# Optimizing Energy Efficiency in Cloud Data Centers:
## 5 Proposed Reinforcement Learning-Based VM Placement in Cloud Environment Using RF_SOM Classifier  
### Reinforcement Learning Model  
At time t, the agent observes the current state of all PMs, searches for an action using the Q-learning algorithm, and sends the decided action to the PM for execution. Each \begin{table}
\begin{tabular}{c c} \hline
**Dataset Characteristics** & **Description** \\ \hline Number of columns (number of characteristics) & \(11\) \\ \hline Characteristic type & Nominal and numeric \\ \hline Missing data & No \\ \hline Label unknown & Yes \\ \hline Number of labels & 2 \\ \hline Distribution of samples in each class & Unbalanced \\ \hline Storage format & CSV \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 2: Proposed VM classifier model. element of the state space S represents the current usage of CPU, RAM, network bandwidth, and storage I/O of each VM on each PM as follows. \[ \begin{split} S_{t}=\{\{\text{CPU}_{1},\text{RAM}_{1},\text{NET}_{1},\text{STIO}_{1}\},\\ \{\text{CPU}_{2},\text{RAM}_{2},\text{NET}_{2},\text{STIO}_{2}\}, \\ \ldots,\{\text{CPU}_{m},\text{RAM}_{m},\text{NET}_{m},\text{STIO}_{m}\}\} \end{split}  \tag{10}\]  
where i = \(1,\ldots,\text{m}\) is the index id of the PM, \(m\) is the number of PM in the data center, and CPU\({}_{i}\in[0,1]\), RAM\({}_{i}\in[0,1]\), NET\({}_{i}\in[0,1]\), STIO\({}_{i}\in[0,1]\). Each resource utilization indicator is normalized with respect to the maximum volume of the corresponding PM resource. The action space is defined as a set:  
\[A=\{a_{1}(t),a_{2}(t),\ldots,a_{m}(t)\},a_{i}(t)\in\{-1,0,1\},i=1,m \tag{11}\]  
Each action \(a_{i}(t)\in A\) transitions the PM to sleep mode or active mode before the next management step \(s_{t+1}\). The dynamic VM consolidation algorithm changes the mode of each PM based on the agent's decision. The agent then observes and computes the Q- value after modifying all operating modes and completing the action taken but before the next management step \(s_{t+1}\). The main objective of the proposed algorithm is to minimize energy consumption. The penalty for increased energy consumption can be defined as follows. \[P_{t}(\text{power})= \frac{\text{Power}_{t}}{\text{Power}_{t-1}+\varepsilon}+P_{\text{ Sal}} \tag{12}\] \[P_{\text{sla}}=\frac{\text{NbrVms}_{\text{sensitive}}}{\text{NbrAllVms}} \tag{13}\]  
where the following are true:  
* \(\varepsilon\) equals 0.01
* Power\({}_{t}\) is the power consumption in the current management step. * Power\({}_{t-1}\) is the power consumption in the previous management step. * \(P_{\text{sla}}\) means that, using of the (RF + SOM) model, the penalty for SLA violation can be represented by the number of sensitive VMs over the overall number of VMs. In this paper, we integrate multiple techniques--random forests (RF), self-organizing maps (SOMs), Q-learning, and the Firefly algorithm (FA)--to address the complex problem of energy-efficient VM placement and migration in cloud data centers. In Figure 3, we present the overall architecture of our hybrid approach, where each component plays a distinct role in achieving optimized resource management and SLA compliance. While this integration might appear complex at first glance, each component serves a specific role that enhances the overall system's performance. RF and SOM are initially used to classify VMs based on their sensitivity to latency. This classification step improves the accuracy of the VM placement decision by distinguishing sensitive VMs from insensitive ones. This classification directly informs the Q-learning process, enabling it to adapt its policy based on VM sensitivity, thereby optimizing energy consumption while ensuring SLA compliance. The Q-learning model continuously adjusts the VM placement strategy based on real-time feedback, learning from previous placements to improve future decisions. The Firefly algorithm is employed during the migration phase to find energy-efficient consolidation strategies, while taking into account VM sensitivity and SLA requirements.