## 4 Results and Analysis
### Discussion of Results
Stability in CPU utilisation suggests the model's efficiency in preventing resource exhaustion. Memory consumption shows periodic peaks, particularly during slices with high data-processing demands. These peaks correlate with traffic surges or resource-intensive operations, suggesting that the AI model dynamically adjusts memory allocation to meet real-time requirements. The predicted slice simulated with MATLAB/Simulink in the proposed ML-based framework was compared against the true slice and an accuracy of 0.99 was achieved with our proposed model. Figure 13 shows the Confusion Matrix for the ONSSO prediction performance across slice categories. Figure 12: Resource utilisation showing CPU, memory, and bandwidth usage across multiple network slices during ONSSO orchestration. The matrix evaluates the classification performance of our ML-based framework by displaying the number of true positive (correct predictions) and false predictions for each slice category. In this matrix:  
* Row 0 (True Slice = 0) corresponds to the bespoke slice, where 796 out of 797 instances are correctly predicted, with one misclassification. * Row 1 (True Slice = 1) corresponds to the URLLC slice, with all two instances correctly predicted. * Row 2 (True Slice = 2) corresponds to another bespoke slice, where 71 out of 72 instances are correctly classified, with one misclassification. From the result shown in Figure 13, it can be seen that Slice 0 and 2 are orchestrated as bespoke slices while slice 1 was identified as a uRLLC slice. Each slice is characterised by parameters such as bandwidth, latency, throughput, data rate, jitter, packet loss, and data volume, as well as metrics related to speed, packet delay, and reliability. These parameters are set to align with the specific requirements of applications and data traffic types. The ONSSO framework's configuration process tailors each slice to fulfil the requirements of diverse applications. For instance, the URLLC slice (slice 1), characterised by high bandwidth, low latency, and high reliability, is designed to support applications that require ultra-reliable and low-latency connections. Conversely, bespoke slices (slice 0 and slice 2) provide more flexible configurations that can accommodate applications with medium  
Figure 13: Confusion matrix showing ONSSO prediction performance across slice categories. or varied performance needs. This tailored configuration ensures that each application receives the appropriate resources, aligning network capabilities with application demands and optimising performance across different use cases. The low-latency, high-bandwidth, and low-packet-loss characteristics of slice 1 correctly indicate the slice to be a uRLLC slice. Similarly, the high-latency characteristics compared to a medium-latency slice 0, the low bandwidth of slice 2 compared to the medium bandwidth requirement of slice 0, and the low throughput of slice 2 compared to the medium throughput of slice 0 all indicate that the resources required to orchestrate slice 0 will be more than the network function resources required for slice 2; hence, slice 2 will be cheaper to orchestrate than slice 0 and slice 1 will be the most expensive slice to orchestrate. On further evaluation of the results, as the framework orchestrates the best alternative slice fit based on available resources, it is possible that the one application that was predicted to use slice 2 instead of 0, and vice versa, can be the alternative allocated best fit at the time. Figure 14 demonstrates the output of our ML-based framework for determining the best fit slice and an alternative slice for various network applications (protocols). This is essential for efficient Network Function Chaining (NFC). The model, as shown in Figure 14 below, correctly chose slice 2 as an alternative best fit slice for applications requiring slice 1 in the event of limited available resources.