{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T16:15:07Z","timestamp":1772900107657,"version":"3.50.1"},"reference-count":74,"publisher":"Tsinghua University Press","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Big Data Min. Anal."],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.26599\/bdma.2023.9020014","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T17:46:40Z","timestamp":1713808000000},"page":"315-339","source":"Crossref","is-referenced-by-count":8,"title":["Extending OpenStack Monasca for Predictive Elasticity Control"],"prefix":"10.26599","volume":"7","author":[{"given":"Giacomo","family":"Lanciano","sequence":"first","affiliation":[{"name":"Scuola Normale Superiore,Pisa,Italy,56126"}]},{"given":"Filippo","family":"Galli","sequence":"additional","affiliation":[{"name":"Scuola Normale Superiore,Pisa,Italy,56126"}]},{"given":"Tommaso","family":"Cucinotta","sequence":"additional","affiliation":[{"name":"Real-Time Systems Laboratory (RETIS), Telecommunications, Computer Engineering, and Photonics Institute (TeCIP), Scuola Superiore Sant&#x0027;Anna,Pisa,Italy,56127"}]},{"given":"Davide","family":"Bacciu","sequence":"additional","affiliation":[{"name":"University of Pisa,Department of Computer Science,Pisa,Italy,56127"}]},{"given":"Andrea","family":"Passarella","sequence":"additional","affiliation":[{"name":"National Research Council,Pisa,Italy,56127"}]}],"member":"11138","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1002\/9780470940105"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/UCC.2015.87"},{"key":"ref3","article-title":"A simple and effective predictive resource scaling heuristic for large-scale cloud applications","volume-title":"presented at 2nd Int. Workshop on Applied AI for Database Systems and Applications, Online Event","author":"Rebjock"},{"key":"ref4","article-title":"A new era of DevOps, powered by machine learning","author":"Vogels","year":"2021"},{"key":"ref5","article-title":"Introducing native support for predictive scaling with Amazon EC2 auto scaling","author":"Horsfield","year":"2021"},{"key":"ref6","volume-title":"Using EC2 auto scaling predictive scaling policies with blue\/green deployments","author":"Sethi","year":"2021"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2021.111124"},{"key":"ref8","article-title":"Open Stack","volume-title":"Welcome to Monascas documentation","year":"2022"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref10","volume-title":"Time Series Analysis: Forecasting and Control","author":"Box","year":"2018"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3468737.3494104"},{"key":"ref12","article-title":"RECAP artificial data traces","author":"Leznik","year":"2019"},{"key":"ref13","article-title":"NFV Industry Specification Group","volume-title":"Network functions virtualisation, Introductory White Paper","year":"2012"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CLOUD.2011.42"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2009.04.090402"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45798-4_7"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2011.05.027"},{"key":"ref18","volume-title":"TPC, TPC-W benchmark","year":"2021"},{"key":"ref19","first-page":"37","article-title":"Predicting cloud resource utilization","volume-title":"Proc. 9th Int. Conf. Utility and Cloud Computing","author":"Borkowski"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2019.2944364"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/UCC48980.2020.00031"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CloudNet.2013.6710578"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3344341.3368821"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3492323.3495588"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132772"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.07.012"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2014.2350475"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CLOUD.2018.00013"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS47924.2020.00085"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/UCC48980.2020.00032"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/NFV-SDN.2015.7387417"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8486320"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2018.8422788"},{"key":"ref34","first-page":"7796","article-title":"Deep state space models for time series forecasting","volume-title":"Proc. 32nd Int. Conf. Neural Information Processing Systems","author":"Rangapuram"},{"key":"ref35","article-title":"Time-series extreme event forecasting with neural networks at Uber","author":"Laptev","year":"2017","journal-title":"presented at ICML 2017 Time Series Workshop, Sydney, Australia"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-019-00619-1"},{"key":"ref37","article-title":"TimeNet: Pre-trained deep recurrent neural network for time series classification","volume-title":"presented at 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning","author":"Malhotra"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9014320"},{"key":"ref39","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","volume-title":"Proc. 27th Int. Conf. Neural Information Processing Systems","author":"Sutskever"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CCGrid51090.2021.00069"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2017.2666781"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.06.006"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/NFV-SDN.2016.7919501"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2015.7417181"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3326285.3329056"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.23919\/CNSM.2017.8255982"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CCGRID.2017.15"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ACSOS-C52956.2021.00025"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CCGrid51090.2021.00098"},{"key":"ref50","volume-title":"Open Stack, Open Stack compute (nova)","year":"2022"},{"key":"ref51","volume-title":"Open Stack, Open Stack block storage (cinder) documentation","year":"2022"},{"key":"ref52","volume-title":"Open Stack, Welcome to glances documentation!","year":"2022"},{"key":"ref53","volume-title":"Open Stack, Welcome to neutrons documentation!","year":"2022"},{"key":"ref54","volume-title":"Open Stack, Welcome to the Senlin documentation!","year":"2022"},{"key":"ref55","volume-title":"Open Stack, Welcome to the heat documentation!","year":"2022"},{"key":"ref56","article-title":"How blizzard entertainment uses autoscaling with overwatch","author":"Truong","year":"2022"},{"key":"ref57","volume-title":"Open Stack, Octavia documentation","year":"2022"},{"key":"ref58","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/9780470544037.ch14"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-22885-3_30"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPSW55747.2022.00120"},{"key":"ref63","volume-title":"Monasca-predictor","author":"Lanciano","year":"2022"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.25080\/Majora-92bf1922-011"},{"key":"ref66","first-page":"8024","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume-title":"Proc. 33rd Conf. Neural Information Processing Systems","author":"Paszke"},{"key":"ref67","article-title":"TensorFlow: Large-scale machine learning on heterogeneous systems","author":"Abadi","year":"2015","journal-title":"arXiv preprint"},{"key":"ref68","volume-title":"Distwalk","author":"Cucinotta","year":"2022"},{"key":"ref69","volume-title":"Open Stack, Welcome to Kollas documentation!","year":"2022"},{"key":"ref70","volume-title":"Companion repo of the paper extending OpenStack Monasca for predictive elasticity control","author":"Lanciano","year":"2022"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.3390\/e23081064"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2021.03.012"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2021.07.021"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1145\/3309705"}],"container-title":["Big Data Mining and Analytics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8254253\/10506765\/10506814.pdf?arnumber=10506814","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T19:13:27Z","timestamp":1714763607000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10506814\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":74,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.26599\/bdma.2023.9020014","relation":{},"ISSN":["2096-0654","2097-406X"],"issn-type":[{"value":"2096-0654","type":"print"},{"value":"2097-406X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6]]}}}