時間スケールを考慮した長期依存性トラヒックの性能解析  [in Japanese] Performance Analysis of Long-range Dependent Internet Traffic According to Relevant Time Scales  [in Japanese]

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Abstract

インターネットトラヒックには長期依存性ないし自己相似性が広く観察されている.このようなトラヒックを扱うために,これまでにはいくつかの解析モデルが提案されており,その代表的な例がFBM モデルである.しかし,FBM モデルはその扱いやすさの半面,1 つのハーストパラメータで表せるような限られた特性を持つ自己相似性トラヒックのモデル化にのみ有効である.本研究では,待ち行列システムにおけるトラヒックの挙動と深く関わりのある時間スケールに注目し,より一般的な相関特性を持つ長期依存性トラヒックの性能の解析方法について提案する.ここで扱うトラヒックの特性は広域ネットワークやLAN で観測されたトラヒックのデータから抽出したものである.また,実際のトラヒックデータを用いたシミュレーションの結果を使って,提案方式による近似解析の結果との比較,検証を行う.In recent studies, fractional Brownian motion has been proposed to analyze nowadays internet traffic,which are found to be fractal than the classical Poisson-based models. However, due to its simplicity,fractional Brownian motion is only effcient for approximating the performance of a class of exactly self-similar traffic, whose correlation property can be described by a single Hurst parameter.In this paper,we examine the queueing behavior of long-range dependent internet traffic which has a more general correlation property.We propose an analytical method by focusing on the relationship between time scales and queueing performance. The properties of traffic data discussed in this paper are extracted from traffic traces of real networks,such as a wide area backbone network and a LAN.Results produced by simulation using real traffic data are compared with analytical results carried out by our method.

In recent studies, fractional Brownian motion has been proposed to analyze nowadays internet traffic, which are found to be fractal than the classical Poisson-based models. However, due to its simplicity, fractional Brownian motion is only efficient for approximating the performance of a class of exactly self-similar traffic, whose correlation property can be described by a single Hurst parameter. In this paper, we examine the queueing behavior of long-range dependent internet traffic which has a more general correlation property. We propose an analytical method by focusing on the relationship between time scales and queueing performance. The properties of traffic data discussed in this paper are extracted from traffic traces of real networks, such as a wide area backbone network and a LAN. Results produced by simulation using real traffic data are compared with analytical results carried out by our method.

Journal

  • IPSJ journal

    IPSJ journal 45(5), 1399-1408, 2004-05-15

    Information Processing Society of Japan (IPSJ)

References:  24

Cited by:  1

Codes

  • NII Article ID (NAID)
    110002712189
  • NII NACSIS-CAT ID (NCID)
    AN00116647
  • Text Lang
    JPN
  • Article Type
    Journal Article
  • ISSN
    1882-7764
  • NDL Article ID
    6950241
  • NDL Source Classification
    ZM13(科学技術--科学技術一般--データ処理・計算機)
  • NDL Call No.
    Z14-741
  • Data Source
    CJP  CJPref  NDL  NII-ELS  IPSJ 
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