{"title":"A High-Grained Traffic Prediction for Microseconds Power Control in Energy-Aware Routers","authors":"Sou Koyano, S. Ata, I. Oka, K. Inoue","doi":"10.1109/UCC.2012.55","DOIUrl":null,"url":null,"abstract":"Recently, as significant increase of Internet traffic, power consumption of ICT devices is growing dramatically. Energy-saving of routers is one of important problems in future networks. There are some studies to reduce the power consumption by adjusting routers' performance according to the volume of incoming/outgoing traffic. In such routers high-grained and accurate prediction of future traffic is very important for controlling power savings. In this paper, we propose a traffic prediction method suitable for performance adjustable routers which achieves accurate traffic prediction in short-term. We discuss about the impacts of prediction parameters and their tuning methods, for accurate of prediction. Through trace-driven simulations with real traffic, we show that our prediction method can reduce up to 95% of power consumption without any packet loss.","PeriodicalId":122639,"journal":{"name":"2012 IEEE Fifth International Conference on Utility and Cloud Computing","volume":"518 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Fifth International Conference on Utility and Cloud Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UCC.2012.55","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Recently, as significant increase of Internet traffic, power consumption of ICT devices is growing dramatically. Energy-saving of routers is one of important problems in future networks. There are some studies to reduce the power consumption by adjusting routers' performance according to the volume of incoming/outgoing traffic. In such routers high-grained and accurate prediction of future traffic is very important for controlling power savings. In this paper, we propose a traffic prediction method suitable for performance adjustable routers which achieves accurate traffic prediction in short-term. We discuss about the impacts of prediction parameters and their tuning methods, for accurate of prediction. Through trace-driven simulations with real traffic, we show that our prediction method can reduce up to 95% of power consumption without any packet loss.