A Machine Learning Method for Power Prediction on the Mobile Devices View Full Text


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Article Info

DATE

2015-10

AUTHORS

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

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. More... »

PAGES

126

References to SciGraph publications

  • 2005. Applying Fuzzy Neural Network to Intrusion Detection Based on Sequences of System Calls in ADVANCED DATA MINING AND APPLICATIONS
  • 2004. Web Users’ Classification Using Fuzzy Neural Network in KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10916-015-0320-5

    DOI

    http://dx.doi.org/10.1007/s10916-015-0320-5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1001132108

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/26306877


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