Energy-Efficient Task Partition for Periodic Real-Time Tasks on Platforms with Dual Processing Elements

Modern computing systems often adopt multiple processing elements to enhance the computing capability or reduce the power consumption, especially for embedded systems. Such configurations impose challenges on energy efficiency in hardware and software implementations. This paper targets energy-efficient task partitioning for real-time tasks on a platform with two heterogeneous processing elements (processors), in which each one has its own characteristics on power consumption and job execution. This paper proposes a general framework for different hardware configurations in energy/power consumption. The framework provides a fully polynomial-time approximation scheme (FPTAS) to derive a solution with energy consumption very close to the optimal energy consumption in tolerable time/space complexity. Experimental results reveal that the proposed framework is effective in energy efficiency.