# A Novel Radio Network Information Service (RNIS) to MEC Framework in B5G Networks
## 4 Performance Evaluation and Results
### Environment Setting  
We have considered an environment with two bare-metal machines with different configurations: (i) an Intel Core i5-10300H (CPU @ 2.50 GHz octa-core and 8 GB of RAM, Intel, Santa Clara, CA, USA ) and (ii) an Intel Core i7-10510U (CPU @ 1.80 GHz octa-core and 32 GB of RAM, Intel, Santa Clara, CA, USA). Initially, we planned to integrate the RNIS as a service with the MEC framework EALTEdge, but due to instabilities in the framework, we opted to conduct the evaluation in a bare-metal environment leaving this task as future work.  
Looking at the evaluated environments, the Intel(r) Core(tm) i5-10300H and Intel(r) Core(tm) i7-10510U processors differ mainly in performance and energy efficiency. The i5-10300H offers better performance in intensive computing tasks, while the i7-10510U is more energy efficient. The i7-10510U also has a significantly higher RAM capacity, with 32 GB compared to the i5-10300H's 8 GB.  
The scenario includes the use of three components: Locust, RNIS and MEC app. Locust is an open-source tool known for its simplicity and effectiveness in load evaluation. It simulates user interactions, allowing you to control the frequency and quantity of requests. RNIS receives, processes and forwards data to MEC apps. MEC apps subscribe to RNIS topics to receive notifications. Evaluation will be categorized based on the number of users, execution time and number of MEC apps subscribed (number of customers). Data such as the number of requests, processing time and receipt of information to MEC apps will be analyzed. The evaluated methodology includes sending requests every second, lasting 5, 10 and 20 min, involving different number of users: 100, 200, 400, 600, 800, 1000, 1500 and 2000. Each figure shows a specific number of users and the number of MEC apps consuming information, with two, four, six and eight MEC apps for each group of users mentioned above. By analyzing the graphs, one can see how the number of users and the number of MEC apps influences the sharing of the system, with interesting combinations for use. It is worth noting that the greater the number of users, the greater the amount of information being sent to the RNIS, where each user represents a UE with various pieces of information. The more types of UE connected to the network, the more information will be sent to the RNIS.  
In order to evaluate our proposed service, Radio Access Bearer (RAB) and Public Land Mobile Network (PLMN) messages were sent simultaneously in all instances. With a higher priority for sending PLMN-type messages compared to RAB, Locust allows several types of information to be sent simultaneously, with variations in priority using weights for each type of message. In this case, it was decided to prioritize the sending of PLMN-type messages, which implies a greater amount of information of this type compared to the RAB, obtaining different amounts of messages for analysis. The data analyzed in these evaluations are: (i) average number of requests sent vs. average requests served and requests not served (calculated as a percentage relative to the total sent); (ii) average processing time for requests; (iii) RNIS as a service processing time (total time minus external time); and (iv) average time to reach MEC app.  
All results were run ten times for five minutes each in order to compute the 95% confidence interval. It is important to highlight that several periods of 5, 10, 15, 20, and 30 min were evaluated, none of which impacted the results.