# Improved Q Network Auto-Scaling in Microservice Architecture
## 5 Conclusions  
With the advent of the microservice framework, auto-scaling capabilities that meet various demands have emerged as a major area of interest. Numerous expansion mechanisms have been developed. This study focused on request-based expansion, where we first investigated the performance and efficiency of core expansion for requests by mounting AI algorithms. The results of the experiments showed significant differences in throughput as well as deviations in maximum moisture from average latency. Therefore, the presence or absence of AI algorithms can affect performance. To flexibly adjust the auto-scaling settings to meet the particular requirements of a given situation, we designed RL models based on Q-learning and evaluated their applicability to learn effective scaling policies during runtime. Further, based on other algorithms with an enhanced Q Network, we showed that the proposed model could effectively and appropriately adjust the number of cores within  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **Reference** & **Targets** & **Minpods** & **Maxpods** & **Replicas** & **Time** \\ \hline i2-MSA & Deployment/i2-MSA & 432\%/50\% & 1 & 10 & 1 & 19 s \\ \hline i2-MSA & Deployment/i2-MSA & 415\%/50\% & 1 & 10 & 2 & 22 s \\ \hline i2-MSA & Deployment/i2-MSA & 364\%/50\% & 1 & 10 & 3 & 26 s \\ \hline i2-MSA & Deployment/i2-MSA & 329\%/50\% & 1 & 10 & 4 & 33 s \\ \hline i2-MSA & Deployment/i2-MSA & 269\%/50\% & 1 & 10 & 5 & 37 s \\ \hline i2-MSA & Deployment/i2-MSA & 224\%/50\% & 1 & 10 & 6 & 42 s \\ \hline i2-MSA & Deployment/i2-MSA & 198\%/50\% & 1 & 10 & 7 & 46 s \\ \hline i2-MSA & Deployment/i2-MSA & 168\%/50\% & 1 & 10 & 8 & 51 s \\ \hline i2-MSA & Deployment/i2-MSA & 129\%/50\% & 1 & 10 & 9 & 55 s \\ \hline i2-MSA & Deployment/i2-MSA & 114\%/50\% & 1 & 10 & 10 & 59 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Improved DQN-based auto-scaling performance test results.  
Figure 7: Auto-Scaling Performance Measurement Results by Pod Scaler Type.  
a limited time, thereby outperforming the average throughput of the default HPA. Given these results, the presented research makes a significant contribution to existing work in the fields of both microservice frameworks and RL-based auto-scaling applications.  
Based on the experimental results, we identified the following limitations of this approach: First, the maximum number of cores was limited to 10. There was no experiment investigating unexpected variables depending on the increasing load and whether it could perform better in a real-life environment that required a larger amount of traffic work. We developed the RL approach to learn core expansion policies mainly by testing the load on the target. However, it is necessary to analyze the extent to which the resource use ratio of individual components can affect performance. Therefore, there is a need for a comprehensive study to find the combination of usage levels that is capable of achieving the best possible performance in all microservices.  
Conceptualization, Y.K. and J.K.; Methodology, Y.K. and J.P.; Software, Y.K. and J.P.; Validation, J.P. and J.Y.; Formal analysis, J.P.; Investigation, J.Y.; Resources, J.Y. and J.K.; Data curation, J.P. and J.Y.; Writing--original draft preparation, Y.K.; Writing--review and editing, J.K. and J.Y.; Visualization, J.Y.; Supervision, J.K. and J.Y.; Project administration, J.K.; Funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.  
This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2021R1I1A3060565). This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2021-0-02068, Artificial Intelligence Innovation Hub).  
Not applicable.  
The authors declare no conflict of interest.