Adaptive Real-Time Scheduling for Soft Tasks with Varying Execution Times

As real-time embedded systems get more diverse and more complicated, systems with different types of tasks (e.g., periodic tasks and aperiodic tasks) are prevailing. In such a system, it is important that schedulability of periodic tasks is guaranteed and at the same time response times to aperiodic requests are short enough. Total Bandwidth Server is one of convincing task scheduling algorithms for mixed task sets of periodic and aperiodic tasks. Considering a fact that in most cases tasks' execution times are much shorter than their worst-case execution times, this paper proposes a method of reducing response times of aperiodic execution by using predictive execution times instead of worst-case execution times for deadline calculations in the total bandwidth server to obtain shorter deadlines, while ensuring the integrity of periodic tasks. In the evaluation by simulation, the proposed method combined with a resource reclaiming technique improved average response times for aperiodic tasks, by up to 22% compared with the original total bandwidth server technique, and by up to 48% compared with Constant Bandwidth Server, which is another algorithm appropriate for tasks with varying execution times