# Green Cloud Software Engineering for Big Data Processing
## 4 Results and Discussion
### Performance Evaluation  
The experimental results for the investigation of the impact of six different VM consolidation 'combos' on energy consumption is illustrated in Figure 9. \begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Metrics** & **Description** \\ \hline Total Energy Consumption (E) & It is the sum of energy consumed by the compute servers as a result of application workloads over a specific time period. It is measured in kilowatt hour [59]. \\ \hline Number of VM Migrations & VMs are selected to be migrated once a host is identified to be underloaded or overloaded. Minimizing the time for migration is a crucial step which is achieved by reducing the total number of VM migrations. \\ \hline Power State Changes & The number of state changes (on and off) of compute nodes must be minimal to avoid unnecessary loss of energy. \\ \hline QoS requirements of a system are devised in the form of SLA (service level agreement), determined by attributes such as throughput or response time which are application dependent. In the case of IaaS, QoS can be evaluated using SLA metrics that depend on VM and compute resources [74]. IaaS-SLA violations (SLAV) can be measured using two metrics below. _SLATAH_ (SLA violation time per active host)—The period of time when a host experiences 100% CPU utilization and the requested performance is not delivered as it is limited by the node’s capacity causing a violation of the SLA as shown in Equation (3). \\ \(SLATAH=\frac{1}{N}\sum\limits_{i=1}^{N}\frac{T_{i}}{T_{ai}}\) (3) \\ where \(N\) is the number of compute nodes, \(T_{si}\) is the total time during which the host \(i\) experienced 100% utilization leading to an SLA violation, \(T_{ai}\) is the total time when host \(i\) actively provides VMs [107]. _PDM_ (performance degradation due to migrations) The overall degradation in performance experienced during migration of virtual machines as shown in Equation (4). \\ \(PDM=\frac{1}{M}\sum\limits_{i=1}^{M}\frac{C_{di}}{C_{di}}\) (4) \\ where \(M\) is the number of virtual machines, \(C_{di}\) is the performance degradation of \(VM_{i}\) caused by migrations, \(C_{ri}\) is the total processor capacity requested by \(VM_{i}\). In general, \(C_{di}\) is assumed to be 10% of CPU in million instructions per second (MIPS) during migrations [107]. _SLAV_ (SLA violation)—As _SLATAH_ and _PDM_ are two independent metrics, SLA violation (_SLAV_) is a metric that combines both performance degradation caused by overloading as well as VM migrations as shown in Equation (5). _SLAV_ = _SLATAH_ * _PDM_ (5) It denotes the violation that takes place when the promised QoS is not met [107]. \\ \hline Energy consumption (\(E\)) of compute nodes and _SLAV_ are negatively correlated as energy consumed can be reduced at the cost of increased SLA violations. Whereas, the goal of an energy-aware system is to minimize energy as well as SLA violations. Hence, a combined metric energy and SLA violations (_ESV_) proposed in [67] is shown in Equation (6). \\ \(ESV=E\star SLAV\) (6) \\ A lower \(ESV\) value indicates that energy saving is higher than SLA violations. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Configuration of servers. It is observed that with VM consolidation, minimum amount of energy saved is 8.33% (i.e., compared with Combo1) and maximum amount of energy saved is 44.09% (i.e., compared to Combo6). This shows that 'Combo6' clearly outperforms all the other algorithms with a reduced 2.54 kWh (within a 24-h duration) of electrical energy by switching the underloaded compute nodes to sleep mode. Combo5 and Combo3 save up to 1.73 kWh and 1.44 kWh, respectively (within a 24-h duration). It is observed that VM consolidation selection plays a crucial role in energy saving as random choice (RC) causes aggressive migrations consuming energy which is mitigated by minimum migration time (MMT) algorithm [70]. Combining MMT, prediction-based local regression robust (LRR) and statistical median absolute deviation (MAD) algorithms accomplish substantial energy saving.