# Reducing the Operational Cost of Cloud Data Centers through Renewable Energy
## 6 Conclusions  
We consider EcoMultiCloud, a flexible load management tool that implements multi-objective load management strategies, and we adapt it to the presence of renewable energy sources to power data centers. Our work aims at achieving cost reduction in the load allocation process in a multi-data center scenario, where VMs are assigned to a given data center by considering both energy cost variations and the presence of local renewable energy production, in order to reduce the energy bill. Performance is investigated for a specific infrastructure consisting of four data centers. Results show that geographical data centers can significantly benefit from renewable energy sources when a smart load allocation strategy is implemented.  
Our results show that more than 60% cost can be saved even with a limited size of PV panels, with energy bill reduction as high as almost 100% under proper dimensioning of the PV panel and battery capacity. Scaling up to realistic scenarios, where data centers are made up of thousands of servers each, this means that the energy cost-saving can be of the order of several million dollars per year. Furthermore, combining the use of RE with a dynamic workload distribution by means of the application of migration policies allows further reduction of the energy bill by up to 17%, even without any storage and regardless of the type of adopted strategy. With large RE generators, the introduction of migration policies becomes less beneficial. Hence, the paper proves the feasibility of the introduction of renewable energy to reduce the operational costs of a complex infrastructure such as cloud data centers with geographically-distributed sites. Renewable energy can be effectively introduced given that a smart workload management is implemented; an approach like EcoMultiCloud is suited for this purpose.  
Our results also highlight how the selection of the DCs in which the PV panels are installed is critical and different cost savings can be obtained, depending on the variable energy prices, the local RE production level and the PUE of each DC. In addition, given the same initial investment for a RE generator with a given capacity, although it is not necessary to equip all the DCs with some PV panels, it is more convenient to distribute the installation of PV panels on a fraction of DCs, rather than consolidating the RE generation capacity on a single DC. Future work is required to investigate whether cost-saving might further benefit from the implementation of load management strategies that consider also the forecast future RE production to perform the VM assignment.  
Conceptualization, methodology, C.M. and M.M.; software, D.L. and D.R.; validation, formal analysis, investigation, resources, data curation, D.L., C.M., M.M. and D.R.; writing--original draft preparation, M.M.; writing--review and editing, D.L., C.M., M.M. and D.R.; visualization, D.R.; supervision, project administration, C.M. and M.M.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\(N_{DC}\)Number of DCs  
\(f^{i}_{assign}\)Assignment function for DC \(i\)  
\(F_{i}\) (\(F_{max}\))Key performance indicator for DC \(i\) (max among the DCs)  
\(M\)Number of key performance indicators  
\(C_{i}\)Hourly energy consumption for unit of workload in DC \(i\)  
\(E_{i}\)Available RE in DC \(i\) at a given time slot  
\(P_{i}\)Current hourly price of energy from the power grid in DC \(i\)  
\(P_{i,t}\)Hourly price of energy from the power grid in DC \(i\) at time slot \(t\)  
\(a_{i}\)Weights of the terms considered in the \(f_{assign}\)  
\(PUE_{i}\)Power Usage Effectiveness for DC \(i\)  
\(\Lambda\)Total workload  
\(U_{T_{i}}\)Maximum allowed workload in DC \(i\)  
\(\lambda\) (\(\lambda_{day}\), \(\lambda_{night}\))Arrival rate of VMs (during daytime and nighttime, respectively)  
\(S_{PV}\)PV panel capacity expressed in kWp  
\(B\)Battery capacity expressed in kWh