## 4 Experimental Results
### Accuracy in Terms of Regression  
Figures 7-9 show the regression plots for the firewall, OVS, and Snort VNFs, respectively, in the three ML models considered in this work. On the x-axis, we have a target which is the desired output, i.e., the real CPU consumption calculated through experiment. On the y-axis, we have the predicted output calculated by the ML models. The figure also provides the correlation factor \(R\), according to Equation (13), as described in Section 2.4, between the actual outputs and the predictions of each model. A correlation factor of \(R=1\) shows 100% prediction accuracy, in which case the linear fit (solid line) of the ML model becomes aligned with diagonal (dotted line) labelled Y = T, i.e., prediction equals target value. Figure 7: Firewall regression plot: (**a**) MLR; (**b**) SVR; and (**c**) FNN. For the firewall VNF in Figure 7, the most accurate predictions are delivered by the SVR technique. This is evident from the correlation factor approaching 1 and the linear fit almost aligned with diagonal best fit. Predictions from the MLR model are slightly worse with \(R=0.97259\). Surprisingly, predictions from the FNN are the least accurate among the considered models, although FNN is a more sophisticated tool. One possible reason for the low accuracy of FNN predictions might be the data set size of the firewall VNF. The set of 755 data points for the firewall function might be too small for the single-layer FNN. Improvement in the prediction accuracy of the FNN in the following VNFs with larger data set sizes provides support to this conjecture. Figure 8 shows the regression plots for the Open vSwitch VNF. The SVR model produces the most accurate predictions with correlation factor \(R=0.99629\), while the FNN produces the least accurate predictions. In terms of the correlation factor, the predictions of the SVR are equally accurate as in the case of firewall VNF. OVS predictions of the MLR model exhibit a degradation compared to those of the firewall. The accuracy of the OVS predictions from FNN show a considerable improvement with respect to the firewall case. Finally, Figure 9 presents the regression plots for the Snort VNF. Here, again, the SVR produces the most accurate predictions followed, in decreasing order of accuracy, by MLR and FNN. It is interesting to note that the accuracy of the FNN predictions improves significantly for OVS and Snort functions with larger data sets of 1172 and 1359 data points, respectively. The prediction accuracy of the SVR model remains practically the same for the three VNFs. On the other hand, prediction accuracy of the MLR degrades slightly for the OVS and Snort network functions. Figures 7-9 demonstrate that the SVR produces  
Figure 8: Open vSwitch regression plot: (**a**) MLR; (**b**) SVR; and (**c**) FNN. Figure 9: Snort regression plot: (**a**) MLR; (**b**) SVR; and (**c**) FNN. First, Figure 10 shows prediction accuracy of MLR for the three VNFs. It can be seen in the figure that the percentage error in the predictions of MLR is limited to 8% most of the time. For a desired percentage error of up to 8%, prediction accuracy of MLR is very close to each other for the three VNFs, with the accuracy for firewall being slightly better. This is in agreement with the prediction accuracy indicated by correlation factor in results of Figures 7-9. However, the prediction accuracy for the three VNFs is not the same if the desired prediction accuracy is higher. For example, if prediction error of up to 2% is admissible, then MLR predictions are accurate 60% of the time for the OVS function, while those for the Snort function are accurate approximately 45% of the time. Alternatively, if the 80 th percentile is observed, MLR predictions for Snort contain up to 4% errors, while those for OVS up to almost 3% errors. Figure 11 shows the CDFs of percentage errors in predictions for the firewall, OVS, and Snort using the SVR model.