# Improved Q Network Auto-Scaling in Microservice Architecture
## 4 Experiment and Results
The number of cores began with one. All cores were opened in one minute and one second. This method helps stabilize the system by predicting a response to a future request according to the value loaded on the target. Using this process, we proved that the AI Autoscaler had remarkable performance compared to the existing HPA. We also compared its performance with that of the Autoscaler equipped with concepts aiming to strengthen the Q Network process in the DQN structure. Figure 5: Deep Q Network-based intelligent autoscaler compensation value result. As shown in Figure 6 at the top, the highest value was derived with the compensation value 9 at the 26, 39, and 40 th out of the total number of learning, and better performance was derived than before. In Autoscaler work equipped with new AI algorithms, CPU allocation increased faster than it previously did for each new experiment, starting with one core. To make the most of the cores, the response to each request was loaded to perform simulations in a manner similar to the previous environment. The maximum number of cores was also limited to 10. The time used for each core was then measured. The detailed experimental results are summarized in Table 4.  
The Autoscaler equipped with new AI algorithms has an intelligent algorithm that is more suitable for predicting overloads. It can perform tasks by opening the cores very effectively due to its excellent prediction of performance loads. It was also confirmed that the work proceeded radically, starting with one core. All cores were opened in 59 s. This method helps stabilize the system by making more effective predictions based on the value loaded on the target. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **Reference** & **Targets** & **Minpods** & **Maxpods** & **Replicas** & **Time** \\ \hline ii-MSA & Deployment/i1-MSA & 438\%/50\% & 1 & 10 & 1 & 25 s \\ \hline ii-MSA & Deployment/i1-MSA & 419\%/50\% & 1 & 10 & 2 & 31 s \\ \hline ii-MSA & Deployment/i1-MSA & 359\%/50\% & 1 & 10 & 3 & 34 s \\ \hline ii-MSA & Deployment/i1-MSA & 330\%/50\% & 1 & 10 & 4 & 39 s \\ \hline ii-MSA & Deployment/i1-MSA & 276\%/50\% & 1 & 10 & 5 & 46 s \\ \hline ii-MSA & Deployment/i1-MSA & 222\%/50\% & 1 & 10 & 6 & 48 s \\ \hline ii-MSA & Deployment/i1-MSA & 198\%/50\% & 1 & 10 & 7 & 53 s \\ \hline ii-MSA & Deployment/i1-MSA & 172\%/50\% & 1 & 10 & 8 & 57 s \\ \hline ii-MSA & Deployment/i1-MSA & 148\%/50\% & 1 & 10 & 9 & 1 min 1 s \\ \hline ii-MSA & Deployment/i1-MSA & 119\%/50\% & 1 & 10 & 10 & 1 min 10 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: DQN-based auto-scaling performance test results. Figure 6: Quad Q Network-based intelligent autoscaler compensation value result. Figure 7 above depicts the Auto-Scaling Performance Measurement Results by Pod Scaler Type. The results for HPA, i1-MSA (Intelligent Autoscaler Mounted), and i2-MSA (Improved DQN Algorithm) are also shown. The performances of the Autoscalers equipped with AI algorithms were found to be significantly higher than that of HPA, with the Autoscaler equipped with the newly developed DQN algorithm showing the best performance.