{"title":"每小时服务器工作负载预测，最多提前168小时使用季节性ARIMA模型","authors":"Van Giang Tran, V. Debusschere, S. Bacha","doi":"10.1109/ICIT.2012.6210091","DOIUrl":null,"url":null,"abstract":"Data center workload prediction is important to take decisions in resources management system. Seasonal ARIMA model provide a good server workload methodology for the server workload forecasting. A large set of our experiments confirm that it has high performance, scalability and reliability and will bee integrated in our system. This paper presents a general expression in development of our forecast model in the project EnergeTic-FUI, France.","PeriodicalId":365141,"journal":{"name":"2012 IEEE International Conference on Industrial Technology","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"57","resultStr":"{\"title\":\"Hourly server workload forecasting up to 168 hours ahead using Seasonal ARIMA model\",\"authors\":\"Van Giang Tran, V. Debusschere, S. Bacha\",\"doi\":\"10.1109/ICIT.2012.6210091\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data center workload prediction is important to take decisions in resources management system. Seasonal ARIMA model provide a good server workload methodology for the server workload forecasting. A large set of our experiments confirm that it has high performance, scalability and reliability and will bee integrated in our system. This paper presents a general expression in development of our forecast model in the project EnergeTic-FUI, France.\",\"PeriodicalId\":365141,\"journal\":{\"name\":\"2012 IEEE International Conference on Industrial Technology\",\"volume\":\"70 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"57\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Industrial Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIT.2012.6210091\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Industrial Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIT.2012.6210091","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hourly server workload forecasting up to 168 hours ahead using Seasonal ARIMA model
Data center workload prediction is important to take decisions in resources management system. Seasonal ARIMA model provide a good server workload methodology for the server workload forecasting. A large set of our experiments confirm that it has high performance, scalability and reliability and will bee integrated in our system. This paper presents a general expression in development of our forecast model in the project EnergeTic-FUI, France.
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