## 4 Results and Analysis
### Discussion of Results  
In our evaluation, we compared the latency achieved by the ONSSO framework with traditional resource allocation mechanisms. The traditional methods exhibited latency values between 60 and 90 ms, as documented in studies by [PERSON] et al. [16]. In contrast, the ONSSO framework demonstrated significantly lower latency values, ranging from 10 to 25 ms, due to its dynamic, AI-driven resource allocation capabilities. This performance improvement highlights ONSSO's potential to support ultra-low latency applications, which is critical in meeting 5G's stringent requirements. This improvement can be attributed to the ability of the ONSSO framework to predict resource requirements in real time using Automated Reinforcement Learning (AutoRL), reducing the delays introduced by static or reactive resource allocation. The heatmap in Figure 10 visualises the latency across different network slices, regions, and time intervals under the proposed ML-based ONSSO framework. Low-latency regions: the majority of the network slices exhibit reduced latency, demonstrating the effectiveness of the proposed ML-driven orchestration framework in dynamically optimising slice performance. The clustering of light-shaded areas highlights improved real-time responsiveness, likely attributed to the adaptive allocation of resources. High-latency zones: a few regions in the bottom-left quadrant display persistent latency spikes. This could be due to heavy resource demands or suboptimal scheduling at those points. These findings suggest that, while the model performs optimally for most slices, further tuning of resource allocation strategies may be required to address latency outliers. The throughput increases drastically in the ONSSO framework, reaching 250-500 Mbps, compared to 20-50 Mbps for traditional methods. Throughput measures the rate at which data are successfully transmitted over the network, typically expressed in megabits per second (Mbps). For 5G networks, throughput is a key indicator of network capacity, and it determines the ability of the network to handle high-speed data traffic. Figure 11 provides a comparative analysis of algorithms employed in the ONSSO framework orchestration, evaluating their performance in terms of latency reduction, accuracy, and computational efficiency. Figure 11: Comparative performance analysis of algorithms used in ONSSO orchestration. Figure 10: Latency heatmap showing the latency distribution across various network slices. The packet loss is reduced to less than 1%, indicating better data integrity. Packet loss refers to the failure of data packets to reach their destination, which is detrimental in real-time applications like video conferencing, online gaming, and industrial automation, where lost data can lead to quality degradation or communication breakdowns. In comparison, in traditional 5G slicing, packet loss rates can vary between 2 and 5%, particularly under high network loads. Traditional static resource allocation approaches often lead to either over-provisioning or under-utilisation of resources, with average utilisation rates around 60-75%. By contrast, the ONSSO framework, driven by AI and AutoRL, dynamically adjusts resource allocation based on real-time network conditions and demands. In our simulations, the ONSSO framework consistently achieved utilisation rates between 85 and 95%, indicating more efficient use of available resources while maintaining flexibility to handle traffic spikes. Figure 12 illustrates CPU, memory, and bandwidth utilisation across various network slices during the operation of the AI-based network orchestration framework. The graph highlights the resource usage patterns over time or across network slices, providing a comprehensive view of how computational and bandwidth resources are distributed under dynamic orchestration conditions. CPU usage remains relatively stable across most slices, indicating that the orchestration model effectively distributes computational workloads.