Traffic characterization for traffic engineering purposes: Analysis of funet data

被引:0
|
作者
Juva, I [1 ]
Susitaival, R [1 ]
Peuhkuri, M [1 ]
Aalto, S [1 ]
机构
[1] Aalto Univ, Networking Lab, Helsinki, Finland
关键词
D O I
暂无
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
For Internet traffic engineering purposes, it is important to characterize traffic volumes typically over 5-minute intervals. Based on measurements made in a local network at Lucent in winter 1999, Cao et al. [2] proposed a moving IID Gaussian model for the characterization of 5-minute traffic volumes, with a power-law relationship between the mean and the variance. In this paper we analyze novel measurements gathered from a 2.5 Gbps link in the Finnish university network (Funet) in summer 2004. We investigate the validity of the moving IID Gaussian model and the proposed mean-variance relationship when the measurement interval is varying from 1 second to 5 minutes. As a result, we find that the Gaussian assumption is much more justified with current core link rates. The mean-variance relationship seems, indeed, to follow a power-law with exponent approximately equal to 1.3 in our data set. However, the IID assumption concerning the standardized residual is not verified, but we find a clear positive correlation between adjacent 5-minute volumes, and only slightly weaker negative correlation for traffic volumes with distance 20-30 minutes. In addition, we demonstrate that the same phenomenon is already prevailing in the Lucent data set.
引用
收藏
页码:404 / 411
页数:8
相关论文
共 50 条
  • [21] Fractal traffic model for Internet traffic engineering
    Ryu, B
    Lowen, SB
    MODELING AND SIMULATION ENVIRONMENT FOR SATELLITE AND TERRESTRIAL COMMUNICATIONS NETWORKS, PROCEEDINGS, 2002, 645 : 65 - 103
  • [22] MEDIA VIEWS OF THE TRAFFIC ENGINEER AND TRAFFIC ENGINEERING
    THOMPSON, S
    ITE JOURNAL-INSTITUTE OF TRANSPORTATION ENGINEERS, 1985, 55 (05): : 17 - 20
  • [23] Analysis of Traffic Engineering capabilities for SDN-based Data Center Networks
    Pilimon, Artur
    Kentis, Angelos Mimidis
    Ruepp, Sarah
    Dittmann, Lars
    2018 FIFTH INTERNATIONAL CONFERENCE ON SOFTWARE DEFINED SYSTEMS (SDS), 2018, : 211 - 216
  • [24] Implications of interdomain traffic characteristics on traffic engineering
    Uhlig, S
    Bonaventure, O
    EUROPEAN TRANSACTIONS ON TELECOMMUNICATIONS, 2002, 13 (01): : 23 - 32
  • [25] Academic influence analysis of Journal of Traffic and Transportation Engineering based on big data
    Zhao Z.-H.
    Ren L.
    Dai J.
    Wu M.-Q.
    Jiaotong Yunshu Gongcheng Xuebao/Journal of Traffic and Transportation Engineering, 2022, 22 (04): : 408 - 420
  • [27] Detection of Potential Traffic Jam Based on Traffic Characteristic Data Analysis
    Amelia, Anasthasia
    Saptawati, G. A. Putri
    2014 INTERNATIONAL CONFERENCE ON DATA AND SOFTWARE ENGINEERING (ICODSE), 2014,
  • [28] Extraction and analysis of microscopic traffic data in disordered heterogeneous traffic conditions
    Amrutsamanvar, R. B.
    Muthurajan, B. R.
    Vanajakshi, L. D.
    TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH, 2021, 13 (01): : 1 - 20
  • [29] Analysis of MPLS based Traffic Engineering solution
    Ayyangar, A
    Sidhu, D
    JOINT 4TH IEEE INTERNATIONAL CONFERENCE ON ATM (ICATM'01) AND HIGH SPEED INTELLIGENT INTERNET SYMPOSIUM, 2001, : 21 - 27
  • [30] Data center traffic engineering using Markov approximation
    Hirata, Kouji
    Yamamoto, Miki
    2017 31ST INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN), 2017, : 173 - 178