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首页> 外文期刊>IEEE transactions on industrial informatics >Elastic DVS Management in Processors With Discrete Voltage/Frequency Modes
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Elastic DVS Management in Processors With Discrete Voltage/Frequency Modes

机译:具有离散电压/频率模式的处理器中的弹性DVS管理

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摘要

Applying classical dynamic voltage scaling (DVS) techniques to real-time systems running on processors with discrete voltage/frequency modes causes a waste of computational resources. In fact, whenever the ideal speed level computed by the DVS algorithm is not available in the system, to guarantee the feasibility of the task set, the processor speed must be set to the nearest level greater than the optimal one, thus underutilizing the system. Whenever the task set allows a certain degree of flexibility in specifying timing constraints, rate adaptation techniques can be adopted to balance performance (which is a function of task rates) versus energy consumption (which is a function of the processor speed). In this paper, we propose a new method that combines discrete DVS management with elastic scheduling to fully exploit the available computational resources. Depending on the application requirements, the algorithm can be set to improve performance or reduce energy consumption, so enhancing the flexibility of the system. A reclaiming mechanism is also used to take advantage of early completions. To make the proposed approach usable in real-world applications, the task model is enhanced to consider some of the real CPU characteristics, such as discrete voltage/frequency levels, switching overhead, task execution times nonlinear with the frequency, and tasks with different power consumption. Implementation issues and experimental results for the proposed algorithm are also discussed
机译:将经典的动态电压缩放(DVS)技术应用于在具有离散电压/频率模式的处理器上运行的实时系统会浪费计算资源。实际上,每当DVS算法计算出的理想速度水平在系统中不可用时,为了保证任务集的可行性,就必须将处理器速度设置为大于最佳速度的最接近水平,从而使系统无法充分利用。只要任务集在指定时序约束时允许一定程度的灵活性,就可以采用速率自适应技术来平衡性能(这是任务速率的函数)与能耗(是处理器速度的函数)之间的平衡。在本文中,我们提出了一种将离散DVS管理与弹性调度相结合的新方法,以充分利用可用的计算资源。根据应用需求,可以将算法设置为提高性能或减少能耗,从而增强系统的灵活性。还使用回收机制来利用早期完成的优势。为了使所提出的方法在实际应用中可用,增强了任务模型以考虑某些实际的CPU特性,例如离散的电压/频率水平,切换开销,任务执行时间与频率呈非线性关系以及具有不同功率的任务消费。还讨论了该算法的实现问题和实验结果

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