Conservative Dynamic Energy Management for Real-Time Dataflow Applications Mapped on Multiple Processors

Voltage-frequency scaling (VFS) trades a linear processor slowdown for a potentially quadratic reduction in energy consumption. Complex dependencies may exist between different tasks of an application. The impact of VFS on the end-to-end application performance is difficult to predict, especially when these tasks are mapped on multiple processors that are scaled independently. This is a problem for real-time (RT) applications that require guaranteed end-to-end performance. In this paper we first classify the slack existing in RT applications consisting of multiple dependent tasks mapped on multiple processors independently using VFS, resulting in static, work, and share slack. Then we concentrate on work and share slack as they can only be detected at run time, thus their conservative use is challenging. We propose SlackOS, a dynamic, dependency-aware, task scheduling that conservatively scales the voltage and frequency of each processor, to respect RT deadlines. When applied to a H.264 application, our method delivers 22% to 33% energy reduction, compared to dynamic RT scheduling that is not energy aware.