# Intelligent Resource Orchestration for 5G Edge Infrastructures
## 7 Results
### Resource Optimization Results
#### 7.3.1 Comparison of Different Objective Functions without Optional Constraints  
The initial experiments involved a comparison of the optimal allocation obtained with the three different objective functions: minimization of the number of servers in use, Equation (6); minimization of the total number of latency violations, Equation (12); and optimization of load balance among servers, Equation (9). In these experiments, these objective functions were used without any optional constraints (only strict constraints were considered, as explained in Section 6.2.4). It is important to note that since all edge servers have similar hardware configurations, the objective function that minimizes the total number of servers in use is equivalent to minimizing the total number of cores, Equation (7). We also compared these objective functions with the solution obtained without any optimization (i.e., allocating each VM to its preferred edge location). The results of these experiments are shown in Table 3 and Figure 8.  
Table 3 provides a summary of the allocation achieved for each objective function (cases 1, 2, and 3) and the solution obtained without optimization (case 4). In case 4, each VM was simply allocated to its preferred edge location. The table presents the total number of servers in use, the total number of latency violations, and the execution time of the ILP solver for each solution. On the other hand, Figure 8 depicts the estimated average daily load per core of each server based on the VM CPU usage predictions used in the optimization model, Equation (8), as well as the actual average daily load of each server (similar to Equation (8), but using real VM CPU usage values instead of predicted values). Additionally, the figure illustrates the maximum hourly actual load, representing the worst-case scenario (maximum peak load) across all servers and time intervals. Figure 7: Distribution of VMs according to the preferred edge location. As observed in Table 3, allocating VMs to their preferred edge locations without any optimization (case 4) resulted in no latency violations. However, this approach led to significant server overloading, as depicted in Figure 8 (case 4). Servers 1, 2, and 3 exhibited actual average loads exceeding 100%, with a maximum peak load surpassing 140%. Upon implementing optimization (cases 1, 2, and 3), regardless of the chosen objective function, the strict constraint specified in Equation (17) (with \(\alpha=1\)) effectively prevented overloading. Consequently, the actual average load remained below 100% for all three cases and the maximum peak load approached 100%. When comparing the results of the different objective functions, optimizing the number of latency violations (case 2) allocated only 29 out of 500 VMs to an edge location different from their preferred one, utilizing the 12 available servers. On the other hand, when minimizing the number of servers in use (case 1), the solution obtained utilized only nine servers instead of 12. However, this resulted in a significant number of latency violations, with 389 out of 500 VMs affected. These minimum values (29 latency violations and nine servers) represent the global minima for these two optimization problems. Hence, solutions with fewer than nine servers or fewer than 29 latency violations are not considered feasible. Regarding the load profiles in Figure 8, for these two cases, although the average daily load per core did not exceed 100% for any server, there was a noticeable imbalance in the load across the different servers, and the peak load slightly exceeded 100%. The load balancing objective function (case 3) addressed this issue by utilizing all 12 available servers and incurring a larger number of latency violations (432). However, it achieved a better distribution of the load among the different servers and successfully avoided overloading in all time slots. Finally, regarding the execution times of the ILP solver, it can be observed that,  
Figure 8: Estimated vs. actual load.