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Dynamic power management under the RUN scheduling algorithm: a slack filling approach
Real-Time Systems ( IF 1.3 ) Pub Date : 2021-03-22 , DOI: 10.1007/s11241-021-09367-2
Lais Borin , George Lima , Márcio Castro , Patricia D. M. Plentz

Effective energy-aware scheduling is paramount for current and future multiprocessor real-time systems, being Dynamic Power Management (DPM) one of the employed techniques. In this paper we extend the Reduction to Uniprocessor (RUN) algorithm making it DPM-compliant. RUN is an optimal multiprocessor real-time scheduling for periodic implicit-deadline tasks and it is known to generate low overhead in terms of preemptions and migrations. It is based on an off-line reduction of the target multiprocessor system into one or more uniprocessor systems. On-line scheduling decisions for the latter is then efficiently translated back to the original system. The developed approach in this paper, called Dynamic Slack Filling (DSF-RUN), extends RUN in two aspects. First, the RUN reduction process is adapted to take into account slack entities, properly defined to represent processor spare capacity for DPM management. Second, on-line scheduling rules of RUN are modified so as to generate long time periods in the schedule by dynamically inserting slacks. Our approach has the same off-line complexity of the original RUN algorithm and takes either quadratic or linear on-line complexity in the number of tasks, depending on the strategy used to compute the maximum allowed idle periods. Results obtained with simulations show that our approach consumes less than 10% more energy than an idealized optimum DPM strategy.



中文翻译:

RUN调度算法下的动态电源管理:一种松弛填充方法

对于当前和未来的多处理器实时系统而言,有效的能源感知调度至关重要,它是采用的动态电源管理(DPM)技术之一。在本文中,我们将归约化简化为单处理器(RUN)算法,使其符合DPM。RUN是用于周期性隐式截止任务的最佳多处理器实时调度,并且在抢占和迁移方面产生的开销较低。它基于将目标多处理器系统离线还原为一个或多个单处理器系统的基础。然后将后者的在线调度决策有效地转换回原始系统。本文中开发的方法称为动态松弛填充(DSF-RUN),它从两个方面扩展了RUN。首先,减少RUN的过程要考虑到松弛实体,正确定义以代表DPM管理的处理器备用容量。其次,修改RUN的在线调度规则,以便通过动态插入松弛来在调度中生成较长的时间段。我们的方法具有与原始RUN算法相同的离线复杂度,并且在任务数量上采用二次或线性在线复杂度,具体取决于用于计算最大允许空闲周期的策略。通过仿真获得的结果表明,与理想的最佳DPM策略相比,我们的方法所消耗的能源不到10%。我们的方法具有与原始RUN算法相同的离线复杂度,并且在任务数量上采用二次或线性在线复杂度,具体取决于用于计算最大允许空闲周期的策略。通过仿真获得的结果表明,与理想的最佳DPM策略相比,我们的方法所消耗的能源不到10%。我们的方法具有与原始RUN算法相同的离线复杂度,并且在任务数量上采用二次或线性在线复杂度,具体取决于用于计算最大允许空闲周期的策略。通过仿真获得的结果表明,与理想的最佳DPM策略相比,我们的方法所消耗的能源不到10%。

更新日期:2021-03-22
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