Power minimization for parallel real-time systems with malleable jobs and homogeneous frequencies

In this work, we investigate the potential benefit of parallelization for both meeting real-time constraints and minimizing power consumption. We consider malleable Gang scheduling of implicit-deadline sporadic tasks upon multiprocessors. By extending schedulability criteria for malleable jobs to DPM/DVFS-enabled multiprocessor platforms, we are able to derive an offline polynomial-time optimal processor/frequency-selection algorithm. Simulations of our algorithm on randomly generated task systems executing on platforms having up to 16 processing cores show that the theoretical power consumption is reduced by a factor of 36 compared to the optimal non-parallel approach.