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Easily implementable time series forecasting techniques for resource provisioning in cloud computing

Author

Listed:
  • Michel Fliess

    (LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau] - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique, AL.I.E.N. - ALgèbre pour Identification & Estimation Numériques)

  • Cédric Join

    (CRAN - Centre de Recherche en Automatique de Nancy - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique, AL.I.E.N. - ALgèbre pour Identification & Estimation Numériques)

  • Maria Bekcheva

    (Inagral, L2S - Laboratoire des signaux et systèmes - UP11 - Université Paris-Sud - Paris 11 - CentraleSupélec - CNRS - Centre National de la Recherche Scientifique)

  • Alireza Moradi

    (Inagral)

  • Hugues Mounier

    (L2S - Laboratoire des signaux et systèmes - UP11 - Université Paris-Sud - Paris 11 - CentraleSupélec - CNRS - Centre National de la Recherche Scientifique)

Abstract

Workload predictions in cloud computing is obviously an important topic. Most of the existing publications employ various time series techniques, that might be difficult to implement. We suggest here another route, which has already been successfully used in financial engineering and photovoltaic energy. No mathematical modeling and machine learning procedures are needed. Our computer simulations via realistic data, which are quite convincing, show that a setting mixing algebraic estimation techniques and the daily seasonality behaves much better. An application to the computing resource allocation, via virtual machines, is sketched out.

Suggested Citation

  • Michel Fliess & Cédric Join & Maria Bekcheva & Alireza Moradi & Hugues Mounier, 2019. "Easily implementable time series forecasting techniques for resource provisioning in cloud computing," Post-Print hal-02024835, HAL.
    • Handle: RePEc:hal:journl:hal-02024835
    DOI: 10.1109/codit.2019.8820396
    Note: View the original document on HAL open archive server: https://polytechnique.hal.science/hal-02024835v3
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    References listed on IDEAS

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    1. Hassane Abouaïssa & Michel Fliess & Cédric Join, 2016. "On short-term traffic flow forecasting and its reliability," Post-Print hal-01275311, HAL.
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    More about this item

    Keywords

    Cloud computing; computing resources; virtual machines; forecasting; time series; nonstandard analysis; trend; quick fluctuation; machine learning; estimation; seasonality;
    All these keywords.

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