## 4 Performance Evaluation of GASM Algorithms over Cloud/MEC RAN Architecture
### Using GASM in a Reconfigurable Scenario  
When GASM or Evolutive GASM is used to reconfigure the VNF provisioning, the time is divided into time slots and GASM is launched at the beginning of each slot. The duration of the time slot is set to 900 s as it is the one that offers the best results in terms of service blocking rate and CPU usage (not shown due to the lack of space).  
In the following studies, we compare the performance of static planning (with \(k=1.5\) and \(k=2\)) with the provisioning obtained using GASM and Evolutive GASM when a reconfiguration is allowed. In the static planning, GASM method evolves during 100 generations as the planning is done off-line and no time restrictions are required, but with reconfiguration methods, only 10 generations are created to cut down the execution time. Note that the computing time must be lower than the duration of a time slot. MEC-First and CO-First are launched online, therefore, they work with real traffic and they add/delete VNF as required similarly as it is done in References [32; 33].  
Figure 6a shows the global service blocking ratio depending on the average number of users in each HD-AO. Figure 6b shows the corresponding percentage of CPU core saving respect to static planning with \(k=2\). The corresponding execution times are shown in Figure 7.  
Figure 6a shows that making a static planning leads to a reduction in the network performance. GASM \(k=2\) is the algorithm with the highest percentage of CPU usage and its service blocking rate is one of the highest. This happens as the VNF planning is over-dimensioned and it is not properly designed. When \(k=1.5\) better results can be obtained, but it is worse than those obtained by basic GASM with reconfiguration. On the other hand, CO-First (which does not exploit edge computing capabilities) presents the highest values of service blocking rates. Hence, not only does the use of MEC  
Figure 5: Execution times for GASM with \(k=1,\;k=1.5\) and \(k=2\).  
help to reduce the delay, but it also improves the performance of the network. Although MEC-First obtains good results in terms of service blocking rate, Figure 6b shows that the algorithm makes a higher use of CPU cores in comparison to other methods, particularly for the lower number of average users per AO.  
Additionally, the evolutive method obtains the best results in terms of service blocking rate: Almost an order of magnitude when compared to MEC-First and the static version with the optimal configuration (\(k=1.5\)). Furthermore, when compared to basic GASM when reconfiguration is allowed, the service blocking ratio is reduced in almost two orders of magnitude, showing the effectiveness of the learning step added to the algorithm. Evolutive GASM also presents a good performance in terms of IT resources, outperforming MEC-First and GASM for \(k=1.5\) and reducing the percentage of CPU cores in use almost in 10%.  
In terms of execution times (Figure 7), MEC-First and CO-First are capable of solving all the service requests in less than 1 s while GASM methods require more time to perform the planning (around 20 s more time). In any case, the time required is much lower than the time slot duration of 900 s and can be used with the time slot used in the study.  
Figure 6: (**a**) Service blocking rate and (**b**) Percentage of the CPU Cores in use for the proposed methods. The results obtained when reconfiguration and online is performed are indicated with full symbols.  
Figure 7: Execution times for basic GASM, evolutive GASM, MEC First and CO First for \(\phi=0.5\).