NFVPerf:支持NFV在线性能监控和瓶颈检测

Priyanka Naik, Dilip Kumar Shaw, Mythili Vutukuru
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引用次数: 53

摘要

网络功能虚拟化(NFV)是网络中的一种新趋势,其中网络功能正在从定制硬件设备转移到运行在托管在商品硬件上的虚拟机(vm)上的软件实现。虽然NFV的好处,如降低成本和提高敏捷性是众所周知的,但软件实现是否能与硬件设备提供的高性能相匹配,仍然存在疑问。在这种情况下,网络运营商将受益于从供应商那里获得的监控性能和识别虚拟网络功能(VNF)实现瓶颈的框架。虽然已经有几种技术可以识别基于云的应用程序中的性能问题,但大多数技术要么使用硬件资源利用率来识别热点(使它们无法检测非硬件性能瓶颈),要么依赖于特定于应用程序的测量(vnf可能并不总是向供应商公开这些测量)。本文介绍了NFVPerf——一种面向NFV的性能监控和瓶颈检测工具。NFVPerf作为承载NFV部署的云的一部分,并将指定VNF基本架构的配置文件作为输入。它嗅探所有vm到vm通信路径上的数据包,计算每跳吞吐量和延迟,并单独使用这些“黑盒”测量来实时识别性能瓶颈(包括软件瓶颈),而不需要任何VNF检测。此外,NFVPerf可以定制为任何VNF实现,只需更改配置。我们对NFVPerf的评估表明,它可以监控性能并识别NFV部署中的瓶颈,具有高精度和最小的开销。我们相信NFVPerf这样的系统将会成为NFV时代云管理系统的一个很好的补充。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
NFVPerf: Online performance monitoring and bottleneck detection for NFV
Network Function Virtualization (NFV) is a new trend in networking, where network functions are moving from custom hardware appliances to software implementations running on virtual machines (VMs) hosted on commodity hardware. While the benefits of NFV such as cost reduction and increased agility are well understood, doubts still exist on whether a software implementation can match up to the high performance that hardware appliances deliver. In this context, network operators would benefit from frameworks that monitor performance and identify bottlenecks in Virtual Network Function (VNF) implementations obtained from vendors. While several techniques already exist to identify performance issues in cloud-based applications, most of them either use hardware resource utilizations to identify hot-spots (making them incapable of detecting non-hardware performance bottlenecks) or rely on application specific measurements (which may not be exposed by VNFs to vendors always). This paper describes NFVPerf, a performance monitoring and bottleneck detection tool for NFV. NFVPerf works as part of a cloud that hosts a NFV deployment, and takes a configuration file specifying the basic architecture of the VNF as input. It sniffs packets on all VM-to-VM communication paths, computes per-hop throughputs and delays, and uses these “black-box” measurements alone to identify performance bottlenecks (including software bottlenecks) in real time, without requiring any instrumentation of the VNF. Further, NFVPerf can be customized to any VNF implementations with just configuration changes. Our evaluation of NFVPerf shows that it can monitor performance and identify bottlenecks in an NFV deployment, with high accuracy and minimal overhead. We believe that a system like NFVPerf would form a great addition to cloud management systems in the era of NFV.
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