dc.contributor.author | Susanto, Hengky | |
dc.contributor.author | Kim, ByungGuk | |
dc.date.accessioned | 2018-07-22T08:35:29Z | |
dc.date.available | 2018-07-22T08:35:29Z | |
dc.date.issued | 2015 | |
dc.identifier.issn | 2210-142X | |
dc.identifier.uri | https://journal.uob.edu.bh:443/handle/123456789/415 | |
dc.description.abstract | Understanding network traffic pattern and its impact on the Internet provides valuable insights in designing new network protocols, particularly in designing one for applications with a tendency to generate bursty traffic of data, such as Voice over IP (VoIP). To capture the behavior, network traffic can be illustrated on many scales using the notation of self-similarity because network traffic is statistically self-similar. In this paper, we propose a study on analyzing the length of a traffic interval by self-similarity based on the difference between arrival times of packets. We examine the dependency between fast and slow interval as well as a study on the data transition between both intervals. | en_US |
dc.language.iso | en | en_US |
dc.publisher | University of Bahrain | en_US |
dc.rights | Attribution-NonCommercial-ShareAlike 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
dc.subject | network traffic | en_US |
dc.subject | self-similarity | en_US |
dc.subject | Probability | en_US |
dc.title | Examining Self-Similarity Network Traffic Intervals | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.12785/IJCDS/040201 | |
dc.volume | 04 | |
dc.issue | 02 | |
dc.source.title | International Journal of Computing and Digital Systems | |
dc.abbreviatedsourcetitle | IJCDS |
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