An OP-TEE Energy-Efficient Task Scheduling Approach Based on Mobile Application Characteristics  

作　　者：Hai Wang Xuan Hao Shuo Ji Jie Zheng Yuhui Ma Jianfeng Yang 

机构地区：[1]State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services,School of Information Science and Technology,Northwest University,Xi’an,710127,China

出　　处：《Intelligent Automation & Soft Computing》2023年第8期1621-1635,共15页智能自动化与软计算（英文）

基　　金：funded by National Key Research and Development Program of China under Grant No.2019YFC1520904 from January 2020 to April 2023;funded by Shaanxi Innovation Program under Grant 2023-CX-TD-04 January 2023 to December 2025.

摘　　要：Trusted Execution Environment(TEE)is an important part of the security architecture of modern mobile devices,but its secure interaction process brings extra computing burden to mobile devices.This paper takes open portable trusted execution environment(OP-TEE)as the research object and deploys it to Raspberry Pi 3B,designs and implements a benchmark for OP-TEE,and analyzes its program characteristics.Furthermore,the application execution time,energy consumption and energy-delay product(EDP)are taken as the optimization objectives,and the central processing unit(CPU)frequency scheduling strategy of mobile devices is dynamically adjusted according to the characteristics of different applications through the combined model.The experimental result shows that compared with the default strategy,the scheduling method proposed in this paper saves 21.18%on average with the Line Regression-Decision Tree scheduling model with the shortest delay as the optimization objective.The Decision Tree-Support Vector Regression(SVR)scheduling model,which takes the lowest energy consumption as the optimization goal,saves 22%energy on average.The Decision Tree-K-Nearest Neighbor(KNN)scheduling model with the lowest EDP as the optimization objective optimizes about 33.9%on average.

关 键 词：Trusted execution environment energy efficiency optimization CPU scheduling governor machine learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]