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01-10-2015 | Mobile Systems

A Machine Learning Method for Power Prediction on the Mobile Devices

Authors: Da-Ren Chen, You-Shyang Chen, Lin-Chih Chen, Ming-Yang Hsu, Kai-Feng Chiang

Published in: Journal of Medical Systems | Issue 10/2015

Abstract

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.

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Title: A Machine Learning Method for Power Prediction on the Mobile Devices
Authors: Da-Ren Chen
You-Shyang Chen
Lin-Chih Chen
Ming-Yang Hsu
Kai-Feng Chiang
Publication date: 01-10-2015
Publisher: Springer US
Published in: Journal of Medical Systems / Issue 10/2015
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-015-0320-5

At a glance: The STEP trials

Springer Medicine

A Machine Learning Method for Power Prediction on the Mobile Devices

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

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