低功耗嵌入式操作系统的实时动态电压缩放

P. Pillai, K. Shin
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引用次数: 1303

摘要

近年来,非传统计算平台,特别是移动和便携式计算设备迅速而广泛地传播开来。随着应用变得越来越复杂和处理能力的提高,这些设备最严重的限制是可用的电池寿命。动态电压缩放(DVS)是利用处理器的硬件特性,通过降低电源电压和工作频率来降低功耗的一项关键技术。在通用系统中,分布式交换机算法能够在提供必要的峰值计算能力的同时显著节省能源。然而,对于诸如移动电话和摄像机之类的嵌入式实时系统中的大量应用来说,可变的工作频率会干扰它们的截止日期保证机制,而在这种情况下,尽管DVS越来越重要,但它在很大程度上被忽视/开发不足。为了提供实时保证,分布式交换机必须考虑实时任务的最后期限和周期性,这需要与实时调度程序集成。在本文中,我们提出了一类称为实时分布式交换机(RT-DVS)的新算法,它修改了操作系统的实时调度程序和任务管理服务,在保持实时截止日期保证的同时提供显著的能源节约。我们通过模拟和工作原型实现表明,这些RT-DVS算法非常接近能耗的理论下限,并且可以轻松地将嵌入式实时系统的能耗降低20%至40%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time dynamic voltage scaling for low-power embedded operating systems
In recent years, there has been a rapid and wide spread of non-traditional computing platforms, especially mobile and portable computing devices. As applications become increasingly sophisticated and processing power increases, the most serious limitation on these devices is the available battery life. Dynamic Voltage Scaling (DVS) has been a key technique in exploiting the hardware characteristics of processors to reduce energy dissipation by lowering the supply voltage and operating frequency. The DVS algorithms are shown to be able to make dramatic energy savings while providing the necessary peak computation power in general-purpose systems. However, for a large class of applications in embedded real-time systems like cellular phones and camcorders, the variable operating frequency interferes with their deadline guarantee mechanisms, and DVS in this context, despite its growing importance, is largely overlooked/under-developed. To provide real-time guarantees, DVS must consider deadlines and periodicity of real-time tasks, requiring integration with the real-time scheduler. In this paper, we present a class of novel algorithms called real-time DVS (RT-DVS) that modify the OS's real-time scheduler and task management service to provide significant energy savings while maintaining real-time deadline guarantees. We show through simulations and a working prototype implementation that these RT-DVS algorithms closely approach the theoretical lower bound on energy consumption, and can easily reduce energy consumption 20% to 40% in an embedded real-time system.
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