## 5 Evaluated Algorithms
### Carbon- and Power-Efficient Optimal Frequency VM Placement (C-PEF)

Table 10: Power and carbon footprint with dynamic power usage effectiveness (PUE). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**C-FF**} & \multicolumn{2}{c}{**C-PE**} & \multicolumn{2}{c}{**C-PEF**} \\ \hline \multirow{2}{*}{**Metric**} & \begin{tabular}{c} **\%CPU** \\ **Utilization** \\ \end{tabular} & **Power (kW)** & \
\begin{tabular}{c} **\%CPU** \\ **Utilization** \\ \end{tabular} & **Power (kW)** &
\begin{tabular}{c} **\%CPU** \\ **Utilization** \\ \end{tabular} & **Power (kW)** \\ \hline Min & 0.1471 & 1029 & 0.1471 & 644.5 & 0.1471 & 969 \\ Max & 88.94 & \(5.823\times 10^{5}\) & 89.67 & \(6.283\times 10^{5}\) & 86.69 & \(5.644\times 10^{5}\) \\ Mean & 48.33 & \(3.854\times 10^{5}\) & 48.48 & \(4.156\times 10^{5}\) & 46.25 & \(3.74\times 10^{5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Statistical analysis with dynamic PUE. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Placement Algorithm** & **Power(kW)** & **Carbon Footprint (Tons)** & **Number of VMs Placed** \\ \hline C-PEF & 564,350.9 & 23.631227 & 1617 \\ C-PE & 628,328.4 & 24.191322 & 1636 \\ C-FFF & 582,335.1 & 24.35445 & 1641 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: RVA % with dynamic PUE. with some degree of minimum resource request constraint. In conclusion, C-FFF is a power-efficient algorithm for VM placement with reduced computational overhead. The formulations presented in this work open new and challenging areas of further research relating to renewable energy sources. Conceptualization, T.R. and K.G. ; methodology, T.R. and K.G. ; writing--original draft preparation, T.R. and K.G. ; supervision, K.G. and P.S. ; writing--review and editing, N.P. and P.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest. ## References

* [PERSON] et al. (2008) [PERSON]; [PERSON]; [PERSON]; [PERSON] _Cloud Computing and Grid Computing 360-Degree Compared_; Grid Computing Environments Workshop: Austin, TX, USA, 2008. [CrossRef]
* [PERSON] (2019) [PERSON] Demand Response Opportunities and Enabling Technologies for Datacenters: Findings from Field Studies. Available online: [[https://escholarship.org/uc/item/?bh6n6](https://escholarship.org/uc/item/?bh6n6) kt]([https://escholarship.org/uc/item/?bh6n6](https://escholarship.org/uc/item/?bh6n6) kt) (accessed on 9 December 2019). [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] Service Level Agreement Cloud Computing. 2009. Available online: [[https://corescholar.libraries.wright.edu/knoesis/78/](https://corescholar.libraries.wright.edu/knoesis/78/)]([https://corescholar.libraries.wright.edu/knoesis/78/](https://corescholar.libraries.wright.edu/knoesis/78/)) (accessed on 9 December 2019). * [PERSON] (2011) [PERSON] A measurement study of server utilization in public clouds. In Proceedings of the 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing, Sydney, Australia, 12-14 December 2011; pp. 435-442. [CrossRef]
* [PERSON] and [PERSON] (2015) [PERSON]; [PERSON] [PERSON] Server consolidation techniques in virtualized data centers: A survey. _IEEE Syst. J._**2015**, _11_, 772-783. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON] Cloud migration process--A survey, evaluation framework, and open challenges. _J. Syst. Softw._**2016**, _120_, 31-69. [CrossRef]
* [PERSON] et al. (2010) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Energy-efficient management of data center resources for cloud computing: A vision, architectural elements, and openchallenges. In Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2010), Las Vegas, NV, USA, 12-15 July 2010. * [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON]; [PERSON] An empirical investigation of the impact of server virtualization on energy efficiency for green data center. _Comput. J._**2013**, _56_, 977-990. [CrossRef]
* [PERSON] et al.