# Green Cloud Software Engineering for Big Data Processing
## 4 Results and Discussion
### Performance Evaluation
Effective identification of overloaded/underloaded hosts and VMs to be migrated is crucial in VM consolidation as aggressive VM migrations lead to unnecessary energy loss [74]. In addition, power state changes between sleep and on states should be kept to a minimum [64]. Figures 10 and 11 compare the number of VM migrations and power state changes of the six approaches. From Figure 10, it is shown that Combo6 has the least number of VMs migrated. The combination of LRR prediction of resource utilization and MMT's strategy to select VMs based on minimum time taken to migrate is effective in saving energy, causing the least migration overhead. Combo5 is the second-best method with less VM migrations. From Figure 10, it is observed that the minimum migration time (MMT) algorithm performs better than random choice (RC) in keeping power state changes to an optimal level as 'combo' that employ MMT (i.e., Combo 4, 5 and 6) have less changes in power states than 'combo' (i.e., Combos 1, 2 and 3) that apply RC algorithm. The following discussion summarizes the results obtained by studying the impact of the six VM consolidation approaches in terms of SLA compliance. SLA violations are caused by both over-utilization of resources (performance degradation due to 100% resource utilization) and degradation caused by extensive VM migrations as defined in [67]. SLA metrics that define SLA violations are SLATAH and PDM as discussed in the previous section. Table 5 presents the comparison of energy and SLA violation metrics of the six approaches. SLA violations (SLAV) metric is computed from SLATAH and PDM for each combo. From Table 5, it is observed that Combo5 (MAD and MMT algorithms applied) has the least SLA violations followed by Combo6 (LRR and MMT). Threshold-based Heuristic (THR) and random choice (RC) algorithms cause most SLA violations and are not as effective as MMT, MAD and LRR. The energy service level agreement violation (ESV) metric in the case of Combo6 is reduced by the energy consumption factor. The balance between energy saving and SLA violations is expressed by ESV [71]. Although Combo5 has less SLA violations, the energy consumed (4.03 kWh) is more compared to Combo6 (3.22 kWh). Figures 12 and 13 present graphs of SLAV and ESV respectively. The results obtained are similar to the results presented in [67]. The experimental results for the effect or overhead caused by the virtualization layer on peak power consumption, CPU and memory utilization, are presented in Table 6. Notations 'A' and 'B' in Table 6 denote 'No virtualization enabled' and 'Virtualization enabled by KVM, Openstack and Openstack NEAT', respectively. It is clear that the increase in PPC caused by the  
Figure 11: Power state changes of compute servers. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Combo** & **E** & **SLATAH** & **PDM** & **SLAV** & **ESV** \\ \hline Combo1 & 5.28 & 17.67 & 0.31 & 5.4777 & 28.92226 \\ Combo2 & 4.64 & 14.54 & 0.28 & 4.0712 & 18.89037 \\ Combo3 & 4.32 & 18.56 & 0.22 & 4.0832 & 17.63942 \\ Combo4 & 4.57 & 19.25 & 0.23 & 4.4275 & 20.23368 \\ Combo5 & 4.03 & 21.42 & 0.09 & 1.9278 & 7.769034 \\ Combo6 & 3.22 & 65.23 & 0.03 & 1.9569 & 6.301218 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Energy and SLA violation metrics. virtualization layer is negligible (in the order of less than 1 Watt of power) and less than 0.5% of CPU and memory utilization. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Host**} & **Virtualization** & **Peak Power** & \multirow{2}{*}{**CPU (\%)**} & \multirow{2}{*}{**Memory (\%)**} \\  & **State** & **Consumed (Wait)** & & \\ \hline \multirow{2}{*}{Compute1} & A 1  & 26.7 & 0.34 & 0.29 \\  & B 2  & 27.8 & 0.42 & 0.36 \\ \hline \multirow{2}{*}{Compute2} & A & 39.2 & 0.25 & 0.19 \\  & B & 39.9 & 0.33 & 0.25 \\ \hline \multirow{2}{*}{Compute3} & A & 39.8 & 0.27 & 0.21 \\  & B & 40.9 & 0.35 & 0.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effect of virtualization on peak power consumption and resource utilization. Figure 12: SLA violations.