## 6 Results and Discussion  
From the _Collector_ and _Processor_ modules of the _Multi-Provider Network Slice Selector_, Figure 11 presents the results from the variation of the Data Rate, Communication Range, E2E Latency, and Reliability for each test performed to support the decision of NS selection.  
To support the data analysis, there is the percentage of iterations in which some slices were considered better than the others. Table 6 shows this percentage of choice for each of the slices evaluated in each test performed. For example, in the first test, slice 01 obtained 73%, slice 02 23%, and slice 03 04%; thus, in this test, slice 1 was considered the best since it obtained the highest percentage.  
For the set of 10 tests performed, the mean, standard deviation (SD), variance (VAR), and confidence interval for the mean (CI) are calculated at a significance level of 95%. Note that each test consists of 100 iterations and follows the defined configurations used in the set of rules for the fuzzification process.  
After obtaining the data resulting from the fuzzy selection process, a descriptive analysis was performed in order to verify if there are significant differences in the performance of the slices for the set of tests performed. The experiment primarily consists of a comparative analysis using [PERSON]'s test of multiple comparisons of means from the VAR analysis. In addition, the Shapiro-Wilk normality, Durbin-Watson independence, and Fligner-Killeen homoscedasticity tests were applied.  
From the analysis of the test results and the Tukey test, it was observed that the selection means between the slices do not differ significantly, as illustrated in Figure 12. Another point refers to the results from the Shapiro-Wilk normalization test that describes a normal distribution of the samples. By the Durbin-Watson test, it can be stated with 95% confidence that the residuals are not independent. For the Fligner-Killeen test, it was found that the samples have homoscedasticity of variances.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Test** & **Slice 01 (\%)** & **Slice 02 (\%)** & **Slice 03 (\%)** \\ \hline
1 & 73 & 23 & 4 \\
2 & 78 & 6 & 16 \\
3 & 64 & 24 & 12 \\
4 & 53 & 11 & 36 \\
5 & 22 & 51 & 27 \\
6 & 31 & 20 & 49 \\
7 & 20 & 35 & 45 \\
8 & 26 & 46 & 28 \\
9 & 23 & 24 & 53 \\
10 & 42 & 30 & 28 \\ \hline Mean & 43.2 & 27 & 29.8 \\ VAR & 496.62 & 198.88 & 262.62 \\ SD & 22.28 & 14.10 & 16.20 \\ CI & 27.25–59.14 & 16.91–37.08 & 18.20–41.39 \\ \hline \end{tabular}
\end{table}
Table 6: Percentage of choice for each of the slices evaluated in each test performed.  
Figure 13 consolidates the results presented in Table 6. Note that, from test 5, slice 01 has its proportion of choice lower than the other slices. In general, this behavior occurs due to the increase in E2E Latency and the consequent decrease in slice reliability. Anyway, from the processing performed, the best choice is slice 01, slice 03, and slice 02, respectively. However, for better understanding the slice selector decision process, it is necessary to verify the criteria values for each of the slices throughout the tests performed using fuzzy logic.  
Figure 11: Variation of: (**a**) Data Rate, (**b**) E2E Latency, (**c**) Communication Range, and (**d**) Reliability.  
Figure 12: Tukey test.