Yogesh Sharma, Michel Gokan Khan, J. Taheri, A. Kassler
{"title":"Performance Benchmarking of Virtualized Network Functions to Correlate Key Performance Metrics with System Activity","authors":"Yogesh Sharma, Michel Gokan Khan, J. Taheri, A. Kassler","doi":"10.1109/NoF50125.2020.9249199","DOIUrl":null,"url":null,"abstract":"Industry is set to enter in a new revolution (Industry 4.0) backed by high inter-connectivity. Therefore, leveraging virtualization technology to deploy networks as virtualized network functions (VNFs) garnered attention. It helps the network operators and service providers to consolidate several VNFs on fewer of-the-shelf servers. This results in reducing the capital and operational expenditures while improving the resource efficiency. However, moving network functions from proprietary devices to standard servers comes with the profound cost of performance degradation. In order to overcome any performance issues to ensure service level agreement (SLA) requirements and before taking the solutions to real world, a sufficient verification and validation of VNFs is required. This is where Network Service benchmarking (NSB) plays a crucial role. NSB identifies any performance compromising bottlenecks by systematically evaluating the capacity of general purpose hardware resources, also know as network function virtualization infrastructure (NFVI), used to host single or multiple VNF instances. This paper presents a benchmarking methodology and framework to extract the correlation among the VNF quality of services (QoS) metrics and NFVI key performance indicators (KPls). For evaluation, VoerEir Touchstone platform is used to execute iPerf based benchmarking application to generate UDP based workload between VNFs. The results demonstrated that CPU utilization and L1- L3 cache memory are statistically correlated with packets dropped (0.43 and 0.47, respectively) and bandwidth utilization (0.99 and 0.92, respectively).","PeriodicalId":405626,"journal":{"name":"2020 11th International Conference on Network of the Future (NoF)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 11th International Conference on Network of the Future (NoF)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NoF50125.2020.9249199","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Industry is set to enter in a new revolution (Industry 4.0) backed by high inter-connectivity. Therefore, leveraging virtualization technology to deploy networks as virtualized network functions (VNFs) garnered attention. It helps the network operators and service providers to consolidate several VNFs on fewer of-the-shelf servers. This results in reducing the capital and operational expenditures while improving the resource efficiency. However, moving network functions from proprietary devices to standard servers comes with the profound cost of performance degradation. In order to overcome any performance issues to ensure service level agreement (SLA) requirements and before taking the solutions to real world, a sufficient verification and validation of VNFs is required. This is where Network Service benchmarking (NSB) plays a crucial role. NSB identifies any performance compromising bottlenecks by systematically evaluating the capacity of general purpose hardware resources, also know as network function virtualization infrastructure (NFVI), used to host single or multiple VNF instances. This paper presents a benchmarking methodology and framework to extract the correlation among the VNF quality of services (QoS) metrics and NFVI key performance indicators (KPls). For evaluation, VoerEir Touchstone platform is used to execute iPerf based benchmarking application to generate UDP based workload between VNFs. The results demonstrated that CPU utilization and L1- L3 cache memory are statistically correlated with packets dropped (0.43 and 0.47, respectively) and bandwidth utilization (0.99 and 0.92, respectively).