ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2016, Vol. 53 ›› Issue (1): 3-14.doi: 10.7544/issn1000-1239.2016.20150660

Special Issue: 2016优青专题

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Research on the Development of the Internet Performance Measurement Technologies

Yin Hao1, Li Feng2   

  1. 1(Tsinghua National Laboratory for Information Science and Technology, Beijing 100084); 2(Department of Computer Science and Technology, Tsinghua University, Beijing 100084)
  • Online:2016-01-01

Abstract: 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.

Key words: network performance measurement, path composition, quality of service(QoS), quality of experience(QoE), machine learning

CLC Number: