Flow interaction based propagation model and bursty influence behavior analysis of Internet flows

被引:5
|
作者
Wu, Xiao-Yu [1 ,3 ]
Gu, Ren-Tao [1 ,3 ]
Ji, Yue-Feng [2 ,3 ]
机构
[1] Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing Key Lab Network Syst Architecture & Conve, Beijing 100876, Peoples R China
[2] Beijing Univ Posts & Telecommun, State Key Lab Informat Photon & Opt Commun, Beijing 100876, Peoples R China
[3] Beijing Adv Innovat Ctr Future Internet Technol, Beijing, Peoples R China
基金
中国国家自然科学基金; 国家高技术研究发展计划(863计划); 北京市自然科学基金;
关键词
Complex system; Internet; Flow interaction; Influence propagation; COMPLEX NETWORKS; DYNAMICS; CONGESTION; CITATION; FAILURE;
D O I
10.1016/j.physa.2016.06.007
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
QoS (quality of service) fluctuations caused by Internet bursty flows influence the user experience in the Internet, such as the increment of packet loss and transmission time. In this paper, we establish a mathematical model to study the influence propagation behavior of the bursty flow, which is helpful for developing a deep understanding of the network dynamics in the Internet complex system. To intuitively reflect the propagation process, a data flow interaction network with a hierarchical structure is constructed, where the neighbor order is proposed to indicate the neighborhood relationship between the bursty flow and other flows. The influence spreads from the bursty flow to each order of neighbors through flow interactions. As the influence spreads, the bursty flow has negative effects on the odd order neighbors and positive effects on the even order neighbors. The influence intensity of bursty flow decreases sharply between two adjacent orders and the decreasing degree can reach up to dozens of times in the experimental simulation. Moreover, the influence intensity increases significantly when network congestion situation becomes serious, especially for the 1st order neighbors. Network structural factors are considered to make a further study. Simulation results show that the physical network scale expansion can reduce the influence intensity of bursty flow by decreasing the flow distribution density. Furthermore, with the same network scale, the influence intensity in WS small-world networks is 38.18% and 18.40% lower than that in ER random networks and BA scale free networks, respectively, due to a lower interaction probability between flows. These results indicate that the macro-structural changes such as network scales and styles will affect the inner propagation behaviors of the bursty flow. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:341 / 349
页数:9
相关论文
共 50 条
  • [1] Flow and Internet shopping behavior - A conceptual model and research propositions
    Smith, DN
    Sivakumar, K
    JOURNAL OF BUSINESS RESEARCH, 2004, 57 (10) : 1199 - 1208
  • [2] Structural Modeling and Characteristics Analysis of Flow Interaction Networks in the Internet
    Wu Xiao-Yu
    Gu Ren-Tao
    Pan Zhuo-Ya
    Jin Wei-Qi
    Ji Yue-Feng
    CHINESE PHYSICS LETTERS, 2015, 32 (06)
  • [3] Targeted influence maximization under a multifactor-based information propagation model
    Li, Lingfei
    Liu, Yezheng
    Zhou, Qing
    Yang, Wei
    Yuan, Jiahang
    INFORMATION SCIENCES, 2020, 519 : 124 - 140
  • [4] Social content based latent influence propagation model
    Wang Z.-J.
    Wang S.-H.
    Zhang W.-G.
    Huang Q.-M.
    Jisuanji Xuebao, 8 (1528-1540): : 1528 - 1540
  • [5] Internet User Behavior Analysis Based on Big Data
    He, Jiangnan
    Yin, Xiaoyin
    IWCMC 2021: 2021 17TH INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE (IWCMC), 2021, : 432 - 435
  • [6] The heterogeneity of inter-domain Internet application flows: entropic analysis and flow graph modelling
    Yu, Ke
    Li, Frank Y.
    Wu, Xiaofei
    Di, Jiaxi
    TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, 2015, 26 (05): : 760 - 771
  • [7] Langevin model of the flow control in the internet and its phase transition analysis
    Fan Hua
    Li Li
    Yuan Jian
    Shan Xiu-Ming
    ACTA PHYSICA SINICA, 2009, 58 (11) : 7507 - 7513
  • [8] Quantitative Agent Based Model of User Behavior in an Internet Discussion Forum
    Sobkowicz, Pawel
    PLOS ONE, 2013, 8 (12):
  • [9] A Stock Market Model Based on the Interaction of Heterogeneous Traders' Behavior
    Yuan, Ye
    Chen, Xuebo
    Sun, Qiubai
    ADVANCES IN HUMAN FACTORS IN SIMULATION AND MODELING (AHFE 2017), 2018, 591 : 312 - 321
  • [10] The Analysis of the Internet Development Based on the Complex Model of the Discursive Space
    Maciag, Rafal
    INFORMATION, 2018, 9 (01):