{"title":"在服务器功率建模中结合全局回归和局部逼近","authors":"Xiaoming Du, Cong Li","doi":"10.1007/s00450-018-0391-x","DOIUrl":null,"url":null,"abstract":"To evaluate energy use in green clusters, power models take the resource utilization data as the input to predict server power consumption. We propose a novel method in power modeling combining a global linear model and a local approximation model. The new model enjoys high accuracy by compensating the global linear model with local approximation and exhibits robustness with the generalization capability of the global regression model. Empirical evaluation demonstrates that the new approach outperforms the two existing approaches to server power modeling, the linear model and the k-nearest neighbor regression model.","PeriodicalId":41265,"journal":{"name":"SICS Software-Intensive Cyber-Physical Systems","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2018-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Combining global regression and local approximation in server power modeling\",\"authors\":\"Xiaoming Du, Cong Li\",\"doi\":\"10.1007/s00450-018-0391-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To evaluate energy use in green clusters, power models take the resource utilization data as the input to predict server power consumption. We propose a novel method in power modeling combining a global linear model and a local approximation model. The new model enjoys high accuracy by compensating the global linear model with local approximation and exhibits robustness with the generalization capability of the global regression model. Empirical evaluation demonstrates that the new approach outperforms the two existing approaches to server power modeling, the linear model and the k-nearest neighbor regression model.\",\"PeriodicalId\":41265,\"journal\":{\"name\":\"SICS Software-Intensive Cyber-Physical Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2018-05-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SICS Software-Intensive Cyber-Physical Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s00450-018-0391-x\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SICS Software-Intensive Cyber-Physical Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s00450-018-0391-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
Combining global regression and local approximation in server power modeling
To evaluate energy use in green clusters, power models take the resource utilization data as the input to predict server power consumption. We propose a novel method in power modeling combining a global linear model and a local approximation model. The new model enjoys high accuracy by compensating the global linear model with local approximation and exhibits robustness with the generalization capability of the global regression model. Empirical evaluation demonstrates that the new approach outperforms the two existing approaches to server power modeling, the linear model and the k-nearest neighbor regression model.