Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16650
Title: Ubiquitous Learning Models for 5G Communication Network Utility Maximization Through Utility-Based Service Function Chain Deployment
Authors: Alghayadh, Faisal Yousef
Ramesh, Janjhyam Venkata Naga
Quraishi, Aadam
Dodda, Sarath babu
Maruthi, Srihari
Raparthi, Mohan
Patni, Jagdish Chandra
Farouk, Ahmed
Keywords: Genetic Algorithms
Ubiquitous Learning
Matlab'S Linprog Function
5G-Ran-Architecture
Service Function Chain
Network Functions Virtualization
Issue Date: 2024
Publisher: Computers In Human Behavior
Pergamon-Elsevier Science Ltd
Citation: Vol. 156
Abstract: The main problem of deploying service function chains in virtualized 5G networks is dealt with in wireless 5G communications and effective ubiquitous learning models. The aim is to ensure differentiated network performance for a wide range of services while maximizing the collaborative revenue of infrastructure operators and wireless virtual operators. To achieve this, a utility-based service function chain deployment strategy is introduced, tailored to the specific characteristics of Ubiquitous Learning based 5G-RAN-Architecture. Consideration is given to the virtual operator's maximum tolerable end-to-end latency, minimum service rate requirements, and the infrastructure operator's constraints on computing and link resources. It also looks at how different deployment scenarios for service function chains affect network performance and creates a utility model using a business framework. The ultimate objective is to optimize the collective revenue of infrastructure operators and virtual operators. The approach leverages genetic algorithms and Matlab's Linprog function for iterative problem-solving. The graph clearly shows that the SFC deployment algorithm in this study uses less infrastructure resources for Front Haul links. This implies that the algorithm successfully reduces the load on Front Haul lines, which in turn lowers the cost of SFC deployment and makes it easier to deploy more SFCs across the infrastructure. This work contributes to the evolution of 5G wireless communications and its seamless integration with ubiquitous learning models.
URI: http://dx.doi.org/10.1016/j.chb.2024.108227
https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16650
ISSN: 0747-5632
1873-7692
Appears in Collections:Journal Articles

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