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Characterization of Different User Behaviors for Demand Response in Data Centers

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Euro-Par 2022: Parallel Processing (Euro-Par 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13440))

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Abstract

Digital technologies are becoming ubiquitous while their impact increases. A growing part of this impact happens far away from the end users, in networks or data centers, contributing to a rebound effect. A solution for a more responsible use is therefore to involve the user. As a first step in this quest, this work considers the users of a data center and characterizes their contribution to curtail the computing load for a short period of time by solely changing their job submission behavior.

The contributions are: (i) an open-source plugin for the simulator Batsim to simulate users based on real data; (ii) the exploration of four types of user behaviors to curtail the load during a time window, namely delaying, degrading, reconfiguring or renouncing their job submissions. We study the impact of these behaviors on four different metrics: the energy consumed during and after the time window, the mean waiting time and the mean slowdown. We also characterize the conditions under which the involvement of users is the most beneficial.

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Notes

  1. 1.

    Batsim: https://batsim.org/.

  2. 2.

    SimGrid: https://simgrid.org with the energy plugin https://simgrid.org/doc/latest/Plugins.html?highlight=energy#host-energy.

  3. 3.

    Batmen repository: https://gitlab.irit.fr/sepia-pub/mael/batmen.

  4. 4.

    Experiments repository: https://gitlab.irit.fr/sepia-pub/open-science/demand-response-user/-/tree/europar2022.

  5. 5.

    METACENTRUM-2013-3.swf available at https://www.cs.huji.ac.il/labs/parallel/workload/l_metacentrum2/index.html.

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Acknowledgements and Data Availability Statement.

Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr). The scripts and instructions necessary to reproduce and analyze our result are available in a Figshare repository [15].

This work was partly supported by the French Research Agency under the project Energumen (ANR-18-CE25-0008) and DataZero2 (ANR-19-CE25-0016).

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Authors and Affiliations

  1. IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3, Toulouse, France

    Maël Madon, Georges Da Costa & Jean-Marc Pierson

Authors
  1. Maël Madon
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  2. Georges Da Costa
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  3. Jean-Marc Pierson
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Corresponding author

Correspondence to Maël Madon .

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Editors and Affiliations

  1. University of Glasgow, Glasgow, UK

    José Cano

  2. University of Glasgow, Glasgow, UK

    Phil Trinder

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Madon, M., Da Costa, G., Pierson, JM. (2022). Characterization of Different User Behaviors for Demand Response in Data Centers. In: Cano, J., Trinder, P. (eds) Euro-Par 2022: Parallel Processing. Euro-Par 2022. Lecture Notes in Computer Science, vol 13440. Springer, Cham. https://doi.org/10.1007/978-3-031-12597-3_4

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  • DOI: https://doi.org/10.1007/978-3-031-12597-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12596-6

  • Online ISBN: 978-3-031-12597-3

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