Near real-time periodic tasks, which are popular in multimedia streaming applications, have deadline periods that are longer than the input intervals thanks to buffering. For such applications, the conventional frame-based schedulings cannot realize the optimal scheduling due to their shortsighted deadline assumptions. To realize globally energy-efficient executions of these applications, we propose a novel task scheduling algorithm, which takes advantage of the long deadline period. We confirm our approach can take advantage of the longer deadline period and reduce the average power consumption by up to 18%.

Improving energy efficiency is critical for embedded systems in our rapidly evolving information society. Near real-time data processing tasks, such as multimedia streaming applications, exhibit a common fact that their deadline periods are longer than their input intervals due to buffering. In general, executing tasks at lower performance is more energy efficient. On the other hand, higher performance is necessary for huge tasks to meet their deadlines. To minimize the energy consumption while meeting deadlines strictly, adaptive task scheduling including dynamic performance mode selection is very important. In this work, we propose an energy efficient slack-based task scheduling algorithm for such tasks by adapting to task size variations and applying DVFS with the help of statistical analysis. We confirmed that our proposal can further reduce the energy consumption when compared to oracle frame-based scheduling.