Machine Learning-Based Network Sub-Slicing Framework in a Sustainable 5G Environment

Abstract

Nowadays, 5G network infrastructures are being developed for various industrial IoT (Internet of Things) applications worldwide, emerging with the IoT. As such, it is possible to deploy power-optimized technology in a way that promotes the long-term sustainability of networks. Network slicing is a fundamental technology that is implemented to handle load balancing issues within a multi-tenant network system. Separate network slices are formed to process applications having different requirements, such as low latency, high reliability, and high spectral efficiency. Modern IoT applications have dynamic needs, and various systems prioritize assorted types of network resources accordingly. In this paper, we present a new framework for the optimum performance of device applications with optimized network slice resources. Specifically, we propose a Machine Learning-based Network Sub-slicing Framework in a Sustainable 5G Environment in order to optimize network load balancing problems, where each logical slice is divided into a virtualized sub-slice of resources. Each sub-slice provides the application system with different prioritized resources as necessary. One sub-slice focuses on spectral efficiency, whereas the other focuses on providing low latency with reduced power consumption. We identify different connected device application requirements through feature selection using the Support Vector Machine (SVM) algorithm. The K-means algorithm is used to create clusters of sub-slices for the similar grouping of types of application services such as application-based, platform-based, and infrastructure-based services. Latency, load balancing, heterogeneity, and power efficiency are the four primary key considerations for the proposed framework. We evaluate and present a comparative analysis of the proposed framework, which outperforms existing studies based on experimental evaluation.

Machine learning based distributed big data analysis framework for next generation web in IoT

“…For machine learning, the K-means algorithm is one of the simplest and popular for clustering. It does not have labels or results in data processing, so it is called unsupervised learning [10,26]. The K-means algorithm's primary idea is to group (cluster) related data sets values and recognize underlying designs.…”

“…βmk (a − s ) = 1 for data point belong to cluster, otherwise βmk = 0 and m = 1, 2, 3, … … … … … . M Minimization has two sections: 1) Distance minimize concerning and s stable; 2) Distance minimize concerning s and is stable [10]. Genetic clustering methods can predict the movement of points is known as k-means.…”

“…With the help of a 1-nearest neighbor classifier, find new data value in the existing clusters. Algorithm 2 describes how to get the optimal group datacenters and the authorization of the data points [10].…”

“…Many operations adapt to data control among various communication devices, and it provides intelligence processing, analysis in IoT. Cloud computing is employed for delivering high performance to the IoT server [10]. Traditional machine learning also has significant issues such as low precision, low rate, low latency, and less computational in the cloud layer.…”

Design of a Structured Hypercube Network Chip Topology Model for Energy Efficiency in Wireless Sensor Network Using Machine Learning

“…When a packet is trying to make a jump across a link, it is currently sitting in this buffer entry, and is trying to acquire a buffer entry over here. So, this creates a situation of deadlock [32][33][34].…”