互联网性能测量技术发展研究  

Research on the Development of the Internet Performance Measurement Technologies

作　　者：尹浩[1] 李峰[2] 

机构地区：[1]清华信息科学与技术国家实验室(筹),北京100084 [2]清华大学计算机科学与技术系,北京100084

出　　处：《计算机研究与发展》2016年第1期3-14,共12页Journal of Computer Research and Development

基　　金：国家"九七三"重点基础研究发展计划基金项目(2012CB315800);国家自然科学基金项目(61170290;61222213)~~

摘　　要：互联网从一个实验网络成长为如今的超复杂系统,其服务性能一直备受关注.网络性能作为衡量网络服务的重要指标,广泛地应用于服务选择、拥塞控制、路由选择、网络性能优化、未来网络体系架构设计等方面.针对这些应用需求,学术界提出了众多的网络性能测量技术.互联网性能测量技术经过长期的发展与演变,其测量范围从小规模网络发展到覆盖全球,测量目标从点到点测量发展成大规模分布式P2P测量,测量对象从QoS转变到基于用户感受的QoE,其发展过程可分为3个阶段:传统的"所见即所得"测量、路径拟合的大规模分布式测量以及大数据驱动的QoE测量,每个阶段都具有独特的测量技术.最后,分析了互联网和网络应用的高速发展对网络性能测量技术带来的挑战,指出了未来需要研究的内容和发展方向.Nowadays, the Internet network has grown into a super-complex system from a small network in a laboratory, and its performance has been of great concern. The network performance is an important indicator of evaluating the network service performance, which can be widely used in service selection, congestion control, routing selection, network performance optimization, future network system architecture design, and so on. Many Internet performance measurement technologies are developed for these application requirements. In this paper, we systematically summarize the development of the existing network performance measurement technologies： first of all, the network performance measurement technologies is classified into different models, and the advantages and disadvantages of performance measurement technologies are well studied from different points of view; and then, the network performance measurement technologies can be divided into three stages： the measurement based on ＂what you see is what you get＂, the large-scale distributed measurement based on path composition, and the big data driven QoE measurement, so the development and evolution of performance measurement technologies are well understood; finally, the challenges of network performance measurement technologies are deeply analyzed, and with the rapid development of the Internet network applications, the content which is needed to be studied in the future is pointed out, as well as the direction of development.

关 键 词：网络性能测量 路径拟合 QOS QOE 机器学习 

分 类 号：TP393.1[自动化与计算机技术—计算机应用技术]