Data-based statistical models of data networks

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    Machine (Statistical) learning methods are used for predicting the delivery times of the packages transmitted through the data network (DN). The statistical model of the DN is proposed, this model allows predicting the delivery times depending on a state of the DN (network load) and the statistical dependences between the delivery times of different transmitted packages. For constructing this model, various statistical methods (forecasting, dimensionality reduction) are applied to the data which are the results of computational experiments performed with detailed simulation model of the DN. The constructed model simulates the processes of package transmission over the DN. Motivation for a construction of such model is a need to create Monte Carlo network simulators to imitate the delivery times of transmitted packages, such simulators can be used in modeling of Information and Control Systems whose objects communicate with each other through the DN.

    Original languageEnglish
    Title of host publicationProceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages433-438
    Number of pages6
    ISBN (Electronic)9781509002870
    DOIs
    Publication statusPublished - 2 Mar 2016
    EventIEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 - Miami, United States
    Duration: 9 Dec 201511 Dec 2015

    Publication series

    NameProceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015

    Conference

    ConferenceIEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
    Country/TerritoryUnited States
    CityMiami
    Period9/12/1511/12/15

    Keywords

    • Data network simulation
    • Dimensionality reduction
    • Forecasting
    • Information and control systems modeling
    • Statistical learning
    • Surrogate modeling

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