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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Vydané v:Sensors (Basel, Switzerland) Ročník 21; číslo 22; s. 7701
Hlavný autor: Shah, Sayed-Chhattan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 19.11.2021
MDPI
Predmet:
Cameras
Cloud computing
Computer centers
Computers
Data analysis
Data collection
Design
Distributed processing
edge computing
fog computing
Infrastructure
intelligent network layer
Internet access
Internet of Things
internet of things applications
local cluster heterogeneous network
Machine learning
Operating systems
Personal computers
Project Report
resource management
Robots
Security services
Sensors
Smart cities
Smart houses
Smartphones
Surveillance
Wireless communications
Wireless networks
ISSN:1424-8220, 1424-8220
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Shrnutí: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s21227701