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首页> 外文期刊>Journal of systems architecture >ENOrMOUS: ENergy Optimization for MObile plateform using User needS
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ENOrMOUS: ENergy Optimization for MObile plateform using User needS

机译：巨大的：使用用户需求的移动板式能量优化

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摘要

Optimizing energy consumption in modern mobile handled devices plays a crucial role as lowering the power consumption impacts battery life and system reliability. Recent mobile platforms have an increasing number of sensors and processing components. Added to the popularity of power-hungry applications, battery life in mobile devices is an important issue. However, the utilization pattern of large amount of data from the various sensors can be beneficial to detect the changing device context, the user needs and the running application requirements in terms of resources. When these information are used properly, an efficient control of power knobs can be implemented to reduce the energy consumption. This paper presents a framework for ENergy Optimization for MObile platform using User needS (ENOsMOUS). This framework is able to identify user contexts and to understand user habits, preferences and needs to improve the operating system power scheme. Machine Learning (ML) algorithms have been used to obtain an efficient trade-off between power consumption reduction opportunities and user satisfaction requirements. ENOrMOUS is a generic solution that manages the power knobs. When applied to the CPU frequency, the sound level, the screen brightness and the Wi-Fi, ENOrMOUS can lower the power consumption by up to 35% compared the out-of-the-box operating system power manager schemes with a negligible overhead.
机译：优化现代移动处理设备中的能耗起到了一个至关重要的作用，降低功耗影响电池寿命和系统可靠性。最近的移动平台具有越来越多的传感器和处理组件。添加到耗电量的普及，移动设备中的电池寿命是一个重要问题。然而，来自各种传感器的大量数据的利用模式可以有利于检测改变设备上下文，用户需求和运行的应用要求。当正确使用这些信息时，可以实现对功率旋钮的有效控制以降低能量消耗。本文介绍了使用用户需求（Enosmous）的移动平台的能量优化框架。该框架能够识别用户上下文，并了解用户习惯，首选项和需要改进操作系统电源方案。机器学习（ML）算法已被用于在功耗降低机会和用户满意度要求之间获得有效的权衡。巨大的是管理电源旋钮的通用解决方案。当应用于CPU频率时，声级，屏幕亮度和Wi-Fi，可以将功耗降低到35％，比较出箱式操作系统电源管理器方案，具有可忽略的开销。

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