## 4 Multi-Objective Hybrid Resource Consolidation Algorithm
### Hybrid Flower Pollination Resource Consolidation Algorithm
#### 4.2.5 Implementation of Hybrid Resource Consolidation Algorithm
To achieve optimal resource consolidation, the MOH-FPRC algorithm incorporated the Pareto optimization strategy, DC strategy, resource monitoring, VM and PM selection, and a transition state. This resulted in efficient and reliable resource consolidation for CC data centers with minimum energy consumption and fewer SLA violations. The DC strategy maps VM requests based on their application requirement to assist in finding the appropriate cluster among the clusters that minimize the energy consumption and reduce the complexity that lead to local entrapment avoidance. The avoidance of entrapment is the result of the LNS strategy integrated into the FPA algorithm. The MOH-FPRC algorithm optimizes the allocation of VMs, monitors the performance of VM and PM, migrates VMs, and decides on the transition state of the resources. Furthermore, the proposed algorithm reduces the migration requirement of VMs to PMs, thereby making it robust and energy-efficient. Therefore, the proposed algorithm can effectively save energy consumption of the Cloud datacenter while reducing SLA violation rates compared to the benchmark algorithms. The simulation results significantly show the performance of MOH-FPRC as compared to similar nature-inspired consolidation-based algorithms using different performance evaluation metrics. However, the article also reveals a gap between Cloud user requirements and the IaaS provider in terms of resource management. Overall, utilizing CDC resource management techniques to build a more credible green data center with Fog computing is a promising research direction. Figure 9: Comparison of algorithms using MAD migration strategy. **Author Contributions:** Conceptualization, M.J.U. methodology, M.J.U., A.A., L.A.G. and D.G. ; formal analysis, H.C.; investigation, L.A.G., D.G. and H.C.; resources, M.J.U., D.G. and H.C.; data curation, M.J.U. ; writing--original draft preparation, M.J.U., A.A., H.C.; writing--review and editing, D.G. and H.C.; visualization, M.J.U., A.A. and L.A.G. All authors have read and agreed to the published version of the manuscript. **Funding:** Funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R178), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. **Institutional Review Board Statement:** Not applicable to our study. **Informed Consent Statement:** The study does not involve human subjects. **Data Availability Statement:** The data is freely available online: [[https://github.com/beloglazov/planetlab-workload-traces](https://github.com/beloglazov/planetlab-workload-traces)]([https://github.com/beloglazov/planetlab-workload-traces](https://github.com/beloglazov/planetlab-workload-traces)) (access on 4 March 2018). Simulator: IntelliJ IDEA release version 3.4. **Acknowledgments:** This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R178), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. **Conflicts of Interest:** The authors declare that there is no any conflict of interest.