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

Abstract

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.

Construction and Application of a Private Cloud-Based Online Simulation Training Platform for Higher Education

“…Crucially, the platform employs open-source virtualization solutions to build a private cloud physical resource pool, deploying various applications through virtual machines and efficiently managing and allocating resources with container orchestration platforms, ensuring the stable operation of the system. This technological architecture not only provides a secure and controllable online teaching environment but also offers users limitless possibilities for learning and experimentation, making a valuable contribution to the advancement of the education sector [4][5] .…”

Classification of Odor Drift Data Based on Several Machine Learning Algorithms

“…Machine learning algorithm has the ability to process large-scale data and complex models [2], and can classify and identify multidimensional data features of odor drift, so as to judge the feature significance and recognizability of odor, so as to better explore and manage odor drift data later. Through experiments on odor drift, we can better distinguish the accuracy of machine learning algorithms supported by more superior algorithms and understand the relevant principles.…”