Scheduling Moldable Parallel Streaming Tasks on Heterogeneous Platforms with Frequency Scaling

We extend static scheduling of parallelizable tasks to machines with multiple core types, taking differences in performance and power consumption due to task type into account. Next to energy minimization for given deadline, i.e. for given throughput requirement, we consider makespan minimization for given energy or average power budgets. We evaluate our approach by comparing schedules of synthetic task sets for big.LITTLE with other schedulers from literature. We achieve an improvement of up to 33%.