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Online Workload Forecasting

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Self-Aware Computing Systems

Abstract

This chapter gives a summary of the state-of-the-art approaches from different research fields that can be applied to continuously forecast future developments of time series data streams. More specifically, the input time series data contains continuously monitored metrics that quantify the amount of incoming workload units to a self-aware system. It is the goal of this chapter to identify and present approaches for online workload forecasting that are required for a self-aware system to act proactively—in terms of problem prevention and optimization—inferred from likely changes in their usage. The research fields covered are machine learning and time series analysis. We describe explicit limitations and advantages for each forecasting method.

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Author information

Authors and Affiliations

  1. University of Würzburg, Würzburg, Germany

    Nikolas Herbst & Samuel Kounev

  2. Swinburne University of Technology, Melbourne, Australia

    Ayman Amin

  3. Heidelberg University, Heidelberg, Germany

    Artur Andrzejak

  4. Humboldt-Universität zu Berlin, Berlin, Germany

    Lars Grunske

  5. Carnegie Mellon University, Pittsburgh, CA, USA

    Ole J. Mengshoel & Priya Sundararajan

Authors
  1. Nikolas Herbst
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  2. Ayman Amin
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  3. Artur Andrzejak
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  4. Lars Grunske
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  5. Samuel Kounev
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  6. Ole J. Mengshoel
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  7. Priya Sundararajan
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Correspondence to Nikolas Herbst .

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Editors and Affiliations

  1. University of Würzburg, Würzburg, Germany

    Samuel Kounev

  2. Thomas J. Watson Research Center, Hawthorne, New York, USA

    Jeffrey O. Kephart

  3. University of Würzburg, Würzburg, Germany

    Aleksandar Milenkoski

  4. VMWare Inc.Futurewei Technologies, Inc. , Santa Clara, California, USA

    Xiaoyun Zhu

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Herbst, N. et al. (2017). Online Workload Forecasting. In: Kounev, S., Kephart, J., Milenkoski, A., Zhu, X. (eds) Self-Aware Computing Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-47474-8_18

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  • DOI: https://doi.org/10.1007/978-3-319-47474-8_18

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