A Statistical Analysis of Network Parameters for the Self-management of Lambda-Connections

Tiago Fioreze, L. Granville, R. Sadre, Aiko Pras

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

    5 Citations (Scopus)
    48 Downloads (Pure)

    Abstract

    Network monitoring plays an important role in network management. Through the analysis of network parameters (e.g., flow throughput), managers can observe network behavior and make decisions based on them. The choice of network parameters although should be relevant for each specific objective. In this paper, we focus on the analysis of network parameters that are relevant for our self-management of lambda-connections proposal. This proposal consists of an automatic decision process to offload large IP flows onto lambda-connections. This paper aims at statistically analyzing a list of potential network parameters as relevant estimators for flow volume. The main contribution of this work is the introduction of a statistical methodology to validate that some few network parameters can be considered as good predictors for flow volume. These predictors are therefore of great interest to be used in our automatic decision process.
    Original languageUndefined
    Title of host publicationProceedings of the 3rd International Conference on Autonomous Infrastructure, Management and Security (AIMS 2009)
    Place of PublicationBerlin
    PublisherSpringer
    Pages15-27
    Number of pages13
    ISBN (Print)978-3-642-02626-3
    DOIs
    Publication statusPublished - 19 Jun 2009
    Event3rd International Conference on Autonomous Infrastructure, Management and Security, AIMS 2009 - Enschede, Netherlands
    Duration: 30 Jun 20092 Jul 2009
    Conference number: 3

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    Volume5637
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference3rd International Conference on Autonomous Infrastructure, Management and Security, AIMS 2009
    Abbreviated titleAIMS 2009
    CountryNetherlands
    CityEnschede
    Period30/06/092/07/09

    Keywords

    • EWI-15470
    • IR-65531
    • METIS-263892

    Cite this

    Fioreze, T., Granville, L., Sadre, R., & Pras, A. (2009). A Statistical Analysis of Network Parameters for the Self-management of Lambda-Connections. In Proceedings of the 3rd International Conference on Autonomous Infrastructure, Management and Security (AIMS 2009) (pp. 15-27). [10.1007/978-3-642-02627-0_2] (Lecture Notes in Computer Science; Vol. 5637). Berlin: Springer. https://doi.org/10.1007/978-3-642-02627-0_2
    Fioreze, Tiago ; Granville, L. ; Sadre, R. ; Pras, Aiko. / A Statistical Analysis of Network Parameters for the Self-management of Lambda-Connections. Proceedings of the 3rd International Conference on Autonomous Infrastructure, Management and Security (AIMS 2009). Berlin : Springer, 2009. pp. 15-27 (Lecture Notes in Computer Science).
    @inproceedings{ac84e558c92745a2987ec82c77e8c5e9,
    title = "A Statistical Analysis of Network Parameters for the Self-management of Lambda-Connections",
    abstract = "Network monitoring plays an important role in network management. Through the analysis of network parameters (e.g., flow throughput), managers can observe network behavior and make decisions based on them. The choice of network parameters although should be relevant for each specific objective. In this paper, we focus on the analysis of network parameters that are relevant for our self-management of lambda-connections proposal. This proposal consists of an automatic decision process to offload large IP flows onto lambda-connections. This paper aims at statistically analyzing a list of potential network parameters as relevant estimators for flow volume. The main contribution of this work is the introduction of a statistical methodology to validate that some few network parameters can be considered as good predictors for flow volume. These predictors are therefore of great interest to be used in our automatic decision process.",
    keywords = "EWI-15470, IR-65531, METIS-263892",
    author = "Tiago Fioreze and L. Granville and R. Sadre and Aiko Pras",
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    year = "2009",
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    isbn = "978-3-642-02626-3",
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    Fioreze, T, Granville, L, Sadre, R & Pras, A 2009, A Statistical Analysis of Network Parameters for the Self-management of Lambda-Connections. in Proceedings of the 3rd International Conference on Autonomous Infrastructure, Management and Security (AIMS 2009)., 10.1007/978-3-642-02627-0_2, Lecture Notes in Computer Science, vol. 5637, Springer, Berlin, pp. 15-27, 3rd International Conference on Autonomous Infrastructure, Management and Security, AIMS 2009, Enschede, Netherlands, 30/06/09. https://doi.org/10.1007/978-3-642-02627-0_2

    A Statistical Analysis of Network Parameters for the Self-management of Lambda-Connections. / Fioreze, Tiago; Granville, L.; Sadre, R.; Pras, Aiko.

    Proceedings of the 3rd International Conference on Autonomous Infrastructure, Management and Security (AIMS 2009). Berlin : Springer, 2009. p. 15-27 10.1007/978-3-642-02627-0_2 (Lecture Notes in Computer Science; Vol. 5637).

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

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    T1 - A Statistical Analysis of Network Parameters for the Self-management of Lambda-Connections

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    N2 - Network monitoring plays an important role in network management. Through the analysis of network parameters (e.g., flow throughput), managers can observe network behavior and make decisions based on them. The choice of network parameters although should be relevant for each specific objective. In this paper, we focus on the analysis of network parameters that are relevant for our self-management of lambda-connections proposal. This proposal consists of an automatic decision process to offload large IP flows onto lambda-connections. This paper aims at statistically analyzing a list of potential network parameters as relevant estimators for flow volume. The main contribution of this work is the introduction of a statistical methodology to validate that some few network parameters can be considered as good predictors for flow volume. These predictors are therefore of great interest to be used in our automatic decision process.

    AB - Network monitoring plays an important role in network management. Through the analysis of network parameters (e.g., flow throughput), managers can observe network behavior and make decisions based on them. The choice of network parameters although should be relevant for each specific objective. In this paper, we focus on the analysis of network parameters that are relevant for our self-management of lambda-connections proposal. This proposal consists of an automatic decision process to offload large IP flows onto lambda-connections. This paper aims at statistically analyzing a list of potential network parameters as relevant estimators for flow volume. The main contribution of this work is the introduction of a statistical methodology to validate that some few network parameters can be considered as good predictors for flow volume. These predictors are therefore of great interest to be used in our automatic decision process.

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    Fioreze T, Granville L, Sadre R, Pras A. A Statistical Analysis of Network Parameters for the Self-management of Lambda-Connections. In Proceedings of the 3rd International Conference on Autonomous Infrastructure, Management and Security (AIMS 2009). Berlin: Springer. 2009. p. 15-27. 10.1007/978-3-642-02627-0_2. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-02627-0_2