## 4 Results and Analysis
### Discussion of Results
This will help meet the network slice requirement of all applications by efficiently managing and allocating the network slice dynamically to applications requesting them while proactively catering to available resources. The ONSO framework effectively matches each application to a network slice that best fits its performance requirements. Each row represents a unique application or protocol, identified by its protocol name, where:  
* Best_Fit_Slice: the slice category assigned by the framework to meet the application's requirements (e.g., slice 2 corresponds to bespoke). * Alternative_Slice: the fallback slice category if the best fit slice cannot meet resource demands. For example, protocol 'GOOGLE' is the best fit for slice 2, with slice 1 as the alternative. Protocols 'AMAZON' and 'YOUTUBE' are also allocated slice 2, with a fallback to slice 1. Figure 14: Best fit and alternative slice allocation for various applications. This highlights the framework's ability to dynamically allocate and suggest alternative slices to optimise resource usage. The metrics used to improve the Quality of Service (QoS) and the Quality of Experience (QoE) of the orchestrated slice were classified into low, medium, and high thresholds based on 3 GPP recommendations [17]:  
* **Low Traffic Load**: supports applications such as email, general browsing, and IoT sensors with minimal demands on bandwidth and latency. User Traffic Rate: up to 5 Mbps per user; Network Resource Utilisation: 0-30%; Latency: 10-20 ms.
* **Moderate Traffic Load**: manages services like video conferencing and streaming, with moderate demands on resources. User Traffic Rate: 5-50 Mbps per user; Network Resource Utilisation: 30-70%; Latency: 20-50 ms.
* **High Traffic Load**: targets high-demand applications like autonomous vehicle control or gaming, requiring significant resources and ultra-low latency. User Traffic Rate: over 50 Mbps per user; Network Resource Utilisation: >70%; Latency: 1-10 ms. The resource utilisation analysis highlights the AI model's capacity to optimise resource management dynamically. While the model ensures efficient CPU and bandwidth allocation, periodic peaks in memory usage suggest the need for improved memory prediction techniques for high-demand slices. Overall, the findings validate the model's robustness in resource utilisation while maintaining operational efficiency. The CNaaS model is designed to be compatible with current 5G ecosystems, leveraging existing network slicing and virtualisation technologies. Key considerations include:  
* Scalability: the model supports dynamic scaling to accommodate varying company sizes and demands. * Interoperability: it is designed to integrate with standard 5G network functions and management systems. * Deployment: implementation can be achieved through collaboration with network operators, utilising their infrastructure to offer tailored network services to enterprises. While the model is feasible with current technology, successful deployment would require addressing challenges such as security, quality of service assurance, and regulatory compliance. The CNaaS model leverages existing 5G technologies such as network slicing and virtualisation, making it a viable solution for modern network demands. However, challenges including integration complexity, security concerns, and the need for standardised APIs must be addressed to ensure seamless implementation.