Harvesting-Aware Power Management for Real-Time Systems With Renewable Energy

In this paper, we propose a harvesting-aware power management algorithm that targets at achieving good energy efficiency and system performance in energy harvesting real-time systems. The proposed algorithm utilizes static and adaptive scheduling techniques combined with dynamic voltage and frequenc...

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Bibliographic Details
Published in:IEEE transactions on very large scale integration (VLSI) systems 2012-08, Vol.20 (8), p.1473-1486
Main Authors: Liu, Shaobo, Lu, Jun, Wu, Qing, Qiu, Qinru
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Batteries
Design. Technologies. Operation analysis. Testing
Dynamic voltage and frequency selection (DVFS)
Dynamical systems
Dynamics
Electric potential
Electronics
embedded system
Energy
Energy dissipation
energy harvest
Energy harvesting
Energy. Thermal use of fuels
Exact sciences and technology
Integrated circuits
Power management
Rational use of energy: conservation and recovery of energy
Real time systems
real-time
Schedules
Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices
Studies
task scheduling
Time measurements
Timing
Very large scale integration
Voltage
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Summary:In this paper, we propose a harvesting-aware power management algorithm that targets at achieving good energy efficiency and system performance in energy harvesting real-time systems. The proposed algorithm utilizes static and adaptive scheduling techniques combined with dynamic voltage and frequency selection to achieve good system performance under timing and energy constraints. In our approach, we simplify the scheduling and optimization problem by separating constraints in timing and energy domains. The proposed algorithm achieves improved system performance by exploiting task slack with dynamic voltage and frequency selection and minimizing the waste on harvested energy. Experimental results show that the proposed algorithm improves the system performance in deadline miss rate and the minimum storage capacity requirement for zero deadline miss rate. Comparing to the existing algorithms, the proposed algorithm achieves better performance in terms of the deadline miss rate and the minimum storage capacity under various settings of workloads and harvested energy profiles.
ISSN:1063-8210
1557-9999
DOI:10.1109/TVLSI.2011.2159820