Energy-Aware CPU Frequency Scaling for Mobile Video Streaming
Energy-Aware CPU Frequency Scaling for Mobile Video Streaming
复制标题
DOI：
10.1109/tmc.2018.2878842
复制
发表时间：
2019-11
影响因子：
7.9
通讯作者：
Yi Yang;Wenjie Hu;Xianda Chen;G. Cao
中科院分区：
文献类型：
--
作者：
Yi Yang;Wenjie Hu;Xianda Chen;G. Cao
视频流所消耗的能量包括数据传输和CPU处理所消耗的能量，两者都受CPU主频的影响。高CPU频率可以减少数据传输时间，但会消耗更多的CPU能量。降低CPU主频可以降低CPU能量，但会增加数据传输时间，从而增加能耗。在本文中，我们的目标是通过自适应调整CPU频率来降低移动视频流的总能量。基于实际测量结果，我们建立了CPU频率对TCP吞吐量和系统功率的影响模型。在此基础上，提出了一种能量感知的CPU频率缩放（EFS）算法，该算法选择的CPU频率在节省数据传输能量和CPU能量之间达到平衡。由于现有视频流应用的下载进度在能量方面没有优化，我们还提出了一种确定何时下载以及下载多少数据的方法。通过跟踪驱动的模拟和实际测量，我们证明了EFS算法可以为Youtube应用程序减少30%的能量，并且我们的下载方法和EFS算法相结合可以比默认的Youtube应用程序节省50%的能量。
The energy consumed by video streaming includes the energy consumed for data transmission and CPU processing, which are both affected by the CPU frequency. High CPU frequency can reduce the data transmission time but it consumes more CPU energy. Low CPU frequency reduces the CPU energy but increases the data transmission time and then increases the energy consumption. In this paper, we aim to reduce the total energy of mobile video streaming by adaptively adjusting the CPU frequency. Based on real measurement results, we model the effects of CPU frequency on TCP throughput and system power. Based on these models, we propose an Energy-aware CPU Frequency Scaling (EFS) algorithm which selects the CPU frequency that can achieve a balance between saving the data transmission energy and CPU energy. Since the downloading schedule of existing video streaming apps is not optimized in terms of energy, we also propose a method to determine when and how much data to download. Through trace-driven simulations and real measurement, we demonstrate that the EFS algorithm can reduce 30 percent of energy for the Youtube app, and the combination of our download method and EFS algorithm can save 50 percent of energy than the default Youtube app.