10.5220\/0010517600420049","DOI":"10.5220\/0010517600420049"},{"key":"6085_CR33","doi-asserted-by":"publisher","unstructured":"Yadwadkar NJ, Hariharan B, Gonzalez JE, Smith B, Katz RH (2017) Selecting the best VM across multiple public clouds: a data-driven performance modeling approach. In: Proceedings of the 2017 Symposium on Cloud Computing, SoCC 2017, Santa Clara, CA, USA, 24\u201327 Sept 2017, pp 452\u2013465. https:\/\/doi.org\/10.1145\/3127479.3131614","DOI":"10.1145\/3127479.3131614"},{"key":"6085_CR34","doi-asserted-by":"crossref","unstructured":"Yue X, Shi L, Zhao Y, Ji H, Wang G (2021) Online runtime prediction method for distributed iterative jobs. In: Web Information Systems and Applications: 18th International Conference, WISA 2021, Kaifeng, China, 24\u201326 Sept 2021, Proceedings 18. Springer, pp 156\u2013168","DOI":"10.1007\/978-3-030-87571-8_14"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06085-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06085-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06085-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T11:09:52Z","timestamp":1719313792000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06085-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,13]]},"references-count":34,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["6085"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06085-x","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,13]]},"assertion":[{"value":"18 March 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and discussion reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Ethical review and approval were waived in this study because our research focused on the design and implementation of algorithms in the distributed system did not involve research with humans or animals, and was not within the scope of requiring ethical review.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}