Energy-Efficient Real-Time Scheduling of DAGs on Clustered Multi-Core Platforms
Energy-Efficient Real-Time Scheduling of DAGs on Clustered Multi-Core Platforms
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DOI：
10.1109/rtas.2019.00021
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
Zhishan Guo;Ashikahmed Bhuiyan;Di Liu;Aamir Khan;Abusayeed Saifullah;Nan Guan
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文献类型：
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作者：
Zhishan Guo;Ashikahmed Bhuiyan;Di Liu;Aamir Khan;Abusayeed Saifullah;Nan Guan
随着多核嵌入式系统上计算密集型实时应用的增长，节能的实时调度变得至关重要。多核处理器实现了任务内并行性，并且在利用这一点方面取得了很大进展，而在节能多核实时调度方面却进展甚微。在这项工作中，我们研究了能源有效的实时调度约束的最后期限零星的并行任务，其中每个任务表示为一个有向无环图（DAG）。我们考虑一个集群的多核平台，在同一个集群内的处理器在任何给定的时间以相同的速度运行。提出了一个新的概念，称为速度剖面模型的每个任务和每个集群的能耗变化在运行时，以尽量减少预期的长期能源消耗。据我们所知，没有现有的工作考虑能源感知的实时调度DAG任务的最后期限约束，也不是在集群多核平台。在ODROID XU-3板上实现了所提出的能量感知实时调度器，以评估和证明其可行性和实用性。为了补充我们的大规模系统实验，我们还进行了模拟，与现有方法相比，通过我们提出的方法，CPU节能高达57%。
With the growth of computation-intensive real-time applications on multi-core embedded systems, energy-efficient real-time scheduling becomes crucial. Multi-core processors enable intra-task parallelism, and there has been much progress on exploiting that, while there has been only a little progress on energy-efficient multi-core real-time scheduling as yet. In this work, we study energy-efficient real-time scheduling of constrained deadline sporadic parallel tasks, where each task is represented as a directed acyclic graph (DAG). We consider a clustered multi-core platform where processors within the same cluster run at the same speed at any given time. A new concept named speed-profile is proposed to model per-task and per-cluster energy-consumption variations during run-time to minimize the expected long-term energy consumption. To our knowledge, no existing work considers energy-aware real-time scheduling of DAG tasks with constrained deadlines, nor on a clustered multi-core platform. The proposed energy-aware realtime scheduler is implemented upon an ODROID XU-3 board to evaluate and demonstrate its feasibility and practicality. To complement our system experiments in large-scale, we have also conducted simulations that demonstrate a CPU energy saving of up to 57% through our proposed approach compared to existing methods.