Review on Internet Traffic Sharing Using Markov Chain Model in Computer Network

被引:1
|
作者
More, Sarla [1 ]
Shukla, Diwakar [1 ]
机构
[1] Dr Hari Singh Gour Vishwavidyalaya, Dept Comp Sci & Applicat, Sagar, Madhya Pradesh, India
来源
关键词
Internet traffic sharing; Markov chain model; ICT; Big data; Stochastic processes; Sampling techniques; Quality of service;
D O I
10.1007/978-981-10-7641-1_7
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The Internet traffic sharing is one of the major concerns in the dynamic field of Information and communications technology (ICT). In this scenario the concept of Big Data arises which defines the unstructured nature of data so that there is a strong need of efficient techniques to tackle this heterogeneous type of environment. Many things are dependent on Internet today, and a person has a lot of work to be done with the help of Internet. Due to this problems arise like congestion, disconnectivity, non-connectivity, call drop, and cyber crime. This review study is for the analysis purpose of all this type of problems. Various kinds of methods are discussed based upon the problem formation of Internet access and their respected solutions are discovered with the help of Markov chain model. This model is used to study about how the quality of service is obtained and the traffic share is distributed among the operators on the basis of state probability, share loss analysis, call-by-call attempt, two-call attempt, two market, disconnectivity, index, iso-share curve, elasticity, cyber crime, re-attempt, least square curve fitting, bounded area, area estimation and computation, Rest state, and multi-operator environment.
引用
收藏
页码:81 / 98
页数:18
相关论文
共 50 条
  • [41] A Prediction Model Based on Neural Network and Fuzzy Markov Chain
    Liu, Jia
    Li, Shunxiang
    Jia, Shusheng
    2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23, 2008, : 790 - +
  • [42] Generation model of dynamic network topology based on Markov chain
    Ma Biao
    Proceedings of 2005 Chinese Control and Decision Conference, Vols 1 and 2, 2005, : 1451 - +
  • [43] Markov Chain Model for the Decoding Probability of Sparse Network Coding
    Garrido, Pablo
    Lucani, Daniel E.
    Aguero, Ramon
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2017, 65 (04) : 1675 - 1685
  • [44] Sharing nonlocality in a network using the quantum violation of chain network inequality
    Kumar, Rahul
    Pan, A. K.
    QUANTUM STUDIES-MATHEMATICS AND FOUNDATIONS, 2023, 10 (03) : 353 - 372
  • [45] Sharing nonlocality in a network using the quantum violation of chain network inequality
    Rahul Kumar
    A. K. Pan
    Quantum Studies: Mathematics and Foundations, 2023, 10 : 353 - 372
  • [46] Hybrid Deep Neural Network - Hidden Markov Model Based Network Traffic Classification
    Tan, Xincheng
    Xie, Yi
    COMMUNICATIONS AND NETWORKING, CHINACOM 2018, 2019, 262 : 604 - 614
  • [47] Multi-scale Internet Traffic Prediction Using Wavelet Neural Network Combined Model
    Chen Di
    Feng Hai-liang
    Lin Qing-jia
    Chen Chun-xiao
    2006 FIRST INTERNATIONAL CONFERENCE ON COMMUNICATIONS AND NETWORKING IN CHINA, 2006,
  • [48] USING THE WORLDWIDE COMPUTER NETWORK, INTERNET, IN CHEMICAL SCIENCES
    EDVARDSEN, O
    ACTA CHEMICA SCANDINAVICA, 1995, 49 (05): : 344 - 350
  • [49] A novel networked traffic parameter forecasting method based on Markov chain model
    Hu, JM
    Song, JY
    Yu, GQ
    Zhang, Y
    2003 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS 1-5, CONFERENCE PROCEEDINGS, 2003, : 3595 - 3600
  • [50] A scene-based generalized Markov chain model for VBR video traffic
    Chiruvolu, G
    Das, TK
    Sankar, R
    Ranganathan, N
    ICC 98 - 1998 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS VOLS 1-3, 1998, : 554 - 558