## 4 Multi-Objective Hybrid Resource Consolidation Algorithm
### Hybrid Flower Pollination Resource Consolidation Algorithm
#### 4.2.5 Implementation of Hybrid Resource Consolidation Algorithm
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & \multicolumn{2}{c}{**MOH-FPRC**} & \multicolumn{2}{c}{**ACS-VMC**} & \multicolumn{2}{c}{**MOACS**} \\ \hline \multirow{2}{*}{**VM Request**} & **Energy** & \multirow{2}{*}{**SLA Violation**} & **Energy** & \multirow{2}{*}{**SLA Violation**} & **Energy** & \multirow{2}{*}{**Consumption**} & **SLA Violation** \\  & **Consumption** & **\%** & & **(kWh)** & **\%** & **(kWh)** & **\%** \\ \hline
500 & 1089.25 & 0.00 & 1489.35 & 0.201 & 1589.45 & 0.220 \\ \hline
1000 & 1304.05 & 0.00 &



Furthermore, as depicted in Table 3, the MOH-FPRC algorithm shows improvement in terms of PM utilization which leads to minimum energy consumption and less SLA violation after consolidation. In the table, the values indicate the energy consumption and SLA violation of the PM utilization of the tested workload. The data center energy consumption and the SLA violation of different algorithms is shown in Figure 6. The MOACS algorithm consumes higher energy with greater SLA violation compared with the ACS-VMC algorithm. However, the ACS-VMC consolidation algorithm seems more promising than the MOACS in terms of SLA violation and the amount of energy consumption. Moreover, the performance of the MOH-FPRC consolidation algorithm is outstanding at each increasing PM interval. The obtained energy consumption level by the MOH-FPRC is minimal compared to the benchmarked algorithms. This can also be seen in Table 4, which depicts the performance improvement (PI) of the MOH-FPRC algorithm as 14.54% and 29.48% better than the MOACS and ACS-VMC algorithms, respectively. The MOH-FPRC algorithm achieved the optimal balance in multiple conflicting objectives using the positive feedback mechanism and the constantly updated monitoring of resources, which effectively reduced the energy consumption and minimized violation of SLA. Likewise, the table shows a performance improvement of 13.57% for both MOACS and ACS-VMC algorithms in terms of SLA violation. Similarly, MOH-FPRC provides minimum SLA violation compared to the ACS-VMC and MOACS consolidation algorithms, as stated above. The reason is that the incorporation of the Pareto Optimization strategy provides better options for selecting PMs with less energy consumption within the proposed algorithm, helping to reduce the level of energy consumption and avoid high SLA violation. The VM selection and PM allocation policies within the proposed algorithm help facilitate the choosing of VMs for migration out from the overloaded PMs and selecting new PMs for the VMs to be migrated as shown in Table 3. The migration strategies used in this research by the MOH-FPRC and benchmarking algorithms are Static Threshold (ST) and Inter Quartile Range (IQR), and the strategy that returned the same performance in terms of the number of migrations and dynamic VM allocation strategies based on Median Absolute Deviation (MAD). Table 5 shows the results generated due to the migration of VM resources using different strategies with different numbers of VMs at the interval of 625 VMs. The table reveals more details of the performance of the proposed algorithm on _VM_ migration. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & \multicolumn{2}{c}{**MOH-FPRC**} & \multicolumn{2}{c}{**ACS-VMC**} & \multicolumn{2}{c}{**MOACS**} \\ \hline
**PM Utilization** & \begin{tabular}{c} **Energy** \\ **Consumption** \\ **(kWh)** \\ \end{tabular}  & \begin{tabular}{c} **SLA Violation** \\ **\%** \\ \end{tabular}  & \begin{tabular}{c} **Energy** \\ **Consumption** \\ **(kWh)** \\ \end{tabular}  & \begin{tabular}{c} **SLA Violation** \\ **\%** \\ \end{tabular}  & \ \begin{tabular}{c} **Energy** \\ **Consumption** \\ **(kWh)** \\ \end{tabular}  & \begin{tabular}{c} **SLA Violation** \\ **\%** \\ \end{tabular}