# Optimizing Energy Efficiency in Cloud Data Centers:
## 7 Results and Discussion  
In this study, we compare the proposed Q-learning-Firefly-based method's three baseline algorithms: power aware best fit decreasing (PABFD), particle swarm optimization \begin{table}
\begin{tabular}{c c c c} \hline \hline
**VM Type** & **MIPS** & \begin{tabular}{c} **Core (Processing** \\ **Elements**) \\ \end{tabular} & \
\begin{tabular}{c} **RAM** \\ **(MB)** \\ \end{tabular} &
\begin{tabular}{c} **Bandwidth** \\ **(Mbps)** \\ \end{tabular} \\ \hline High-CPU Medium Instance & 2500 & 1 & 870 & 100 \\ \hline Extra-Large Instance & 2000 & 1 & 1740 & 100 \\ \hline Small Instance & 1000 & 1 & 1740 & 100 \\ \hline Micro-Instance & 500 & 1 & 613 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: VMs’ specifications. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Name** & **0\%** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** & **100\%** \\ \hline Hp ProLiantM110G4 Xeon3040 & 86 & 89.4 & 92.6 & 96 & 99.5 & 102 & 106 & 108 & 112 & 114 & 117 \\ \hline Hp ProLiantM110G5 Xeon 3075 & 93.7 & 97.0 & 101 & 105 & 110 & 116 & 121 & 125 & 129 & 133 & 135 \\ \hline Hp ProLiantD1360G7 XeonX5675 & 55.6 & 95.4 & 107 & 115 & 124 & 133 & 142 & 155 & 173 & 192 & 216 \\ \hline Hp ProLiantD136 0G9 XeonE5-2699 & 45 & 83.7 & 101 & 118 & 133 & 145 & 162 & 188 & 218 & 248 & 276 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Power consumption (watts) at different load levels. (PSO), and genetic algorithm (GA). These methods have been selected due to their prevalence in the literature and their role as standard baselines in the field of virtual machine (VM) consolidation and energy-aware resource management. PABFD serves as a classical heuristic benchmark [32], while PSO and GA represent benchmark model for metaheuristic algorithms. Although more recent techniques exist, these methods are still commonly adopted for performance comparison, providing a reliable reference for evaluating the effectiveness of our approach and the performance of the proposed Q-learning model combined with the Firefly algorithm. To ensure robustness in our evaluation, we considered two types of host behavior models: lr_1.2_mmt and iqr_1.5_mc as described in [33]. * IqrMc: Inter Quartile Range Maximum correlation. This method employs a VM allocation strategy based on the Inter Quartile Range (IQR) hotspot detection technique, combined with a policy that selects VMs with maximum correlation. * LrMmt: Using a method derived from Cleveland (local regression), LrMmt identifies whether a host is over-utilized and whether its VMs need to be migrated. If a host is overused, LrMmt selects a VM that requires the least migration time, using the minimum migration time technique. The Figure 4 illustrates that our model can significantly reduce energy consumption compared to both behaviors. Furthermore, stricter constraints positively impact energy consumption, enabling even greater energy savings. Upon analyzing Figures 4 and 5, it becomes evident that leveraging VM placement through reinforcement learning with Firefly results in the lowest energy consumption among the examined methods. Specifically, under the lr mmt 1,2 strategy, our model combining QL and Firefly achieves a reduction of 5.40% compared to PABFD, 12.80% compared to PSO, and 12% compared to genetic algorithms. Likewise, under the Iqr mc 1,5 strategy, our model achieves a reduction of 12.11% compared to PABFD, 15.60% compared to PSO, and 18.67% compared to genetic algorithms. The number of live migrations remains a key factor affecting energy consumption and service level agreement (SLA) violations. Reducing this number can lessen the overall data center load, consequently reducing SLA breaches. As shown in Figures 6 and 7, our approach achieves the fewest live migrations over the entire 10 days. Additionally, our VM selection policy (QL with Firefly) results in a smaller number of live migrations compared to other VM selection policies. Figure 4: Energy consumption (Kwh) under different workload datasets.