# Reducing the Operational Cost of Cloud Data Centers through Renewable Energy
## 5 Results
### Impact of Renewable Energy on Cost Saving  
We start by reporting in Figure 5 the amount of hourly energy production for a few days in the various DCs. First of all, observe the typical intermittent behavior of RE generation, with production during daytime and no production during night. In addition, since the solar irradiance was taken from real traces, there was some difference in the behavior of different days, as can be clearly seen from the peak values. Second, notice that the different time-zones in which the DCs are located created a shift in the typical intermittent pattern. An effective approach for performance maximization should adapt workload allocation among the DCs taking into account the overall patterns of RE generation, moving the load in accordance to where RE is generated and cost results lower.  
Figure 6 reports the hourly energy consumption and the hourly energy cost for each DC in a sample day. As can be observed from Figure 5(a), the energy consumption of each DC showed a limited variability among DCs and during the day, with a minimum consumption level of slightly more than 50% the maximum observed hourly energy demand. Conversely, as can be evinced from Figure 5(b), the energy costs varied much more over time and from DC to DC, due to energy price fluctuations. In particular, considering the time window from midnight to 6:00, despite similar energy consumption levels in the different DCs, the cost for the energy consumption showed huge variability over different DCs, with the range of variation being as high as 80% the maximum registered hourly energy cost. A proper VM assignment was hence required in order to place energy-demanding VMs on DCs where the energy price was lower.  
Figure 5: Production of RE in each considered DC location.  
To understand the benefits of RE generation in the considered scenario, Figure 7 reports the weekly energy cost (Figure 7a) and cost-saving in percentage (Figure 7b) for several cases in which different values of the PV panel size and battery capacity were considered. Clearly, as the PV panel size increased, the amount of produced RE increased, and energy cost decreased. However, the advantages that were large for small RE plants reduced when a condition was reached such that most of the power supply was provided by RE. No additional saving was possible by increasing the PV panel size. Battery capacity had a similar impact. Large values of batteries allowed a better utilization of the produced RE. Once battery capacity was such that all the produced RE was used, no additional advantages could be achieved by using larger values of the battery capacity. Considering that the yearly energy cost was of the order of more than 90,000 $ in this scenario with four DCs, the cost saving may achieve more than 80,000 $ per year. Furthermore, since DCs can be composed of thousands of servers rather than just one to a few hundred, this saving might be as high as 5.5 million $ per year. Since 1 kWp corresponds to 5 m\({}^{2}\) of PV panels, the case reported in the figure corresponded to quite large areas (around 1000 m\({}^{2}\) for the 200 kWp case). This is a potential limitation to the adoption of RE, whose feasibility strongly depends on the environment in which the DC is located.