# Stochastic Model Driven Performance and Availability Planning for a Mobile Edge Computing System
## 5 Evaluation of MEC Availability
### Discussions  
_Performance Evaluation:_ To comprehensively assess the performance of the underlying MEC architecture, we proposed an SPN model which allows estimating MRT and the level of resource utilization at the edge of the network. Ones may configure up to 5 input parameters, which allows a high level of evaluation flexibility. A numerical analysis was performed using real data from a reference paper to feed parameters in the proposed model. The numerical analysis helps investigate the behavior of four metrics (MRT, discard rate, master node utilization, and slave nodes utilization) as a function of the arrival delay. As per observed, the arrival delay is the parameter that exposes a clearly significant impact on the system's performance compared to the remaining parameters.  
A refined model with an absorbing state was developed to explore when applications are most likely to complete their execution through the use of CDF. Two case-studies were conducted to demonstrate the use of this model. In the first case-study, a verification was performed to see which configuration of a slave server best meets the requirements of an infrastructure administrator, according to the total time desired for the application execution. The finding of this case-study is that the total time for execution increases as resources decrease. In the second case-study, we observed the probability of execution based on the time between arrivals. We found that as the time between arrivals increases, the total time required to complete application execution also grows. The performance model was validated with a real experiment, the results of which indicated equality between experiment and model with \(p\)-value equal to 0.684 by \(t\)-Test.  
_Availability Assessment:_ An SPN model was also developed to represent and evaluate the availability of an underlying MEC architecture. It is feasible to analyze which were the most important components of the system based on the proposed model. Furthermore,  
Figure 17: Results (**a**–**c**) show the three most important transitions in the base model; Results (**d**–**f**) present the results of the sensitivity analysis of the most important transitions in the extended model.  
thus, it is also possible to propose an extensional architecture focusing on these components. Scenarios with different quantities of containers were analyzed. As per investigated, the extended architecture shows a considerable improvement in availability compared to the base architecture. The containers were exposed to be the significantly important components in both models, and that a server with distributed responsibility is not always the bottleneck of the system. The results of the extended model, different from the base model, always remain above 99%, since it presents redundancy in the components that were impacting availability in the base model.  
Regarding the performance evaluation, we intend to perform other numerical analyzes, adding more servers or considering different types of applications. We also intend to extend the proposed model to measure energy expenditure and explore allocation between multiple MEC towers. On the other hand, the availability model can be extended by taking into account redundancy of the components in the _master server_, and the consideration of different operational scenarios for testing the models is also an essential extension.