Energy-Aware Algorithms for Task Graph Scheduling, Replica Placement and Checkpoint Strategies

  • Guillaume AupyEmail author
  • Anne Benoit
  • Paul Renaud-Goud
  • Yves Robert


The energy consumption of computational platforms has recently become a critical problem, both for economic and environmental reasons. To reduce energy consumption, processors can run at different speeds. Faster speeds allow for a faster execution, but they also lead to a much higher (superlinear) power consumption. Energy-aware scheduling aims at minimizing the energy consumed during the execution of the target application, both for computations and for communications. The price to pay for a lower energy consumption usually is a much larger execution time, so the energy-aware approach makes better sense when coupled with some prescribed performance bound. In other words, we have a bi-criteria optimization problem, with one objective being energy minimization, and the other being performance-related.


Power Consumption Execution Time Task Graph Incremental Model Dynamic Voltage Scaling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported in part by the ANR RESCUE project.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Guillaume Aupy
    • 1
    Email author
  • Anne Benoit
    • 1
    • 2
  • Paul Renaud-Goud
    • 3
  • Yves Robert
    • 1
    • 2
    • 4
  1. 1.LIP, Ecole Normale Supérieure de LyonLyonFrance
  2. 2.Institut Universitaire de FranceParisFrance
  3. 3.Chalmers University of technologyGothenburgSweden
  4. 4.University Tennessee KnoxvilleKnoxvilleUSA

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