## 4 Multi-Objective Hybrid Resource Consolidation Algorithm
### Hybrid Flower Pollination Resource Consolidation Algorithm
#### 4.2.5 Implementation of Hybrid Resource Consolidation Algorithm
 \\ \hline
10 & 900.021 & 0.00 & 1275.25 & 0.21 & 1370.59 & 0.22 \\ \hline
20 & 1115.50 & 0.01 & 1427.05 & 0.25 & 1522.36 & 0.25 \\ \hline
30 & 1420.11 & 0.15 & 1845.32 & 0.34 & 1940.21 & 0.32 \\ \hline
40 & 1950.01 & 0.20 & 2375.84 & 0.35 & 2470.58 & 0.35 \\ \hline
50 & 2350 The MOH-FPRC algorithm incorporated _VM_ migration from overloaded PMs to avoid SLA violation as shown in Figures 7-9 under different migration strategies. The results produced by the benchmarking algorithms, namely MOACS and ACS-VMC, caused a higher number of migrations for resource consolidation as compared with the proposed MOH-FPRC algorithm. However, MOACS migrated VMs 1405 times, while the ACS-VMC did so 1400 times compared with the proposed MOH-FPRC algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MOH-FPRC** & **ACS-VMC** & **MOACS** \\ \hline \multirow{2}{*}{Total average energy consumption and SLA violation} & 5150.42 & 5925.64 & 6100.07 \\ \cline{2-4}  & 0.43 & 0.50 & 0.50 \\ \hline PI over ACS-VMC and MOACS (kWh) & – & 14.54\% & 29.48\% \\ \hline PI over ACS-VMC and MOACS (SLA violation) & – & 13.57\% & 13.57\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MOH-FPRC performance improvement on energy consumption and SLA violation. Figure 6: Performance comparison of PM Energy consumption, SLA violation and utilization. \begin{table} \begin{tabular}{c} **ST** \\ **Number of** \\ **Migration** \\ \end{tabular}  \\ \hline
100 & 625 & 287 & 69 & 122 & 300 & 210 & 420 & 323 & 400 & 239 \\ \hline
200 & 1250 & 520 & 114 & 145 & 535 & 458 & 810 & 540 & 800 & 466 \\ \hline
300 &



This shows the reason why their energy consumption level (MOACS and ACS-VMC) increased significantly with SLA violation. Furthermore, the results indicate that, when the number of PMs increases due to the user request of VMs, the proposed MOH-FPRC algorithm performs better at minimizing the total energy consumption with SLA violation simultaneously as compared with the benchmarked algorithms. The main reason for that is the proposed algorithm employs a strategy to avoid migration that results in over-utilization of the destination PM. The total number of migrations by the proposed algorithm is less than that of the MOACS and ACS-VMC algorithm because of the use of DC and different migration strategies suitable for the Cloud data center resource management. More interestingly, there is a difference between the three migration strategies (ST, IQR, and MAD) as presented in Figures 7-9 by more than 30% higher due to the dependency of the MC strategy on upper and lower thresholds to perform the _VM_ migration. This leads to the strategy migrating more VMs than the MMT strategy. Similarly, the MAD strategy remains the highest strategy in terms of VMs migration despite the availability of the PMs. Figure 8: Comparison of algorithms using ST migration strategy. Figure 7: Comparison of number of _VM_ migration using IQR. As can be observed from the above-mentioned figures, the performance improvements are a result of the incorporation of the LNS strategy in the local search of the conventional FPA, which significantly improved the convergence of underlying MOH-FPRC that avoid local entrapment. Therefore, the use of the LNS and DC methods efficiently enhances the search performance to obtain better non-dominated solutions. In this way, the data center resource management has been improved in terms of energy efficiency and SLA violation, and the impact on the data center has been equally improved. This proves that MOH-FPRC is an effective and efficient solution method for solving large-scale resource consolidation optimization problems. ## 6 Conclusions

In this article, we presented a MOH-FPRC algorithm. The proposed algorithm addressed conflicting objectives between energy consumption and SLA violation in the context of large-scale data centers to improve resource management.