## 4 Results and Analysis
### Overview of Simulation Results
#### Results Summary  
The results of the simulation trials are summarised below (Table 1): \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Traditional Model** & **Proposed Model** \\ \hline Latency (ms) & 60–90 & 10–25 \\ Throughput (Mbps) & 20–50 & 250–500 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Result summary on latency and throughput. The Table 2 below shows the evaluation metrics and a comparison between the source of the training data. It evaluates metrics based on limited real data, synthetic data, and augmented data where synthetic and real data were combined for training. **Latency Analysis:** The simulation results show that the ONSSO framework consistently demonstrated lower latency across all scenarios compared to traditional static resource allocation methods, as shown in Figure 9 and the data in Table 1.  
In low-demand scenarios, latency for the traditional models averaged around 60 ms, while, in high-demand scenarios, it peaked at approximately 90 ms as studied by [PERSON] et al. (2015). In contrast, the proposed eSS model achieves a latency range of 10 ms to 25 ms, demonstrating a substantial improvement in responsiveness. It can be deduced that static resource allocation models cannot adjust quickly to fluctuating demand, leading to higher average latencies, unlike the ONSSO framework whose dynamic nature of eSS facilitates the allocation of resources in real time based on service priority and network load, reducing waiting times for resource availability. The average utilisation rate is measured by aggregating the percentage of allocated resources for all active slices over the total available resources during the simulation period, averaged across multiple time intervals. **Throughput Analysis:** Throughput significantly increased with the ONSSO framework, reaching a maximum of 300 Mbps during peak user demand, showcasing its ability to handle high data traffic efficiently. The throughput values for the traditional allocation model vary from 20 Mbps to 50 Mbps, as studied by [PERSON] et al. (2016). The proposed framework, however, achieves throughput values ranging from 250 Mbps to 500 Mbps, highlighting its effectiveness in resource allocation for dynamic 5G network slicing. It can be deduced that traditional throughput's fixed nature of resource allocation in traditional 5G networks does not fully utilise available bandwidth, especially during peak \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Real-Life Data** & **Synthetic Data** &
\begin{tabular}{c} **Augmented Data** \\ **(Real + Synthetic)** \\ \end{tabular} \\ \hline Training Time (h) & 6 & 5.5 & 7 \\ MAE & 1.25 & 1.3 & 1.2 \\ RMSE & 1.75 & 1.8 & 1.7 \\ R-Squared & 0.85 & 0.83 & 0.87 \\ CPU Utilisation & 70 & 65 & 75 \\ Memory Usage (GB) & 12 & 11 & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation result of the dataset types. Figure 9: Comparison of latency and throughput. times, unlike the ONSSO framework, which dynamically adapts resource allocation to maximise bandwidth utilisation, allowing higher data rates and smoother performance and incorporates reinforcement learning via AutoRL. **Resource Utilisation:** The resource utilisation percentage showed an improvement, maintaining an average utilisation rate of 85% during peak demands, indicating effective resource management. The proposed ML-based framework avoids over-provisioning and efficiently manages resources by proactively allocating network slices based on the varying demands, while the traditional model approach has lower performance, as shown in the results, due to its inability to adapt to the varying demands. It lagged in responsiveness, which degraded its performance. The ONSSO framework's innovations in sub-slicing and dynamic resource allocation significantly enhance both latency and throughput. **Comparative Analysis:** The simulation results were compared against baseline models [17], illustrating the superiority of the ONSSO framework in managing resources.