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Energy-aware task scheduling for near real-time periodic tasks on heterogeneous multicore processors

机译:能源感知任务调度,用于异构多​​核处理器上的近实时周期性任务

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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 scheduling cannot realize optimal scheduling due to their shortsighted deadline assumption. 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 65%.
机译:在多媒体流应用程序中流行的近实时周期性任务由于缓冲而具有比输入间隔更长的截止期限。对于这样的应用,传统的基于帧的调度由于其短视的截止期限假设而不能实现最佳调度。为了实现这些应用程序的全局节能执行,我们提出了一种新颖的任务调度算法,该算法利用了较长的截止期限。我们确认我们的方法可以利用更长的截止期限,并将平均功耗降低多达65%。

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