{"title":"A network function modeling approach for performance estimation","authors":"M. Baldi, Amedeo Sapio","doi":"10.1109/RTSI.2015.7325152","DOIUrl":null,"url":null,"abstract":"This work introduces a methodology for the modelization of network functions focused on the identification of recurring execution patterns and aimed at providing a platform independent representation. By mapping the model on specific hardware, the performance of the network function can be estimated in terms of maximum throughput that the network function can achieve on the specific execution platform. The approach is such that once the basic modeling building blocks have been mapped, the estimate can be computed automatically. Among other relevant applications, the performance estimation capabilities enabled by the presented modelization approach are key in supporting orchestration of network functions virtualization (NFV) platforms. Being able to automatically estimate the performance of a virtualized network function (VNF) on different execution hardware, enables its optimal placement while efficiently utilizing available resources.","PeriodicalId":187166,"journal":{"name":"2015 IEEE 1st International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 1st International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RTSI.2015.7325152","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
This work introduces a methodology for the modelization of network functions focused on the identification of recurring execution patterns and aimed at providing a platform independent representation. By mapping the model on specific hardware, the performance of the network function can be estimated in terms of maximum throughput that the network function can achieve on the specific execution platform. The approach is such that once the basic modeling building blocks have been mapped, the estimate can be computed automatically. Among other relevant applications, the performance estimation capabilities enabled by the presented modelization approach are key in supporting orchestration of network functions virtualization (NFV) platforms. Being able to automatically estimate the performance of a virtualized network function (VNF) on different execution hardware, enables its optimal placement while efficiently utilizing available resources.