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research-article

Survey of Energy-Cognizant Scheduling Techniques

Published: 01 July 2013 Publication History

Abstract

Execution time is no longer the only metric by which computational systems are judged. In fact, explicitly sacrificing raw performance in exchange for energy savings is becoming a common trend in environments ranging from large server farms attempting to minimize cooling costs to mobile devices trying to prolong battery life. Hardware designers, well aware of these trends, include capabilities like DVFS (to throttle core frequency) into almost all modern systems. However, hardware capabilities on their own are insufficient and must be paired with other logic to decide if, when, and by how much to apply energy-minimizing techniques while still meeting performance goals. One obvious choice is to place this logic into the OS scheduler. This choice is particularly attractive due to the relative simplicity, low cost, and low risk associated with modifying only the scheduler part of the OS. Herein we survey the vast field of research on energy-cognizant schedulers. We discuss scheduling techniques to perform energy-efficient computation. We further explore how the energy-cognizant scheduler's role has been extended beyond simple energy minimization to also include related issues like the avoidance of negative thermal effects as well as addressing asymmetric multicore architectures.

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Published In

cover image IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems  Volume 24, Issue 7
July 2013
212 pages

Publisher

IEEE Press

Publication History

Published: 01 July 2013

Author Tags

  1. Energy consumption
  2. Hardware
  3. Heuristic algorithms
  4. Instruction sets
  5. Processor scheduling
  6. Survey
  7. Thermal management
  8. asymmetric multicore processors
  9. cooperative resource sharing
  10. power-aware scheduling
  11. shared resource contention
  12. thermal effects
  13. thread level scheduling

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  • (2019)Thermal-constrained energy efficient real-time scheduling on multi-core platformsParallel Computing10.1016/j.parco.2019.01.00385:C(231-242)Online publication date: 1-Jul-2019
  • (2018)Cost-Constrained QoS Optimization for Approximate Computation Real-Time Tasks in Heterogeneous MPSoCsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2017.277289637:9(1733-1746)Online publication date: 1-Sep-2018
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