Issue No. 07 - July (2013 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2012.20
Sergey Zhuravlev , Simon Fraser University, Burnaby
Juan Carlos Saez , Complutense University of Madrid, Madrid
Sergey Blagodurov , Simon Fraser University, Burnaby
Alexandra Fedorova , Simon Fraser University, Burnaby
Manuel Prieto , Complutense University of Madrid, Madrid
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.
Thermal management, Energy consumption, Heuristic algorithms, Processor scheduling, Hardware, Instruction sets, cooperative resource sharing, Survey, shared resource contention, thread level scheduling, power-aware scheduling, thermal effects, asymmetric multicore processors
S. Blagodurov, A. Fedorova, S. Zhuravlev, J. C. Saez and M. Prieto, "Survey of Energy-Cognizant Scheduling Techniques," in IEEE Transactions on Parallel & Distributed Systems, vol. 24, no. , pp. 1447-1464, 2013.