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Android应用程序能耗分析与建模研究
引用本文:段林涛,郭兵,沈艳,王毅,张文丽,熊伟. Android应用程序能耗分析与建模研究[J]. 电子科技大学学报(自然科学版), 2014, 43(2): 272-277. DOI: 10.3969/j.issn.1001-0548.2014.02.022
作者姓名:段林涛  郭兵  沈艳  王毅  张文丽  熊伟
作者单位:1.四川大学计算机学院 成都 610065;
基金项目:国家自然科学基金(61332001, 61272104, 61073045); 四川省杰出青年科技基金(2010JQ0011); 四川省产学研创新联盟合作项目(2012ZZ0010); 四川省科技支撑计划(2012GZX0088-1)
摘    要:应用程序能耗分析与建模是智能移动终端能耗优化的重要组成部分. 针对智能移动终端丰富的应用程序, 提出了一种基于应用程序运行时间的时间能耗模型. 与精度高和复杂的应用程序组件能耗模型相比, 该模型使用时间变量刻画和包含终端的功耗、性能等多种因素, 而且运行时间容易精确测量和获取, 能够快速地估算应用程序运行时移动终端产生的能耗. 实验结果表明, 在GT-I9108、GT-I9308和GT-P3108实验平台下, 该模型的能耗估算结果与Android操作系统应用程序框架提供的组件能耗模型测量值相比平均误差分别为0.89%、1.37%和0.29%, 能够为移动终端用户便捷地预测应用程序消耗的电池电能提供帮助.

关 键 词:应用程序能耗模型   电池电能   能耗模型   移动终端   功耗
收稿时间:2012-12-21

Analysis and Modeling of Android Application Energy Consumption
Affiliation:1.School of Computer Science,Sichuan University Chengdu 610065;2.School of Information Science and Technology,Chengdu University Chengdu 610106;3.Key Laboratory of Pattern Recognition and Intelligent Information Processing,Chengdu University Chengdu 610106;4.School of Control Engineering,Chengdu University of Information Technology Chengdu 610225
Abstract:Analysis and modeling of application energy consumption plays a vital role in the optimization of energy consumption of smart mobile devices. An application energy model based on application's running time is proposed. Compared with application's component energy models with high accuracy and complexity, the model is characterized by the time variable and contains a variety of mobile devices' properties, such as power consumption and performance, which can be used to rapidly estimate the mobile devices' energy consumption during application execution, and the execution time of the application is easy to measure and obtain. The experiment results show that the average error rate of proposed model is 0.89%, 1.37% and 0.29% compared with that measured by component energy model provided by Android application framework on GT-I9108, GT-I9308 and GT-P3108, respectively. The model can be used to help the end users to rapidly and conveniently predict the battery energy consumption of applications.
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