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논문검색

Research on the Distribution and Self-Similarity Characteristic of End-To-End Network Delay

초록

영어

As an important indicator of evaluating network performance, network delay can reflect the transmission performance of the current path and also the service level provided by the opposite terminal host. In the paper, analysis is first conducted on the composition of network delay; besides, through Ping measuring method, Ping measuring network delay experiment is conducted on different destination hosts of local area network (LAN) and wide area network (WAN). Besides, the distribution features of network delay as time changes is analyzed, while the influence of data package (in terms of the size) on network delay will be explored in the paper. After the process, the definition, along with the distinguishing method of the self-similarity process is introduced, and self-similarity distinguishing is rendered on network delay through the variance-time plot. According to analysis result, network delay is featured by strong nonlinearity and self-similarity. Compared with LAN, WAN is endowed with higher long-range dependency.

목차

Abstract
 1. Introduction
 2. End-to-end Delay Compositions
 3. S-Ping Method
 4. S-ping based Network Time Delay Measurement Experiments
  4.1. Periodic Property of Time Delay
  4.2. Time-variant Characteristic of Time Delay
  4.3. The Effect of Probe Packet Sizes on the Network Delay
 5. Self-similarity Characteristics of Network Delay
  5.1. Definition of Self-similarity
  5.2 Discriminant Methods of Self-similarity
  5.3. Self-similarity of the Time Delay Sequence
 6. Conclusion
 References

저자정보

  • Min Liu Hebei Normal University of Science & Technology, Qinhuangdao 066004, China
  • Yanru Xue Hebei Normal University of Science & Technology, Qinhuangdao 066004, China
  • Yang Zhao Department of Electronic and Information Technology, Jiangmen Polytechnic, Jiangmen 529090, China
  • Huiling Guo College of Computer Science and Technology, Zhoukou Normal University, Henan 466001, China

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