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A Novel Penalty Controllable Dynamic Voltage Scaling Scheme for Mobile Multimedia Applications

机译:一种适用于移动多媒体应用的惩罚性可控动态电压缩放方案

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Multimedia decoding is one of the key parts of many popular mobile multimedia applications, such as video telephony, streaming, and video playback. Since the multimedia decoding consumes a significant amount of energy on processors, it is crucial to lower the power consumption and prolong the battery life. In this paper, the statistical analysis of more than 600 processor load trace files is first presented. From the analysis, we found that it is feasible to predict the processor load of multimedia applications accurately using a low order linear time series model if the load is sampled using the feature period, which is obtained with fast Fourier transformation. Based on the analysis, we propose a novel interval-based DVS scheme to achieve penalty controllable energy reduction. The DVS scheme does not need any task profile or involvement of application program, and it is compatible with the service model of general purpose mobile operating systems. In addition, the proposed DVS scheme can handle the nonstationary behavior using an efficient online change detector, and important parameters, such as coefficients of the linear time series model, are estimated on the fly. More importantly, the proposed scheme can keep the overscaling rate (OSR) around a certain predefined value. Since the OSR has a simple and stable relationship with the deadline miss rate (DMR), the penalty incurred by DVS is effectively controlled. Experimental results show that the proposed DVS scheme achieves a much smaller prediction error than previous approaches and achieves a significant processor energy reduction with adjustable and controlled penalty
机译:多媒体解码是许多流行的移动多媒体应用程序(例如视频电话,流媒体和视频播放)的关键部分之一。由于多媒体解码会在处理器上消耗大量能量,因此降低功耗和延长电池寿命至关重要。本文首先介绍了600多个处理器负载跟踪文件的统计分析。通过分析,我们发现,如果使用特征周期对负载进行采样(通过快速傅里叶变换获得),则使用低阶线性时间序列模型准确预测多媒体应用程序的处理器负载是可行的。在此基础上,我们提出了一种基于区间的DVS方案,以实现惩罚可控的节能效果。 DVS方案不需要任何任务配置文件或应用程序的参与,并且与通用移动操作系统的服务模型兼容。此外,提出的DVS方案可以使用高效的在线变化检测器来处理非平稳行为,并且可以动态估算重要参数,例如线性时间序列模型的系数。更重要的是,所提出的方案可以使超标率(OSR)保持在某个预定值附近。由于OSR与截止期限未命中率(DMR)具有简单而稳定的关系,因此可以有效地控制DVS带来的损失。实验结果表明,所提出的DVS方案比以前的方法实现了更小的预测误差,并且在可调节和可控制的损失下实现了显着的处理器能耗降低

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