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In this paper, we propose using RNN with long short-term memory (LSTM) units for server load and performance prediction. Classical methods for performance prediction focus on building relation between performance and time domain, which makes a lot of unrealistic hypotheses. Our model is built based on events (user requests), which is the root cause of server performance. We predict the performance of the servers using RNN-LSTM by analyzing the log of servers in data center which contains user\u2019s access sequence. Previous work for workload prediction could not generate detailed simulated workload, which is useful in testing the working condition of servers. Our method provides a new way to reproduce user request sequence to solve this problem by using RNN-LSTM. Experiment result shows that our models get a good performance in generating load and predicting performance on the data set which has been logged in online service. We did experiments with nginx web server and mysql database server, and our methods can been easily applied to other servers in data center.<\/jats:p>","DOI":"10.1155\/2017\/8584252","type":"journal-article","created":{"date-parts":[[2017,11,26]],"date-time":"2017-11-26T23:32:15Z","timestamp":1511739135000},"page":"1-10","source":"Crossref","is-referenced-by-count":28,"title":["Deep Recurrent Model for Server Load and Performance Prediction in Data Center"],"prefix":"10.1155","volume":"2017","author":[{"given":"Zheng","family":"Huang","sequence":"first","affiliation":[{"name":"School of Cyber Security, Shanghai Jiao Tong University, Shanghai, China"},{"name":"Westone Cryptologic Research Center, Beijing 100070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9948-1753","authenticated-orcid":true,"given":"Jiajun","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Cyber Security, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huijuan","family":"Lian","sequence":"additional","affiliation":[{"name":"School of Cyber Security, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Cyber Security, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weidong","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Cyber Security, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1108\/10662241011059471"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2011.05.027"},{"key":"6","first-page":"57","volume":"8","year":"2005","journal-title":"Computer Engineering and Design"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-017-2044-4"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2006.889401"},{"key":"8","volume-title":"Joint language and translation modeling with recurrent neural networks","volume":"3","year":"2013","edition":"8"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2015.2400218"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2527239"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2703832"},{"issue":"99","key":"14","first-page":"1","volume":"pp","year":"2017","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90020-8"},{"issue":"1","key":"22","first-page":"1929","volume":"15","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"16","volume":"3","year":"2015","journal-title":"Learning"},{"key":"15","year":"2012"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2017\/8584252.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2017\/8584252.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2017\/8584252.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T16:02:32Z","timestamp":1589817752000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/complexity\/2017\/8584252\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":15,"alternative-id":["8584252","8584252"],"URL":"https:\/\/doi.org\/10.1155\/2017\/8584252","relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}