## 5 Experimental Evaluation  
The vast quantity of new technologies introduced simultaneously presents a strong research challenge in defining the method to accurately assess the E2E performance of the network when all technologies are interconnected. The combination of new frequencies, formats, and physical layer codes, edge computing, and virtualized network functions (VNFs/NFV) create an end-chain for potentially unpredictable interactions, which will require an efficient research methodology. To start proving the operational efficiency of our Orchestrator, we focused some experimental tests on the _Multi-Provider Network Slice Selector_, which are related to its _Collector_, _Processor_, and _Decision Maker_ modules. First, we defined the service demand scenario worked on in our experiment, according to the specification 3 GPP TS 22.186; R.5.4-006 (Performance requirements: extended sensors information sharing between UEs supporting V2X application under a higher degree of automation for an imminent collision scenario) in terms of Maximum E2E Latency, Reliability, Data rate, and Minimum required communication range, as described in Table 3[120]. The implementation of the simulation scenario was based on the OMNeT++ 6.0 (pre10/pre11) simulator 11, INET v4.3.2[121], and on the Simu5G framework v1.2.0[122]. The choice was due to simulating data forwarding using five UEs, two gNodeBs (5G RAN), 5G Core (5 GC), User Plane Function (UPF), Router, 5G-H Orchestrator, and services (Internet or public cloud), in addition to other features of the simulator and framework that could guarantee the detailed implementation in Figure 8. It should be noted that the mobility of nodes (UEs) was considered within the range intervals predefined by the 3 GPP TS 22.186; R.5.4-006 specification (Table 3), but that the experiment did not consider issues related to mobility management and handover. It was also considered that the traffic generated by the UEs was of the Constant Bit Rate (CBR) type and, therefore, subject to variations, inferred latency, and reliability due to packet loss. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Max E2 Latency** & **Reliability** & **Data Rate** & **Min Required** \\
**(ms)** & **(\%)** & **(Mbps)** & **Communication Range (m)** \\ \hline
10 & 99.99 & 1000 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance requirements: extended sensors information sharing between UEs supporting V2X application under a higher degree of automation for an imminent collision scenario. Based on [120]. By applying fuzzy logic, the mathematical technique that works with the theory of sets, motivated by the accurate output supplied from the raw data input, we calculated the degree of membership of the data for each linguistic input variable in each set using the triangular and trapezoidal membership functions with the inference of the Mamdani method over the generated result, as illustrated in Figure 9.  
The second step was establishing fuzzy rules for all cases and the application of the fuzzy inference system. As there are four input variables with three sets (Low, Medium, and High), there are 81 fuzzy rules. For the output, as seen in Figure 10, the output fuzzy sets are five: _bad_, _close to good_, _good_, _close to great_, and _great_. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter Description** & **Specified Value** \\ \hline Each Simulation Time & 50 s \\ Play Ground Size & 50 m, 800 m, and 1100 m \\ gnodeB Broadcast Message Interval & 0.5 s \\ FbPeriod & 40 \\ AmcType & NRAmc \\ Pilot Mode & ROBUST\_CQI \\ Target Bler & 0.01 \\ Bler Shift & 5 \\ Num Components Carriers & 1 \\ Num Bands & 25 \\ Mobility UE & 0 m (static) \\ Service Hosts Max Apps & 100 \\ Service Hosts Max Ram & 32 GB \\ Service Hosts Max Disk & 100 TB \\ Service Hosts Max Cpu Speed & 400,000 \\ UE Start Time & 1 s \\ UE Stop Time & 35 s \\ UE Num Apps & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters used in each one of 100 simulations per test. Figure 8: Simulation scenario.