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...
| Publicado en: | Journal of Medical Systems Vol. 39; no. 10; pp. 1 - 12 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115925171&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925171 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2015 vid: 39 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925171 115925171 115925171 10.1007/s10916-015-0320-5 115925171 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: A Machine Learning Method for Power Prediction on the Mobile Devices. aug: au: Chen, Da-Ren Chen, You-Shyang Chen, Lin-Chih Hsu, Ming-Yang Chiang, Kai-Feng affil: Department of Information Management, National Taichung University of Science and Technology, Taichung City 404 Republic of China sug: subj: Artificial Intelligence Cellular Phone Computers, Portable Power Sources Utilization Neural Networks (Computer) Operating Systems Descriptive Statistics Software Design Computer Processor Computer Hardware Funding Source ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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