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首页> 外文会议>Proceedings of the 2010 13th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops (ISORC Workshops 2010) >Optimisation of Energy Consumption of Soft Real-Time Applications by Workload Prediction
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Optimisation of Energy Consumption of Soft Real-Time Applications by Workload Prediction

机译：通过工作量预测优化软实时应用程序的能耗

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摘要

Embedded real-time systems often operate under energy constraints due to a limited battery lifetime. Modern processors provide techniques for dynamic voltage and frequency scaling to reduce energy consumption. However, while the processor possibly operates at a lower clock frequency, the running applications should still meet their deadlines and thus set some limits to the use of scaling techniques. In this paper, we propose auto correlation clustering (ACC) as a technique to predict the workload of single iterations of a periodic soft real-time application. Based on this prediction we adjust the processor performance such that deadlines are exactly met. We compare our technique to the broadly implemented race-to-idle (RTI) and identify situations where ACC can gain higher energy savings than RTI. Additionally, ACC can help saving energy in multithreaded processors where RTI can be applied only with a high overhead if at all.
机译：由于电池寿命有限，嵌入式实时系统通常在能量约束下运行。现代处理器提供了动态电压和频率缩放技术，以减少能耗。但是，尽管处理器可能以较低的时钟频率运行，但是运行中的应用程序仍应按时限运行，因此对缩放技术的使用设置了一些限制。在本文中，我们提出自动相关性聚类（ACC）作为一种预测周期性软实时应用程序单次迭代工作量的技术。基于此预测，我们将调整处理器性能，以便准确地满足最后期限。我们将我们的技术与广泛采用的“空转”技术进行了比较，并确定了ACC可以比RTI节省更多能源的情况。此外，ACC可以帮助节省多线程处理器中的能源，在这些处理器中，RTI仅在高开销的情况下才能应用。

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