A Power-Aware Run-Time System for High-Performance Computing
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Procrastinating voltage scheduling with discrete frequency sets
Proceedings of the conference on Design, automation and test in Europe: Proceedings
Dynamic voltage and frequency management based on variable update intervals for frequency setting
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Energy efficient DVS schedule for fixed-priority real-time systems
ACM Transactions on Embedded Computing Systems (TECS) - Special Section LCTES'05
ISLPED '07 Proceedings of the 2007 international symposium on Low power electronics and design
Minimizing expected energy consumption in real-time systems through dynamic voltage scaling
ACM Transactions on Computer Systems (TOCS)
Journal of Signal Processing Systems
An optimal solution for the heterogeneous multiprocessor single-level voltage-setup problem
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
EUC'07 Proceedings of the 2007 international conference on Embedded and ubiquitous computing
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Energy minimization for real-time systems with (m; k)-guarantee
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Some observations on optimal frequency selection in DVFS-based energy consumption minimization
Journal of Parallel and Distributed Computing
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Voltage scheduling is an essential technique used to exploit the benefit of dynamic voltage-scaling processors. Though extensive research exists in this area, current processor limitations such as time and energy transition overhead and voltage-level discretization are often dismissed as insignificant. We show that for hard real-time applications, disregarding these details can lead to suboptimal or even invalid results. We propose two algorithms to account for these limitations. The first is a greedy approach, while the second is more complex, but can significantly reduce the system's energy consumption. Through experimental results on both real and randomly generated systems, we show the effectiveness of both algorithms and explore what conditions make it beneficial to use the complex algorithm over the basic one.