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A Machine Learning Method for Power Prediction on the Mobile Devices.

Da-Ren Chen1, You-Shyang Chen, Lin-Chih Chen

  • 1Department of Information Management, National Taichung University of Science and Technology, Taichung City, 404, Taiwan, Republic of China.

Journal of Medical Systems
|August 27, 2015
PubMed
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This study introduces a fuzzy neural network (FNN) to analyze process execution and predict energy consumption based on system call patterns. The developed Power Estimation Daemon (PED) offers accurate energy profiling for multicore mobile architectures.

Area of Science:

  • Computer Engineering
  • Artificial Intelligence
  • Mobile Computing

Background:

  • Energy profiling is crucial for multicore mobile architectures.
  • System call sequences are recognized for anomaly detection, but their relation to power consumption is understudied.

Purpose of the Study:

  • To propose a fuzzy neural network (FNN) for analyzing process execution behavior.
  • To develop a Power Estimation Daemon (PED) for predicting energy consumption based on system call patterns.

Main Methods:

  • Utilized a fuzzy neural network (FNN) to train and analyze process execution with system calls, parameters, and power consumption.
  • Developed a Power Estimation Daemon (PED) that categorizes system call sequences and predicts energy usage.
  • Implemented PED in an operational stage to identify predefined system call sequences and estimate energy consumption.

Related Experiment Videos

Main Results:

  • The FNN effectively analyzes process behavior concerning system calls and power consumption.
  • The PED successfully categorizes system call sequences and predicts energy consumption.
  • The system demonstrates accurate energy estimation for running processes based on their system call patterns.

Conclusions:

  • The proposed FNN and PED provide a novel approach to energy profiling in multicore mobile systems.
  • This method enhances the understanding of power consumption linked to system call patterns.
  • The research contributes to efficient energy management in mobile devices.