主动和被动测量校园,区域和国家网络骨干路径

P. Calyam, Dima Krymskiy, M. Sridharan, P. Schopis
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引用次数: 33

摘要

互联网服务提供商(isp)使用网络测量基础设施(nmi)对其网络进行测量已成为一种普遍做法。这些nmi支持网络范围内的“主动”和“被动”测量数据收集和分析,以:1)识别网络路径中的端到端性能瓶颈;2)在持续的基础上广泛了解互联网流量特征。在本文中,我们分析了在典型的校园、区域和国家网络中沿网络主干路径收集的主动和被动测量数据,这些网络承载着高质量的语音和视频会议、多媒体流和分布式文件共享等尖端互联网应用的流量。利用“ActiveMon”软件获得主动测量数据,该软件是我们在上述网络主干路径上开发和部署的。被动测量数据是利用SNMP、Syslog和NetFlow数据在位于同一网络主干路径上的战略点的中间路由器上获得的。我们对测量数据的分析包括研究值得注意的趋势、网络事件和网络骨干路径的相对性能问题,这些问题反映在几个月来定期收集的主动和被动测量数据中。因此,我们的研究结果为不同学术网络主干的流量动态提供了有价值的见解,可以用于更好地设计和控制网络,也可以基于真实网络的经验数据开发流量源模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Active and passive measurements on campus, regional and national network backbone paths
It has become a common practice for Internet service providers (ISPs) to instrument their networks with network measurement infrastructures (NMIs). These NMIs support network-wide "active" and "passive" measurement data collection and analysis to: 1) identify end-to-end performance bottlenecks in network paths and 2) broadly understand Internet traffic characteristics, on an ongoing basis. In this paper, we present our analysis of the active and passive measurement data collected along network backbone paths within typical campus, regional and national networks which carry traffic of cutting-edge Internet applications such as high-quality voice and video conferencing, multimedia streaming and distributed file sharing. The active measurement data has been obtained by using "ActiveMon" software, which we have developed and deployed along the above network backbone paths. The passive measurement data has been obtained using SNMP, Syslog and NetFlow data available at the intermediate routers located at strategic points along the same network backbone paths. Our analysis of the measurement data includes studying notable trends, network events and relative performance issues of the network backbone paths which are reflected in the active and passive measurement data collected regularly over several months. Our results thus provide valuable insights regarding traffic dynamics in the different academic network backbones and can be used for better design and control of networks and also to develop traffic source models based on empirical data from real-networks.
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