| Project/Area Number |
13680502
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
情報システム学(含情報図書館学)
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| Research Institution | National Institute of Informatics |
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Principal Investigator |
JI Yusheng National Institute of Informatics, Software Research Division, Associate Professor, ソフトウェア研究系, 助教授 (80225333)
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| Co-Investigator(Kenkyū-buntansha) |
FUJINO Takayuki National Institute of Informatics, Infrastructure Systems Research Division, Assistant Professor, 情報基盤研究系, 助手 (60300703)
ABE Shunji National Institute of Informatics, Infrastructure Systems Research Division, Associate Professor, 情報基盤研究系, 助教授 (00280561)
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| Project Period (FY) |
2001 – 2003
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| Project Status |
Completed (Fiscal Year 2003)
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| Budget Amount *help |
¥3,100,000 (Direct Cost: ¥3,100,000)
Fiscal Year 2003: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2002: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2001: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Keywords | quality of service / traffic control / traffic monitoring / time scale / self-similarity / long-range dependence / トラヒックモニタリニング / 自己相似性 / 品質保証 |
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| Research Abstract |
As the fast growing network technology and services for a wide range of network applications, it is necessary to support a large variety of requirements for the quality of service in communication networks. The aim of this project is to study on characteristics and control methods of network traffic, based on traffic data and statistics monitored from real networks.
Toward this end, we monitored on nodes of a wide area network and a local area network, obtained several series of traffic data traces. Through our analysis of these monitored traffic data, we evidenced the long-range dependence or so-called the self-similarity in traffic of both kinds of networks. Besides, a complex correlation property rather than the exact self-similarity was found: nearly a local Poisson property in short time scales and a strong correlation in long time scales. In order to predict the performance of traffic with such general correlation properties, we have proposed a method, which uses different fractional Brownian motion (FBM) processes to meat correlation properties in different time scales, and obtains a compound performance curve through these FBM processes. In our method, the performance in time scales with suddenly changed correlation property is considered by the compensation with another FBM process to slower the pace of the change of correlation properties. We have also found that traffic on wide area backbone networks has closer marginal distribution with Gaussian distribution than that on local area networks. And our method using multiple FBM process is proven to be more accurate for traffic with marginal distribution close to Gaussian distribution.
The goal of this research has been achieved by the thorough investigation of the traffic characteristics of internet traffic, as well as the approaches for quality of service provisioning.
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