Multiprocessor Online Scheduling of Hard-Real-Time Tasks
IEEE Transactions on Software Engineering
LEneS: task scheduling for low-energy systems using variable supply voltage processors
Proceedings of the 2001 Asia and South Pacific Design Automation Conference
Hard real-time scheduling for low-energy using stochastic data and DVS processors
ISLPED '01 Proceedings of the 2001 international symposium on Low power electronics and design
Real-time dynamic voltage scaling for low-power embedded operating systems
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Timing Analysis for Fixed-Priority Scheduling of Hard Real-Time Systems
IEEE Transactions on Software Engineering
A scheduling model for reduced CPU energy
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
An Integrated Approach for Applying Dynamic Voltage Scaling to Hard Real-Time Systems
RTAS '03 Proceedings of the The 9th IEEE Real-Time and Embedded Technology and Applications Symposium
Practical Voltage-Scaling for Fixed-Priority RT-Systems
RTAS '03 Proceedings of the The 9th IEEE Real-Time and Embedded Technology and Applications Symposium
On energy-optimal voltage scheduling for fixed-priority hard real-time systems
ACM Transactions on Embedded Computing Systems (TECS)
A Dynamic Voltage Scaling Algorithm for Sporadic Tasks
RTSS '03 Proceedings of the 24th IEEE International Real-Time Systems Symposium
Power-Aware Scheduling for Periodic Real-Time Tasks
IEEE Transactions on Computers
Power-Aware Scheduling for AND/OR Graphs in Real-Time Systems
IEEE Transactions on Parallel and Distributed Systems
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With the rapid development of embedded systems, battery life becomes a critical restriction factor. Dynamic voltage scaling (DVS) has been proven to be an effective method for reducing energy consumption of processors. This paper proposes an energy-saving algorithm under a task model (the MSPR model) where a task consists of multiple subtasks with different fixed priorities. This algorithm includes two parts. The first part is a static algorithm, which exploits the relationship among tasks to set the slowdown factors of subtasks. The second part is an algorithm that dynamically reclaims and reuses the slack time of precedent subtasks during the execution of tasks. To the best of our knowledge, this is the first work for energy-efficient scheduling under the complex periodic real-time task model where a task consists of multiple subtasks with different fixed priorities. Experimental results show this method can reduce energy consumption by 20%-80%, while guaranteeing the real-time requirements of systems.