A Machine Learning Method for Power Prediction on the Mobile Devices

Energy profiling and estimation have been popular areas of research in multicore mobile architectures. While short sequences of system calls have been recognized by machine learning as pattern descriptions for anomalous detection, power consumption of running processes with respect to system-call pa...

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Bibliographic Details
Published in:Journal of medical systems 2015-10, Vol.39 (10), p.126-126, Article 126
Main Authors: Chen, Da-Ren, Chen, You-Shyang, Chen, Lin-Chih, Hsu, Ming-Yang, Chiang, Kai-Feng
Format: Article
Language:English
Subjects:
Cell Phone
Descriptions
Electric Power Supplies
Embedded systems
Energy consumption
Fuzzy Logic
Health Informatics
Health Sciences
Humans
Information management
Linux
Machine Learning
Medicine
Medicine & Public Health
Mobile communication systems
Mobile Systems
Models, Statistical
Neural networks
Neural Networks (Computer)
Power consumption
Running
Sequences
Smart Living in Healthcare and Innovations
Smartphones
Statistics for Life Sciences
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Summary:Energy profiling and estimation have been popular areas of research in multicore mobile architectures. While short sequences of system calls have been recognized by machine learning as pattern descriptions for anomalous detection, power consumption of running processes with respect to system-call patterns are not well studied. In this paper, we propose a fuzzy neural network (FNN) for training and analyzing process execution behaviour with respect to series of system calls, parameters and their power consumptions. On the basis of the patterns of a series of system calls, we develop a power estimation daemon (PED) to analyze and predict the energy consumption of the running process. In the initial stage, PED categorizes sequences of system calls as functional groups and predicts their energy consumptions by FNN. In the operational stage, PED is applied to identify the predefined sequences of system calls invoked by running processes and estimates their energy consumption.
ISSN:0148-5598
1573-689X
DOI:10.1007/s10916-015-0320-5