CPU Energy-Aware Parallel Real-Time Scheduling
CPU Energy-Aware Parallel Real-Time Scheduling
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DOI：
10.4230/lipics.ecrts.2020.2
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Abusayeed Saifullah;Sezana Fahmida;V. P. Modekurthy;N. Fisher;Zhishan Guo
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文献类型：
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作者：
Abusayeed Saifullah;Sezana Fahmida;V. P. Modekurthy;N. Fisher;Zhishan Guo
节能和实时性能是许多嵌入式系统应用的关键要求，如自动驾驶汽车，机器人系统，灾难响应和安全/安全控制。这些系统需要大量的实时任务，其中每个任务本身是可以同时利用多个计算单元的并行任务。在并行任务需求不断增长的推动下，多核嵌入式处理器不可避免地向众核方向发展。现有的实时并行任务的工作主要集中在实时调度没有解决能源消耗。在本文中，我们解决了硬实时调度的并行任务，同时最大限度地减少他们的CPU能耗多核嵌入式系统。每个任务被表示为一个有向无环图（DAG），节点表示不同的执行线程，边表示它们的依赖关系。我们的技术是确定DAG节点的执行速度，以最大限度地减少整体能耗，同时满足所有任务的最后期限。它结合了一个频率优化引擎和动态电压和频率缩放（DVFS）计划到经典的实时调度策略（联邦和全球），使他们的能量感知。因此，本文的贡献包括第一个能量感知的在线联邦调度，也是第一个能量感知的DAG全局调度。通过模拟使用合成工作负载的评估表明，我们的能源感知实时调度策略可以实现高达68%的节能相比，经典（能源不知道）的政策。我们还使用物理硬件进行了概念系统评估的证明，通过我们提出的方法证明了能量效率。
Both energy-efficiency and real-time performance are critical requirements in many embedded systems applications such as self-driving car, robotic system, disaster response, and security/safety control. These systems entail a myriad of real-time tasks, where each task itself is a parallel task that can utilize multiple computing units at the same time. Driven by the increasing demand for parallel tasks, multi-core embedded processors are inevitably evolving to many-core. Existing work on real-time parallel tasks mostly focused on real-time scheduling without addressing energy consumption. In this paper, we address hard real-time scheduling of parallel tasks while minimizing their CPU energy consumption on multicore embedded systems. Each task is represented as a directed acyclic graph (DAG) with nodes indicating different threads of execution and edges indicating their dependencies. Our technique is to determine the execution speeds of the nodes of the DAGs to minimize the overall energy consumption while meeting all task deadlines. It incorporates a frequency optimization engine and the dynamic voltage and frequency scaling (DVFS) scheme into the classical real-time scheduling policies (both federated and global) and makes them energy-aware. The contributions of this paper thus include the first energy-aware online federated scheduling and also the first energy-aware global scheduling of DAGs. Evaluation using synthetic workload through simulation shows that our energy-aware real-time scheduling policies can achieve up to 68% energy-saving compared to classical (energy-unaware) policies. We have also performed a proof of concept system evaluation using physical hardware demonstrating the energy efficiency through our proposed approach.