Today the demand for internet services is increasing which leads to an exponential growth of internet traffic. In this paper, the Traffic Matrix Estimation (TME) problem is considered. The TME is one of the critical problems to be solved in the area of Software Defined Networks. TME problem can be solved by determining the traffic demand matrix by using load measurements. Traffic matrix estimation involves finding the most informative flows in a network. To accurately estimate the Traffic Matrix (TM) there are different approaches. This article surveys the various approaches for estimating the TM. Each method follows a different procedure to solve the problem of TME. The various methods to solve the traffic matrix estimation problem are considered followed by the comparison of various TM estimation methods. Also, the algorithm used in each of those methods are depicted very clearly for understanding. Finally, the challenges and opportunities were discussed which provides the guideline to go ahead with the TME problem.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Study on Traffic Matrix Estimation Techniques in Software-Defined Networks


    Contributors:
    K, Swetha. (author) / U, Prabu. (author) / G, Angel. (author) / Y, Lahari. (author)


    Publication date :

    2022-12-01


    Size :

    792833 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Control traffic protection in software-defined networks

    Hu, Yannan / Wendong, Wang / Xiangyang, Gong et al. | IEEE | 2014


    Hierarchical Software-Defined Networks for Wide-Area Air Traffic Management Networks

    Izquierdo-Zaragoza, Jose-Luis / Lins, Wolfgang / Leydold, Peter et al. | IEEE | 2019



    Software Defined Satellite Networks

    Gopal, Rajeev / Ravishankar, Channasandra | AIAA | 2014


    Traffic Matrix Estimation Techniques- A Survey on Current Practices

    Swetha., K / Prabu., U / Angel., G et al. | IEEE | 2023