Optimizing Energy in Non-Preemptive Mixed-Criticality Scheduling by Exploiting Probabilistic Information
Optimizing Energy in Non-Preemptive Mixed-Criticality Scheduling by Exploiting Probabilistic Information
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DOI：
10.1109/tcad.2020.3012231
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发表时间：
2020-11
影响因子：
2.9
通讯作者：
Ashikahmed Bhuiyan;F. Reghenzani;W. Fornaciari;Zhishan Guo
中科院分区：
文献类型：
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作者：
Ashikahmed Bhuiyan;F. Reghenzani;W. Fornaciari;Zhishan Guo
对时序正确性的严格要求使得实时系统的建模和分析偏向于最坏情况的性能。然而，这种对最坏情况的关注并不能提供足够的信息来有效引导资源/能源优化。在本文中，我们将基于概率的能量预测策略与混合关键性任务的精确调度相结合，其中所有场景下的所有任务都必须满足时序正确性。动态电压和频率调节（DVFS）应用于这种精确的调度策略，以实现能量最小化。我们提出了一种概率技术来获得节能的速度（对于处理器而言），从而最大限度地减少平均能耗，同时保证在任何执行条件下所有任务（包括 LO 关键性任务）的（最坏情况）时序正确性。我们在非抢占式固定优先级调度策略下对此类系统进行响应时间分析。最后，我们基于随机生成的任务集进行了广泛的模拟活动，以验证我们的算法的有效性（在节能方面），并报告节能高达 46%。
The strict requirements on the timing correctness biased the modeling and analysis of real-time systems toward the worst-case performances. Such focus on the worst-case, however, does not provide enough information to effectively steer the resource/energy optimization. In this article, we integrate a probabilistic-based energy prediction strategy with the precise scheduling of mixed-criticality tasks, where the timing correctness must be met for all tasks at all scenarios. The dynamic voltage and frequency scaling (DVFS) is applied to this precise scheduling policy to enable energy minimization. We propose a probabilistic technique to derive an energy-efficient speed (for the processor) that minimizes the average energy consumption, while guaranteeing the (worst-case) timing correctness for all tasks, including LO-criticality ones, under any execution condition. We present a response time analysis for such systems under the nonpreemptive fixed-priority scheduling policy. Finally, we conduct an extensive simulation campaign based on randomly generated task sets to verify the effectiveness of our algorithm (with respect to energy savings) and it reports up to 46% energy-saving.