Intraprogram dynamic voltage scaling: Bounding opportunities with analytic modeling
ACM Transactions on Architecture and Code Optimization (TACO)
Efficient behavior-driven runtime dynamic voltage scaling policies
CODES+ISSS '05 Proceedings of the 3rd IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
Energy aware kernel for hard real-time systems
Proceedings of the 2005 international conference on Compilers, architectures and synthesis for embedded systems
Combining compiler and operating system support for energy efficient I/O on embedded platforms
SCOPES '05 Proceedings of the 2005 workshop on Software and compilers for embedded systems
A DVS-assisted hard real-time I/O device scheduling algorithm
Real-Time Systems
Design of a hard real-time multi-core testbed for energy measurement
Microelectronics Journal
Reducing system level power consumption for mobile and embedded platforms
ARCS'05 Proceedings of the 18th international conference on Architecture of Computing Systems conference on Systems Aspects in Organic and Pervasive Computing
Functional-Level Energy Characterization of µC/OS-II and Cache Locking for Energy Saving
Bell Labs Technical Journal
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Dynamic voltage scaling (DVS) is being increasingly used for power management in embedded systems. Energy is a scarce resource in embedded real-time systems and energyconsumption must be carefully balanced against real-time responsiveness. We describe our experiences in implementing an energy driven task scheduler in RT-Linux. We attempt to minimize the energy consumed by a taskset while guaranteeing that all task deadlines are met. Our algorithm, which we call LEDF, follows a greedy approach and schedules as many tasks as possible at a low CPU speed in a power-aware manner. We present simulation results onenergy savings using LEDF, and we validate our approach using the RT-Linux testbed on the AMD Athlon 4 processor. Power measurements taken on the testbed closely match the power estimates obtained using simulation. Our results show that DVS results in significant energy savings for practical real-life task sets. We also show that when CPU speeds are restricted to only a few discrete values, this approach saves more energy than currently existing methods.