Innovative Energy Management System for Mobile Processors Power Consumption with Integration of Predictive Neural Network Models

At the present stage of information technologies development in the field of portable devices implementation one of the perspective direction is the development of software and hardware to optimise the consumption of energy resources in mobile processors. Modern solutions are aimed at continuous imp...

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Autores principales:	Elena A. Kirillova, Alexey I. Lazarev
Formato:	article
Lenguaje:	EN
Publicado:	AIDIC Servizi S.r.l. 2021
Materias:	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895
Acceso en línea:	https://doaj.org/article/dcb8611f115448a097d59cef37fbe80d
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spelling	oai:doaj.org-article:dcb8611f115448a097d59cef37fbe80d2021-11-15T21:47:46ZInnovative Energy Management System for Mobile Processors Power Consumption with Integration of Predictive Neural Network Models10.3303/CET21881242283-9216https://doaj.org/article/dcb8611f115448a097d59cef37fbe80d2021-11-01T00:00:00Zhttps://www.cetjournal.it/index.php/cet/article/view/11917https://doaj.org/toc/2283-9216At the present stage of information technologies development in the field of portable devices implementation one of the perspective direction is the development of software and hardware to optimise the consumption of energy resources in mobile processors. Modern solutions are aimed at continuous improvement of methods to reduce power consumption along with increasing the performance of devices, but the problem of limited time interval of portable devices active use is still relevant. Existing solutions are aimed at using various energy-dependent methods in the kernel configuration – I/O scheduler governors, TCP overload algorithms, as well as additional entropy distribution systems and memory release algorithms. The solutions considered involve a system of aggressive behaviour based on the use of user-driven methods to terminate energy demanding processes in the system. The purpose of this study is to overcome the problems under consideration by developing a cross-platform algorithmic approach, which is based on the tracking of the energy consumption processes in the kernel, taking into account system calls and background activity in the system. A distinctive feature of the proposed solution is the use of a neural network training sample for the processes of tracking user behaviour, which affects the reduction of the CPU load by completing side processes and increasing the time interval of battery performance. To implement the project, root access to the system was also used, assuming full-function access to the system kernel. Another feature of the implemented algorithm is backwards compatibility of the work with mobile processors, which allows to organise work on mobile devices.Elena A. KirillovaAlexey I. LazarevAIDIC Servizi S.r.l.articleChemical engineeringTP155-156Computer engineering. Computer hardwareTK7885-7895ENChemical Engineering Transactions, Vol 88 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895
spellingShingle	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895 Elena A. Kirillova Alexey I. Lazarev Innovative Energy Management System for Mobile Processors Power Consumption with Integration of Predictive Neural Network Models
description	At the present stage of information technologies development in the field of portable devices implementation one of the perspective direction is the development of software and hardware to optimise the consumption of energy resources in mobile processors. Modern solutions are aimed at continuous improvement of methods to reduce power consumption along with increasing the performance of devices, but the problem of limited time interval of portable devices active use is still relevant. Existing solutions are aimed at using various energy-dependent methods in the kernel configuration – I/O scheduler governors, TCP overload algorithms, as well as additional entropy distribution systems and memory release algorithms. The solutions considered involve a system of aggressive behaviour based on the use of user-driven methods to terminate energy demanding processes in the system. The purpose of this study is to overcome the problems under consideration by developing a cross-platform algorithmic approach, which is based on the tracking of the energy consumption processes in the kernel, taking into account system calls and background activity in the system. A distinctive feature of the proposed solution is the use of a neural network training sample for the processes of tracking user behaviour, which affects the reduction of the CPU load by completing side processes and increasing the time interval of battery performance. To implement the project, root access to the system was also used, assuming full-function access to the system kernel. Another feature of the implemented algorithm is backwards compatibility of the work with mobile processors, which allows to organise work on mobile devices.
format	article
author	Elena A. Kirillova Alexey I. Lazarev
author_facet	Elena A. Kirillova Alexey I. Lazarev
author_sort	Elena A. Kirillova
title	Innovative Energy Management System for Mobile Processors Power Consumption with Integration of Predictive Neural Network Models
title_short	Innovative Energy Management System for Mobile Processors Power Consumption with Integration of Predictive Neural Network Models
title_full	Innovative Energy Management System for Mobile Processors Power Consumption with Integration of Predictive Neural Network Models
title_fullStr	Innovative Energy Management System for Mobile Processors Power Consumption with Integration of Predictive Neural Network Models
title_full_unstemmed	Innovative Energy Management System for Mobile Processors Power Consumption with Integration of Predictive Neural Network Models
title_sort	innovative energy management system for mobile processors power consumption with integration of predictive neural network models
publisher	AIDIC Servizi S.r.l.
publishDate	2021
url	https://doaj.org/article/dcb8611f115448a097d59cef37fbe80d
work_keys_str_mv	AT elenaakirillova innovativeenergymanagementsystemformobileprocessorspowerconsumptionwithintegrationofpredictiveneuralnetworkmodels AT alexeyilazarev innovativeenergymanagementsystemformobileprocessorspowerconsumptionwithintegrationofpredictiveneuralnetworkmodels
_version_	1718426816262176768

Innovative Energy Management System for Mobile Processors Power Consumption with Integration of Predictive Neural Network Models

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