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Machine Learning for Improving Mobile User Satisfaction
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DOI：10.1145/3297280.3297398.png)
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Abstract
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Optimizing energy consumption in modern mobile handheld devices plays a crucial role as lowering power consumption impacts system's autonomy and system reliability. Recent mobile platforms have an increasing number of sensors and processing components. Added to the increasing popularity of power-hungry applications, battery life in mobile devices is an important issue. However, we think that the utilization of the large amount of data from the various sensors can be beneficial to detect the changing device context, the user needs or the running application requirements in terms of resources. When these information are used properly, an efficient control of power knobs can be implemented thus reducing the energy consumption. This paper presents URBOC, for User Request Based Optimization Component. This component is an extension of our previous framework [7] ENOrMOUS. This framework was able to identify and classify the user contexts in order to understand user habits, preferences and needs which allow to improve the operating system power scheme. In this paper, we extend the use of ENOrMOUS by allowing the user to send requests in order to extend the battery life up to a specific time. Machine Learning (ML) and data mining algorithms have been used to obtain an efficient trade-off between power consumption reduction opportunities and user requests satisfaction. The proposed solution increases battery life by up to seven hours depending on user requests vs. the out-of-the-box operating system power manager with a negligible overhead.
Keywords:
Mobile Power Consumption
Mobile Systems
Neural networks
run-time analysis
data mining algorithms
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