# A Novel Radio Network Information Service (RNIS) to MEC Framework in B5G Networks
## 4 Performance Evaluation and Results
### Results  
This section presents the results representing different scenarios in two different environments as mentioned before, i.e., i5 and i7. Figures 5 and 6 show the results of two, four, six and eight MEC apps (i5 vs. i7) for RAB messages regarding the average number of requests sent vs. the average requests served and requests not served considering different numbers of users (100, 200, 400, 600, 800, 1000, 1500 and 2000). In the intervals of 100 to 200 users sending information every 1 s (Figures 5-8), it was possible to send an average of 13,591.3 requests with 100 users and 12,744.1 requests with 200 users, being RAB-type requests. In addition, it was possible to send an average of 20,240.7 requests with 100 users and 24,755.9 requests with 200 users, being PLMN-type requests, simultaneously, with no unanswered requests. It is worth mentioning that the number of requests can change depending on the resources the implementation has. In the study carried out, the machine equipped with the i5 processor achieved a better result. On the other hand, the i7 achieved 7767.6 requests with 100 users and 7111.95 requests with 200 users, being of the RAB type. In addition, it recorded 15,669.6 requests with 100 users and 14,446.1 requests with 200 users of the PLMN type, simultaneously, also without recording any unfulfilled requests. With 400 simultaneous users, sending information at one-second intervals, it was clear that RNIS did not meet all the requests. It was observed that, from this scenario onwards, RNIS receives more requests, but is unable to meet all the demands. As the number of  
Figure 8: Requests PLMN i7—two, four, six and eight MEC apps. Figure 7: Requests PLMN i5—two, four, six and eight MEC apps. requests increases, there is a significant increase in the number of unfulfilled requests and a corresponding decrease in fulfilled requests. Figures 9 and 10 show the results of two, four, six and eight MEC apps (i5 vs. i7) for RAB messages regarding average processing time for requested vs. RNIS as a service processing time and time to reach MEC app considering different numbers of users (100, 200, 400, 600, 800, 1000, 1500 and 2000). Figures 11 and 12 show the results of two, four, six and eight MEC apps (i5 vs. i  
Figures 9-12 show that the best scenario is the environment with two consumer MEC apps connected, regardless of the execution machine, i5 or i7. On the i5 machine, when dealing with information of the RAB type, the highest time reached approximately 5.164 ms of internal processing and 7.334 ms of processing up to the MEC app. With regard to PLMN-type information, which is predominantly sent to the RNIS, the highest processing time was 7.184 ms internally and 9.234 ms up to the MEC app. On the i7 machine, when dealing with RAB-type information, the highest time was approximately 8.442 ms of internal processing and 13.399 ms of processing up to the MEC app. With regard to PLMN-type information, which is predominantly sent to RNIS, the highest processing time was 10.167 ms internally and 14.814 ms up to the MEC app. Looking at Figures 9-12, we can see that in the scenarios with six and eight MEC apps, there is a significant increase in processing times, showing the performance of RNIS in scenarios with fewer MEC apps. In both scenarios with six MEC apps, it can be seen that the time needed to process the information and deliver it to the end user is more than 12 ms  
Figure 11: Average time PLMN is—two, four, six and eight MEC apps. Figure 12: Average time PLMN in —two, four, six and eight MEC apps. of internal processing. Since, with the addition of MEC apps that consume information, the service faces greater difficulties, it consumes more resources to manage all the requests and requires longer processing times. It is interesting to note that as the service has more connected users providing information, the number of unanswered requests increases. The fewer requests there are to process, the more quickly and efficiently the system is able to handle them.