Adaptive scheduling and voltage scaling for multiprocessor real-time applications with non-deterministic workload

The computational workload of some real-time applications varies significantly during runtime, which makes the task scheduling and power management a challenge. One of the major influences to the workload of an application is the selection of conditional branches which may activate or deactivate a large set of operations. Focusing on real-time applications with variable workload which is due to random branch selection, this paper presents a framework of task mapping, scheduling and dynamic voltage and frequency scaling (DVFS) for a multiprocessor system. The proposed framework maintains workload awareness using dynamic profiling of branch probability. The profiled information is utilized by the scheduling and DVFS algorithm that are adopted in this framework to generate statistically optimal solution.