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Scheduling Data Gathering with Maximum Lateness Objective

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Parallel Processing and Applied Mathematics (PPAM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10778))

Abstract

In this paper, scheduling in a star data gathering network is studied. The worker nodes of the network produce datasets that have to be gathered by a single base station. The datasets may be released at different moments. Each dataset is assigned a due date by which it should arrive at the base station. The scheduling problem is to organize the communication in the network so that the maximum dataset lateness is minimized. As this problem is strongly NP-hard, we propose a heuristic algorithm for solving it. The performance of the algorithm is evaluated on the basis of computational experiments.

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Acknowledgements

This research was partially supported by the National Science Centre, Poland, grant 2016/23/D/ST6/00410.

Author information

Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, Adam Mickiewicz University in Poznań, Umultowska 87, 61-614, Poznań, Poland

    Joanna Berlińska

Authors
  1. Joanna Berlińska
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Correspondence to Joanna Berlińska .

Editor information

Editors and Affiliations

  1. Czestochowa University of Technology, Czestochowa, Poland

    Roman Wyrzykowski

  2. University of Tennessee, Knoxville, Tennessee, USA

    Jack Dongarra

  3. University of Southern California, Marina Del Rey, California, USA

    Ewa Deelman

  4. Czestochowa University of Technology, Czestochowa, Poland

    Konrad Karczewski

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Berlińska, J. (2018). Scheduling Data Gathering with Maximum Lateness Objective. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2017. Lecture Notes in Computer Science(), vol 10778. Springer, Cham. https://doi.org/10.1007/978-3-319-78054-2_13

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  • DOI: https://doi.org/10.1007/978-3-319-78054-2_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78053-5

  • Online ISBN: 978-3-319-78054-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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