With the advent of virtualization techniques and software-defined networking(SDN),network function virtualization(NFV)shifts network functions(NFs)from hardware implementations to software appliances,between which exi...With the advent of virtualization techniques and software-defined networking(SDN),network function virtualization(NFV)shifts network functions(NFs)from hardware implementations to software appliances,between which exists a performance gap.How to narrow the gap is an essential issue of current NFV research.However,the cumbersomeness of deployment,the water pipe effect of virtual network function(VNF)chains,and the complexity of the system software stack together make it tough to figure out the cause of low performance in the NFV system.To pinpoint the NFV system performance,we propose NfvInsight,a framework for automatic deployment and benchmarking VNF chains.Our framework tackles the challenges in NFV performance analysis.The framework components include chain graph generation,automatic deployment,and fine granularity measurement.The design and implementation of each component have their advantages.To the best of our knowledge,we make the first attempt to collect rules forming a knowledge base for generating reasonable chain graphs.NfvInsight deploys the generated chain graphs automatically,which frees the network operators from executing at least 391 lines of bash commands for a single test.To diagnose the performance bottleneck,NfvInsight collects metrics from multiple layers of the software stack.Specifically,we collect the network stack latency distribution ingeniously,introducing only less than 2.2%overhead.We showcase the convenience and usability of NfvInsight in finding bottlenecks for both VNF chains and the underlying system.Leveraging our framework,we find several design flaws of the network stack,which are unsuitable for packet forwarding inside one single server under the NFV circumstance.Our optimization for these flaws gains at most 3x performance improvement.展开更多
基金the National Key Research and Development Program of China under Grant No.2019YFB1802600the National Natural Science Foundation of China under Grant Nos.61420106013,61702480,61672499,and 61802365+1 种基金the Youth Innovation Promotion Association of Chinese Academy of Sciences under Grant Nos.2013073 and 2020105the Guangdong Province Key Laboratory of Popular High Performance Computers under Grant No.2017B030314073.
文摘With the advent of virtualization techniques and software-defined networking(SDN),network function virtualization(NFV)shifts network functions(NFs)from hardware implementations to software appliances,between which exists a performance gap.How to narrow the gap is an essential issue of current NFV research.However,the cumbersomeness of deployment,the water pipe effect of virtual network function(VNF)chains,and the complexity of the system software stack together make it tough to figure out the cause of low performance in the NFV system.To pinpoint the NFV system performance,we propose NfvInsight,a framework for automatic deployment and benchmarking VNF chains.Our framework tackles the challenges in NFV performance analysis.The framework components include chain graph generation,automatic deployment,and fine granularity measurement.The design and implementation of each component have their advantages.To the best of our knowledge,we make the first attempt to collect rules forming a knowledge base for generating reasonable chain graphs.NfvInsight deploys the generated chain graphs automatically,which frees the network operators from executing at least 391 lines of bash commands for a single test.To diagnose the performance bottleneck,NfvInsight collects metrics from multiple layers of the software stack.Specifically,we collect the network stack latency distribution ingeniously,introducing only less than 2.2%overhead.We showcase the convenience and usability of NfvInsight in finding bottlenecks for both VNF chains and the underlying system.Leveraging our framework,we find several design flaws of the network stack,which are unsuitable for packet forwarding inside one single server under the NFV circumstance.Our optimization for these flaws gains at most 3x performance improvement.
