Power conscious fixed priority scheduling for hard real-time systems
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
Journal of the ACM (JACM)
Battery-aware static scheduling for distributed real-time embedded systems
Proceedings of the 38th annual Design Automation Conference
Energy efficient fixed-priority scheduling for real-time systems on variable voltage processors
Proceedings of the 38th annual Design Automation Conference
Real-time dynamic voltage scaling for low-power embedded operating systems
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Proceedings of the 2000 IEEE/ACM international conference on Computer-aided design
Power optimization of real-time embedded systems on variable speed processors
Proceedings of the 2000 IEEE/ACM international conference on Computer-aided design
Performance Comparison of Dynamic Voltage Scaling Algorithms for Hard Real-Time Systems
RTAS '02 Proceedings of the Eighth IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'02)
Instruction Level Power Analysis and Optimization of Software
VLSID '96 Proceedings of the 9th International Conference on VLSI Design: VLSI in Mobile Communication
Power optimization of variable-voltage core-based systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Energy-aware preemptive scheduling algorithm for sporadic tasks on DVS platform
Microprocessors & Microsystems
Hi-index | 0.00 |
As the quantity and functional complexity of battery powered portable devices continues to rise, energy efficient design of such devices has become increasingly important. Many real-time scheduling algorithms have been developed recently to reduce energy consumption in hard real-time embedded systems that use dynamic voltage scaling (DVS) capable processors. This paper explores an algorithm that seeks to reduce energy consumption by considering tasks in tandem, with the intuition that what may be a good frequency for one task, may be much worse for another. In particular, our algorithm considers pairs of tasks, and optimizes them simultaneously so that their total energy consumption is minimized while all deadlines are met. Experimental results demonstrate that our method is able to effectively improve on the results of look-ahead EDF, one of the best energy-aware schedulers, especially for task sets with moderate utilization, and "harmonious" task periodicity.