{"title":"Tuning the Temporal Characteristics of a Kalman-Filter Method for End-to-End Bandwidth Estimation","authors":"E. Hartikainen, S. Ekelin","doi":"10.1109/E2EMON.2006.1651280","DOIUrl":null,"url":null,"abstract":"In this paper we present a way of tuning the temporal characteristics of a new available-bandwidth estimation method, BART. The estimation engine in this method is Kalman-filter based. A current estimate of the available bandwidth is maintained, and for each new sequence of probe packet pairs an updated estimate is produced. The main input parameters needed by the Kalman filter are the variance of the measurement noise and the covariance of the process noise. The former is measured by the method, whereas the latter is not in general attainable by analytical or empirical investigation. Instead, it is reasonable to treat this as a tunable parameter. We discuss how the temporal characteristics of the tracking of end-to-end available bandwidth may be tuned.","PeriodicalId":143580,"journal":{"name":"2006 4th IEEE/IFIP Workshop on End-to-End Monitoring Techniques and Services","volume":"167 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 4th IEEE/IFIP Workshop on End-to-End Monitoring Techniques and Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/E2EMON.2006.1651280","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
In this paper we present a way of tuning the temporal characteristics of a new available-bandwidth estimation method, BART. The estimation engine in this method is Kalman-filter based. A current estimate of the available bandwidth is maintained, and for each new sequence of probe packet pairs an updated estimate is produced. The main input parameters needed by the Kalman filter are the variance of the measurement noise and the covariance of the process noise. The former is measured by the method, whereas the latter is not in general attainable by analytical or empirical investigation. Instead, it is reasonable to treat this as a tunable parameter. We discuss how the temporal characteristics of the tracking of end-to-end available bandwidth may be tuned.