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作者:

Zhu, Nafei (Zhu, Nafei.) | He, Jingsha (He, Jingsha.) (学者:何泾沙)

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EI Scopus

摘要:

Correlation of network delays or latencies among network nodes is important for network measurement. Although there have been some studies on this subject, few have studied the problem thoroughly. In this paper, we study the similarity among network nodes based on RTT. First, we study the effects of path length and load on RTT and conclude that they may affect similarity. Then, we study the effects of these two factors on similarity respectively. For the effects of path length, similarity has the same mutational trend as the common path length and the inverse mutational trend as the private path length. For load combinations, relationship between similarity and path length correlates to that between similarity and path ratio which is defined as the ratio of the common path length to the longer private path length. And our study shows that similarity has the same mutational trend as the path ratio with the ratio 1:1 being the inflection point. As for the effects of path load, similarity has the same mutational trend as the common path load and the inverse mutational trend as the private path load in any length combination. For the 27 load combinations we used, in a length combination, relationship between similarity and path load correlates to that between similarity and load ratio which is defined as the ratio of the common path load to the heavier private path load. And our study shows that similarity has the same mutational trend as the load ratio. In general, the more the two source nodes have in common along the paths to a common destination node, the more similar the RTTs are between the two nodes to the common destination node. © 2012 ACADEMY PUBLISHER.

关键词:

Computer networks

作者机构:

  • [ 1 ] [Zhu, Nafei]Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [He, Jingsha]Beijing University of Technology, Beijing 100124, China

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来源 :

Journal of Networks

ISSN: 1796-2056

年份: 2012

期: 9

卷: 7

页码: 1423-1430

ESI学科: COMPUTER SCIENCE;

ESI高被引阀值:137

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 2

ESI高被引论文在榜: 0 展开所有

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