## 5 Experimental Evaluation
In Appendix A, Tables A1 and A2 show the set of rules used in the experiment. The application is based on the fuzzy logic rule in which the fuzzy operator AND equals the minimum operator and the operator OR equals the maximum operator. Thus, it is possible to calculate the membership function of the latter. For example, apply the rules to the _bad_ output set. * If E2E Latency is High and Reliability is Low and Data Rate is Low and Communication Range is Low, then discourse universe (mos) is bad;
* If E2E Latency is High and Reliability is Low and Data Rate is Low and Communication Range is Medium, mos is bad;
* If E2E Latency is High and Reliability is Low and Data Rate is Medium and Communication Range is Low, mos is bad;
* If E2E Latency is High and Reliability is Low and Data Rate is Medium and Communication Range is Medium, mos is bad;
* If E2E Latency is High and Reliability is Medium and Data Rate is Low and Communication Range is Low, mos is bad;  
Figure 10: Defuzzification process. Figure 9: Fuzzification process - Degree of membership functions from the chosen linguistic variables: (a) Data Rate, (b) E2E Latency, (c) Communication Range, and (d) Reliability. * If E2E Latency is High and Reliability is Medium and Data Rate is Low and Communication Range is Medium, mos is bad;
* If E2E Latency is High and Reliability is Medium and Data Rate is Medium and Communication Range is Low, mos is bad;
* If E2E Latency is Medium and Reliability is Low and Data Rate is Low and Communication Range is Low, mos is bad. Thus, as there is an alternation between these eight cases, the diffuse operator OR must be used, that is, making the maximum of the minimum values already acquired. Finally, there is the value of the degree of membership of the result (in this case, of the _bad_ set). For the other sets, the method is the same; the fuzzy rules of each are checked, the necessary membership degrees are calculated and the fuzzy operators AND and OR are applied. Finally, the defuzzification process transforms the result obtained from the fuzzy system, the fuzzy aggregated set, into a crisp number from 0 to 5--the linguistic variable called _mos_, as illustrated in Figure 10. The one chosen in this experiment was the maximum value, which can be applied through Equation (1):  
\[x_{0}=\frac{\sum_{i=1}^{m}h_{i}x_{i}}{\sum_{i=1}^{m}h_{i}} \tag{1}\]  
where \(x_{0}\) is the output crisp value, \(m\) is the number of output fuzzy sets, \(h_{i}\) is the membership degree of each calculated output fuzzy set, and \(x_{i}\) is the center point of the set. The sets _bad_ and _good_ were considered in the interval \(]0,2]\) and [4, 5[], respectively. Once the fuzzy system was built, a simulation was carried out in which the best slice was selected in each of the 10 tests. In each test, there are predefined intervals for the variation of each slice in each criterion. For example, the E2E Latency of slice 01 has the range of 1 ms to 13 ms for the first test, whereas the Reliability of slice 02 has the range of 99,985% to 99,930% for the second test. Details about the simulation parameters and all four criteria for the three slices of the ten tests have a predefined and in a modifiable range, as shown in Table 5.  
As shown in Figure 8, the UPF segments the traffic (traffic steering) coming from the UEs into three slices according to the interval of the evaluated QoS variables (vide Table 5). Then, the traffic already marked (target) is forwarded by the vrouter to the slice selector, which performs the fuzzification and defuzzification processes on the attributes and, finally, the slice selection. From there, the 5G-H Orchestrator reserves and allocates the necessary resources defined in the slice and forwards the packets to the respective service providers, which, in this scenario, are represented by hosts Service 1 and Service 2. In each test, there are 100 (one hundred) simulations. In each simulation, values and criteria of each slice are drawn according to the pre-established intervals.