论文标题
SRV6-PM:具有云本地体系结构的SRV6网络的性能监视
SRv6-PM: Performance Monitoring of SRv6 Networks with a Cloud-Native Architecture
论文作者
论文摘要
IPv6(简称SRV6)上的段路由是IP主干和数据中心的网络解决方案。 SRV6标准化,实施和研究正在积极进展,并且已经在许多大型网络部署中采用了SRV6。强烈需要针对SRV6网络的有效性能监控(PM)解决方案。此类PM解决方案的设计,实现和部署涵盖了网络体系结构的不同平面:需要测量(数据包丢失和延迟)(数据包丢失和延迟)(在数据平面中),被监视的节点需要控制(在控制平面中),需要收集测得的数据(在控制/管理平面中),然后将数据进行处理,然后使用该数据进行处理,并使用该处理。 我们专注于损失监测,通过考虑能够在接近实际时间内跟踪单数据包丢失事件的解决方案(例如,延迟20秒)。 我们描述了SRV6-PM,这是用于SRV6网络性能监视的解决方案。 SRV6-PM具有用于Linux路由器的基于SDN的控制以及PM数据的摄入,处理,存储和可视化的云本地体系结构。在数据平面中,SRV6-PM包括用于Linux路由器中的有效构建块(例如,计数计数组件)。 SRV6-PM作为开源发布。我们不仅为PM实验提供了可重现的环境,而且还提供了可重复使用且可扩展的云本地平台,该平台可以自动部署在不同的环境中,从单个主机到私有/公共云上的多个服务器。
Segment Routing over IPv6 (SRv6 in short) is a networking solution for IP backbones and datacenters. The SRv6 standardization, implementation and research are actively progressing and SRv6 has already been adopted in a number of large scale network deployments. Effective Performance Monitoring (PM) solutions for SRv6 networks are strongly needed. The design, implementation and deployment of such PM solutions span the different planes of a networking architecture: Performance Measurements data (packet loss and delay) needs to be measured (in the Data Plane), the monitored nodes needs to be controlled (in the Control Plane), the measured data needs to be collected (in the Control/Management Planes), then the Data must be processed and stored, using Big-Data processing solutions. We focus on Loss Monitoring, by considering a solution capable of tracking single packet loss events in near-real time (e.g. with a delay in the order of 20 seconds). We describe SRv6-PM, a solution for Performance Monitoring of SRv6 networks. SRv6-PM features a cloud-native architecture for the SDN-based control of Linux routers and for ingestion, processing, storage and visualization of PM data. In the Data Plane, SRv6-PM includes efficient building blocks for packet loss evaluation (e.g. the packet counting components) in a Linux router. SRv6-PM is released as open source. Not only we provide a reproducible environment for PM experiments, but we also offer a re-usable and extensible cloud-native platform that can be automatically deployed in different environments, from a single host to multiple servers on private/public clouds.