# Green Cloud Software Engineering for Big Data Processing
## 4 Results and Discussion  
### Peak Power Consumption for Synthetic Workloads  
As discussed in the previous section, stress-ng is used to synthetically stress the compute nodes with CPU, I/O and RAM workloads. The number of cores to be stressed, number of I/O tasks and amount of RAM are provided as input. The experiment is conducted for a time-period of 60 s. Workload is applied in percentages from 0 to 100 in an interval of 10.  
This experiment is repeated 10 times with the peak power consumption (PPC) (in Watt) being observed and noted. The average results are tabulated in Table 3. The graph for a single core is depicted in Figure 7 while the graph for all cores is shown in Figure 8.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Workload** **(\%)**} & \multicolumn{3}{c}{**Average Peak Power Consumption in Watt**} \\ \cline{2-7}  & **Compute1** & & **Compute2** & & **Compute3** \\ \cline{2-7}  & **1 Core** & **All Cores** & **1 Core** & **All Cores** & **1 Core** & **All Cores** \\ \hline
0 & 26.8 & 30.9 & 39.2 & 39.5 & 39.8 & 40.2 \\
10 & 32.9 & 42.1 & 44.0 & 44.9 & 42.8 & 45.1 \\
20 & 35.1 & 48.2 & 47.4 & 48.9 & 47.5 & 49.1 \\
30 & 37.1 & 50.8 & 49.4 & 54.0 & 49.7 & 54.4 \\
40 & 38.1 & 55.8 & 50.9 & 57.9 & 50.8 & 58.6 \\
50 & 38.6 & 60.2 & 51.6 & 58.3 & 52.8 & 62.1 \\
60 & 40.0 & 71.1 & 52.4 & 60.6 & 54.5 & 66.6 \\
70 & 40.5 & 80.1 & 55.0 & 61.3 & 56.0 & 68.0 \\
80 & 42.1 & 83.3 & 56.4 & 63.4 & 58.9 & 69.9 \\
90 & 47.1 & 84.9 & 57.3 & 65.9 & 60.9 & 71.2 \\
100 & 52.1 & 87.6 & 58.7 & 68.4 & 63.1 & 72.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Configuration of Servers.  
Figure 7: Average peak power consumption of compute nodes for synthetic workloads (single core).  
It is observed that for similar workloads, the power usage characteristics of servers with single core and multicores are different and this is affirmed by [52]. From Figure 7, it is evident that both Compute2 and Compute3 consume approximately similar amount of power. However, Compute1 consumes less power compared to the other compute nodes when one of the cores is stressed. This is due to the fact that the i7 processor is optimized for power consumption compared to the Core 2 Duo processors [52]. On the contrary, when all the cores are stressed (see Figure 8), the PPC of Compute1 changes drastically particularly, when the workload is more than 50%. The turbo boost feature of the i7 processor could be responsible for this behavior [104]. The turbo boost feature of i7 processors reduces up to 6% percent of the execution time at the cost of increasing the energy consumption by 16% [105]. By comparing Compute2 and Compute3, the average PPC value is approximately 60W when workload is 50% for both cases. However, when the workload increases from 60% to 100%, Compute3 tends to consume more power than Compute2. Several factors could be responsible for this behavior, one of which is the electronic hardware ageing phenomenon [106]. The above analysis shows that to reduce overall energy consumption during data processing, it is important to reduce the peak power consumption by effectively identifying the underloaded and overloaded hosts, followed by reducing the number of active hosts via putting the other hosts to an idle mode. The idle mode power consumption of the compute nodes is negligible as shown in Table 2.