Abstract-The designs of heterogeneous multi-core multiprocessor real-time systems are evolving for higher energy efficiency at the cost of increased heat density. This adversely effects the reliability and performance of the real-time systems. Moreover, the partitioning of periodic real-time tasks based on their worst case execution time can lead to significant energy wastage. In this paper, we investigate adaptive energy-efficient task partitioning for heterogeneous multi-core multiprocessor realtime systems. We use a power model which incorporates the impact of temperature and voltage of a processor on its static power consumption. Two different thermal models are used to estimate the peak temperature of a processor. We develop two feedback-based optimization and control approaches for adaptively partitioning real-time tasks according to their actual utilizations. Simulation results show that the proposed approaches are effective in minimizing the energy consumption and reducing the number of task migrations.
REF has proposed an adaptive task partitioning for multi-core systems running independent periodic real-time tasks.
Adaptive energy-efficient task partitioning for heterogeneous multi-core multiprocessor real-time systems
2012 International Conference on High Performance Computing & Simulation (HPCS)
2012 International Conference on High Performance Computing & Simulation (HPCS)
Computer Science