Energy-Efficient Continuous Task Scheduling for Near Real-Time Periodic Tasks

Improving energy efficiency is critical for embedded systems in our rapidly evolving information society. It has been considered that heterogeneous multiprocessors can contribute to improving the energy efficiency of embedded systams. In such processors, executing tasks on lower performance core at lower frequency is better for energy efficiency though higher performance is required for huge tasks to meet their deadlines. To minimize the energy consumption while meeting deadlines strictly, adaptive task scheduling is very important. A drawback of the existing scheduling algorithms is, they assume that the deadline is the same as the input interval. Near real-time data processing, such as multimedia streaming applications, has deadline that is longer than the input interval thanks to buffering. For such applications, the conventional frame-based scheduling cannot realize optimal scheduling due to their shortsighted deadline assumption. To realize globally optimal executions of these applictions, we propose a slackbased continuous task scheduling algorithm, which takes advantage of the long deadline. This algorithm determines an active core and the timing of core swithching based on slack time. We use two types core with different performance and realize continuous execution. We confirmed our approach can take advantage of the longer deadline and reduce the average power consumption by up to 27%.