Adaptive Crown Scheduling for Streaming Tasks on Many-Core Systems with Discrete DVFS

We consider temperature-aware, energy-efficient scheduling of streaming applications with parallelizable tasks and throughput requirement on multi-/many-core embedded devices with discrete dynamic voltage and frequency scaling (DVFS). Given the few available discrete frequency levels, we provide the task schedule in a conservative and a relaxed form so that using them adaptively decreases power consumption, i.e. lowers chip temperature, without hurting throughput in the long run. We support our proposal by a toolchain to compute the schedules and evaluate the power reduction with synthetic task sets.