| Project/Area Number |
15560328
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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 |
Communication/Network engineering
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| Research Institution | Osaka University (2004-2005)
Kyoto University (2003) |
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Principal Investigator |
TAKINE Tetsuya Osaka University, Graduate School of Engineering, Professor, 大学院・工学研究科, 教授 (00216821)
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| Co-Investigator(Kenkyū-buntansha) |
MATSUDA Takahiro Osaka University, Graduate School of Engineering, Lecturer, 大学院・工学研究科, 講師 (50314381)
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| Project Period (FY) |
2003 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥3,700,000 (Direct Cost: ¥3,700,000)
Fiscal Year 2005: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2004: ¥1,400,000 (Direct Cost: ¥1,400,000)
Fiscal Year 2003: ¥1,200,000 (Direct Cost: ¥1,200,000)
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| Keywords | IP traffic / time scale / longrange dependence / packet loss ratio / closed-form solution / superposition of on-off sources / M / G / ∞ input / 最関連時間スケール / 局所ポワソン性 / IPトラヒックモデリング / マルコフ型到着流 / 周期的クラスタ到着 |
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| Research Abstract |
It is known that flow-aggregated IP traffic has multiple time-scale behaviors. In short time scales, it exhibits random characteristics, while it has long-range dependence in long time-scales. This fact is very important from an engineering view point Note that the packet loss probability and the tail distribution of delay in queueing models depend only on stochastic characteristics in a certain finite time-scale, and therefore under an appropriate condition, the packet loss probability in IP networks can be determined based on random characteristics of traffic in short time-scales. Compared with long-range dependent traffic, it is easy to handle traffic with random characteristics, and the analytical framework proposed by the investigator can be utilized for qualitative evaluation of QoS performance. In this study, we investigated the mechanism of appearance of local randomness in IP traffic and obtained a method to identify the range of time scales in which local randomness dominates the performance. For a part of this achievement, we received the Best Paper Award from the IEICE. We also studied analytical approaches to queueing models with multiple Markovian arrival streams. Note that the Markovian arrival stream can represent any stationary point process approximately with an arbitrary accuracy. In particular, we establish an approach to analyzing queues with time-dependent service speed. Further we derived the closed-form solution of the mean buffer contents in discrete-time queues with multiple Markovian arrival streams, and revealed how traffic parameters have an impact on the mean buffer contents. Moreover, we analyzed the geometric and subexponential asymptotics in Markov chains of M/G/1 type and proposed an algorithm to compute their transient solutions. With all those achievements, we established the foundations of quantitative evaluation of multiservice IP networks with variable-length packets.
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