An adaptive energy-efficient task scheduling under execution time variation based on statistical analysis
Takashi Nakada, Tomoki Hatanaka, Hiroshi Nakamura, Hiroshi Ueki, Masanori Hayashikoshi, Toru Shimizu · 2016
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. Therefore, it is possible to minimize their energy consumption without deadline violations. 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.