Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System

Recent advances in mobile technologies have facilitated the development of a new class of smart city and fifth-generation (5G) network applications. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and acces...

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Autor principal:	Sayed-Chhattan Shah
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fog computing edge computing resource management intelligent network layer local cluster heterogeneous network internet of things applications Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/ce4baaa5257f40dbba1625dc8ab1da93
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spelling	oai:doaj.org-article:ce4baaa5257f40dbba1625dc8ab1da932021-11-25T18:58:44ZDesign of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System10.3390/s212277011424-8220https://doaj.org/article/ce4baaa5257f40dbba1625dc8ab1da932021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7701https://doaj.org/toc/1424-8220Recent advances in mobile technologies have facilitated the development of a new class of smart city and fifth-generation (5G) network applications. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to sensors and actuators. A heterogeneous private edge cloud system was proposed to address the requirements of these applications. The proposed heterogeneous private edge cloud system is characterized by a complex and dynamic multilayer network and computing infrastructure. Efficient management and utilization of this infrastructure may increase data rates and reduce data latency, data privacy risks, and traffic to the core Internet network. A novel intelligent middleware platform is proposed in the current study to manage and utilize heterogeneous private edge cloud infrastructure efficiently. The proposed platform aims to provide computing, data collection, and data storage services to support emerging resource-intensive and non-resource-intensive smart city and 5G network applications. It aims to leverage regression analysis and reinforcement learning methods to solve the problem of efficiently allocating heterogeneous resources to application tasks. This platform adopts parallel transmission techniques, dynamic interface allocation techniques, and machine learning-based algorithms in a dynamic multilayer network infrastructure to improve network and application performance. Moreover, it uses container and device virtualization technologies to address problems related to heterogeneous hardware and execution environments.Sayed-Chhattan ShahMDPI AGarticlefog computingedge computingresource managementintelligent network layerlocal cluster heterogeneous networkinternet of things applicationsChemical technologyTP1-1185ENSensors, Vol 21, Iss 7701, p 7701 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fog computing edge computing resource management intelligent network layer local cluster heterogeneous network internet of things applications Chemical technology TP1-1185
spellingShingle	fog computing edge computing resource management intelligent network layer local cluster heterogeneous network internet of things applications Chemical technology TP1-1185 Sayed-Chhattan Shah Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System
description	Recent advances in mobile technologies have facilitated the development of a new class of smart city and fifth-generation (5G) network applications. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to sensors and actuators. A heterogeneous private edge cloud system was proposed to address the requirements of these applications. The proposed heterogeneous private edge cloud system is characterized by a complex and dynamic multilayer network and computing infrastructure. Efficient management and utilization of this infrastructure may increase data rates and reduce data latency, data privacy risks, and traffic to the core Internet network. A novel intelligent middleware platform is proposed in the current study to manage and utilize heterogeneous private edge cloud infrastructure efficiently. The proposed platform aims to provide computing, data collection, and data storage services to support emerging resource-intensive and non-resource-intensive smart city and 5G network applications. It aims to leverage regression analysis and reinforcement learning methods to solve the problem of efficiently allocating heterogeneous resources to application tasks. This platform adopts parallel transmission techniques, dynamic interface allocation techniques, and machine learning-based algorithms in a dynamic multilayer network infrastructure to improve network and application performance. Moreover, it uses container and device virtualization technologies to address problems related to heterogeneous hardware and execution environments.
format	article
author	Sayed-Chhattan Shah
author_facet	Sayed-Chhattan Shah
author_sort	Sayed-Chhattan Shah
title	Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System
title_short	Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System
title_full	Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System
title_fullStr	Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System
title_full_unstemmed	Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System
title_sort	design of a machine learning-based intelligent middleware platform for a heterogeneous private edge cloud system
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ce4baaa5257f40dbba1625dc8ab1da93
work_keys_str_mv	AT sayedchhattanshah designofamachinelearningbasedintelligentmiddlewareplatformforaheterogeneousprivateedgecloudsystem
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Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System

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