## 6 Experimental Evaluation
### Evaluation of the Proposed Approach without VM Distribution  
3.1 The Scenario I: Mapping of Heterogeneous VM Types to Heterogeneous Machines without Distribution Policy  
The mapping of VMs to PMs is modeled as a variable size bin-packing problem. For Scenario I, Figure 3 depicts the variation in number of VM requests and CPU demand in the workload. The VM  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **ECU** & **Speed (GHz)** & **Memory (MB)** \\ \hline M11.small & 1 & 1 & 1740 \\ M1.large & 2 & 4 & 7680 \\ M1.xlarge & 4 & 8 & 15,360 \\ M2.xlarge & 2 & 6.5 & 17,510 \\ M22.xlarge & 4 & 13 & 35,020 \\ C1.median & 2 & 5 & 1740 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Virtual machine request types.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Machines** & **Frequency(MHz)** & **No of cores** & **Power model** & **Memory(GB)** \\ \hline M1 & 2500 & 56 & 1 & 192 \\ M2 & 2200 & 16 & 2 & 24 \\ M3 & 2200 & 16 & 3 & 24 \\ M4 & 2500 & 112 & 4 & 384 \\ M5 & 2200 & 64 & 5 & 128 \\ M6 & 2300 & 36 & 6 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical machine characteristics.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Power Model** & **Idle** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** & **100\%** \\ \hline
1 & 48.2 & 106 & 129 & 154 & 179 & 205 & 235 & 269 & 306 & 347 & 385 \\
2 & 69.4 & 103 & 117 & 137 & 159 & 182 & 201 & 218 & 240 & 290 & 315 \\
3 & 59.5 & 87.6 & 98.5 & 109 & 120 & 136 & 153 & 177 & 204 & 233 & 261 \\
4 & 89.8 & 237 & 286 & 333 & 383 & 442 & 511 & 592 & 710 & 839 & 939 \\
5 & 84.9 & 138 & 154 & 168 & 182 & 197 & 214 & 234 & 253 & 271 & 287 \\
6 & 47.3 & 92.3 & 108 & 123 & 138 & 153 & 172 & 199 & 230 & 260 & 289 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Power (in Watts) setting of physical machines (PMs).  
types given in Table 4 are mapped to the machines given in Table 2. Figure 4 displays the RVA% of AHSA, PA, FFD and BFD approaches for 24-h duration. Figure 4 reveals 1-2% increase in RVA% by AHSA than PA, and 3-13% for FFD and BFD. Figure 5 presents the RVA% for the first 12 reservation cycles. The CPU utilization for the first five reservation cycles with 100% RVA for all approaches is 19.75%, 38.74%, 52.12%, 67.35% and 78.25%. The AHSA achieved an overall power reduction (24-h duration) of 2.18% more than FFD, 1.481% more than PA, and 2.24% more than BFD, as shown in Figure 6. Figure 7 presents the power consumption for the acceptance rate indicated in Figure 5. As in Figure 7, the power reduction for 100% acceptance of AHSA with respect to the first four intervals is 1.05% less than PA, 1.78% less than FFD and 1.86% less than BFD.  
Figure 5: RVA% Scenario I within an hour.  
Figure 6: Power consumption Scenario I.  
Figure 7: Power consumption Scenario I within an hour.  
3.2 Scenario II: Mapping of Heterogeneous VM types to Homogeneous Machines without Distribution Policy  
In Scenario II, the total number of 50 M4 machines, as in Table 2, is considered for mapping the VM types given in Table 4, which portrays a fixed size bin-packing problem. The RVA% for the 24-h duration is shown in Figure 8. It can be inferred from Figure 8 that the AHSA has obtained 1-4% more RVA% than PA, and approximately 5-22% more than FFD and BFD.  
Figure 9 presents the overall power consumption of all the methods for a day. The overall power reduction achieved by AHSA is approximately 1.75% more than PA, BFD and FFD. In Figure 10, the first 14 reservation cycles depict 100% acceptance of all the algorithms, which can be used as a fair measure for comparison of power for the same utilization. AHSA provides approximately 7% more overall power reduction in the first 14 cycles than BFD and FFD, and approximately 1% more than PA for the same utilization.