Online task-scheduling for fault-tolerant low-energy real-time systems

In this paper we investigate fault tolerance and Dynamic Voltage Scaling (DVS) in hard real time systems. We present two low-complexity fault-aware scheduling algorithms that combine feasibility analysis of Rate Monotonic Algorithm (RMA) schedules and DVS-based frequency scaling using exact characte...

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Vydané v:International Conference on Computer Aided Design: Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design : San Jose, California; 05-09 Nov. 2006 s. 522 - 527
Hlavní autori: Wei, Tongquan, Mishra, Piyush, Wu, Kaijie, Liang, Han
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: 05.11.2006
ISBN:1595933891, 9781595933898
ISSN:1092-3152
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Abstract In this paper we investigate fault tolerance and Dynamic Voltage Scaling (DVS) in hard real time systems. We present two low-complexity fault-aware scheduling algorithms that combine feasibility analysis of Rate Monotonic Algorithm (RMA) schedules and DVS-based frequency scaling using exact characterization of RMA algorithm. These algorithms lay the foundation for highly efficient online schemes that minimize energy consumption by adapting DVS policies to runtime behavior of tasks and fault occurrences without violating the offline feasibility analysis. Simulation results demonstrate energy savings of up to 60% over low-energy offline scheduling algorithms [22].
AbstractList In this paper we investigate fault tolerance and Dynamic Voltage Scaling (DVS) in hard real time systems. We present two low-complexity fault-aware scheduling algorithms that combine feasibility analysis of Rate Monotonic Algorithm (RMA) schedules and DVS-based frequency scaling using exact characterization of RMA algorithm. These algorithms lay the foundation for highly efficient online schemes that minimize energy consumption by adapting DVS policies to runtime behavior of tasks and fault occurrences without violating the offline feasibility analysis. Simulation results demonstrate energy savings of up to 60% over low-energy offline scheduling algorithms [22].
Author Wei, Tongquan
Liang, Han
Mishra, Piyush
Wu, Kaijie
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