Optimizing 6G Network Slicing with the EvoNetSlice Model for Dynamic Resource Allocation and Real-Time QoS Management

Venkata Ramana K; Ramesh B; Ravindra Changala; Aditya Sai Srinivas T; Praveen Kumar Kalangi; Bhavsingh M

doi:10.54392/irjmt24324

Authors

Venkata Ramana K Department of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad-500090, Telangana, India Author https://orcid.org/0000-0002-2208-7966
Ramesh B Department of Electronics and Communication Engineering, CVR College of Engineering, Telangana, India Author https://orcid.org/0000-0003-4116-2156
Ravindra Changala Department of Information Technology, Guru Nanak Institutions Technical Campus, Hyderabad, India Author
Aditya Sai Srinivas T Department of Computer Science and Engineering, Jayaprakash Narayan College of Engineering, Mahabubnagar-509001, Telangana, India Author
Praveen Kumar Kalangi Department of Information Technology, Anurag University, Hyderabad, India Author https://orcid.org/0000-0002-8378-4191
Bhavsingh M Department of Computer Science and Engineering, Ashoka Women’s Engineering College, Kurnool, Andhra Pradesh, India Author https://orcid.org/0000-0002-9634-8794

DOI:

https://doi.org/10.54392/irjmt24324

Keywords:

6G Networks, Network Slicing, Dynamic Resource Allocation, Real-Time Qos Adaptation, Evolutionary Algorithms, AI-Powered Analytics, Multi-Objective Optimization, Network Efficiency, Low Latency, High Throughput, Data-Driven Insights

Abstract

This research paper focuses on thoroughly examining the challenges in 6G network slicing. To develop, evaluate performance characteristics for on-demand reallocation and instantaneously changeable QoS EvoNetSlice model. The study employs integrated evolutionary algorithms with artificial intelligence-enabled data analytics and multi-objective optimization to optimize network resources usage under minimum end-to-end delay, high transmission rates and optimal background data management. Firstly, the network resource allocation individuals should be based on the network traffic data, QoD (quality of demand) value for some applications and users’ behaviors. The performance degradation detection and quality of service (QoS) adaptation mechanism combined with a multi-layer objective fitness function for achieving good balance in conflict between conflicting objectives. Results indicate that EvoNetSlice improves the general efficiency of a particular network, adapts according to ever shifting requirements for QoS at any time and provides crucial statistics-focused data on network management. The importance of this work lies in developing the future 6G network’s technology. W the key issues, including resource optimization and real-time adaptation required to support modern 6G services, are considered by EvoNetSlice. Such an exploration is an essential element in developing flexible 6G systems that will define next-generation wireless communication.

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