An accurate approach of large-scale IP traffic matrix estimation

被引:14
|
作者
Jiang, Dingde [1 ]
Chen, Jun [2 ]
He, Linbo [2 ]
机构
[1] Univ Elect Sci & Technol China, Key Lab Broadband Opt Fiber Transmiss & Commun Ne, Chengdu 610054, Sichuan, Peoples R China
[2] Chengdu Univ Informat Technol, Dept Network Engn, Chengdu 610225, Sichuan, Peoples R China
关键词
network tomography; traffic matrix; IPFP; Fratar model;
D O I
10.1093/ietcom/e90-b.12.3673
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This letter proposes a novel method of large-scale IP traffic matrix estimation which is based on Partial Flow Measurement and Fratar Model (PFMFM). Firstly, we model OD flows as Fratar model and introduce the constrained relations between traffic matrix and link loads. By combining partial flow measurement, we can get a good prior value of network tomography. Then a good estimation of traffic matrix is attained with the modified network tomography method. Finally, we use the real data [8] from network Abilene to validate our method. In contrast to TomoGravity [1], the results show that our method improves remarkably and the estimation of traffic matrix is closer to real data, and especially when the flow is small and changes dramatically, the estimation is better.
引用
收藏
页码:3673 / 3676
页数:4
相关论文
共 50 条
  • [31] Community Detection in large-scale IP networks by Observing Traffic at Network Boundary
    Jakalan, Ahmad
    Gong, Jian
    Su, Qi
    Hu, Xiaoyan
    WORLD CONGRESS ON ENGINEERING AND COMPUTER SCIENCE, WCECS 2015, VOL I, 2015, : 59 - 64
  • [32] Intelligent Traffic Matrix Estimation Using LevenBerg-Marquardt Artificial Neural Network of Large Scale IP Network
    Hussain, Syed Saiq
    Sultan, Muhammad Arif
    Qazi, Sameer
    Ameer, Mehmood
    2019 13TH INTERNATIONAL CONFERENCE ON MATHEMATICS, ACTUARIAL SCIENCE, COMPUTER SCIENCE AND STATISTICS (MACS-13), 2019,
  • [33] Enabling Efficient and Accurate Large-Scale Simulations of VANETs for Vehicular Traffic Management
    Killat, Moritz
    Schmidt-Eisenlohr, Felix
    Hartenstein, Hannes
    Roessel, Christian
    Vortischt, Peter
    Assenmacher, Silja
    Busch, Fritz
    VANET'07: PROCEEDINGS OF THE FOURTH ACM INTERNATIONAL WORKSHOP ON VEHICULAR AD HOC NETWORKS, 2007, : 29 - 38
  • [34] Large-scale Traffic Data Imputation Using Matrix Completion on Graphs
    Han, Tianyang
    Wada, Kentaro
    Oguchi, Takashi
    2019 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC), 2019, : 2252 - 2258
  • [35] Bayesian hierarchical model for large-scale covariance matrix estimation
    Zhu, Dongxiao
    Hero, Alfred O., III
    JOURNAL OF COMPUTATIONAL BIOLOGY, 2007, 14 (10) : 1311 - 1326
  • [36] Configuration Estimation for Accurate Position Control of Large-Scale Soft Robots
    Hyatt, Phillip
    Kraus, Dustan
    Sherrod, Vallan
    Rupert, Levi
    Day, Nathan
    Killpack, Marc D.
    IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2019, 24 (01) : 88 - 99
  • [37] A Hierarchical Approach for Dynamic Origin-Destination Matrix Estimation on Large-Scale Congested Networks
    Frederix, Rodric
    Viti, Francesco
    Tampere, Chris M. J.
    2011 14TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2011, : 1543 - 1548
  • [38] Arbitrarily Accurate Approximation Scheme for Large-Scale RFID Cardinality Estimation
    Gong, Wei
    Liu, Kebin
    Miao, Xin
    Liu, Haoxiang
    2014 PROCEEDINGS IEEE INFOCOM, 2014, : 477 - 485
  • [39] Efficient Traffic State Estimation for Large-Scale Urban Road Networks
    Kong, Qing-Jie
    Zhao, Qiankun
    Wei, Chao
    Liu, Yuncai
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2013, 14 (01) : 398 - 407
  • [40] AIGC in Urban Traffic: A Paradigm Shift in Large-Scale State Estimation
    Zhao, Danqi
    Qiu, Hanyi
    Xu, Mingxing
    Wang, Liang
    SMART TRANSPORTATION SYSTEMS 2024, KES-STS 2024, 2024, 407 : 143 - 154