# Design of Cloud-Based Real-Time Eye-Tracking Monitoring and Storage System
## 4 Discussion  
The demand for data has seen a substantial increase in recent years due to factors such as rapid technological advancements, growing interest in AI from both the private sector and researchers, and the proliferation of diverse research in the literature [66; 67; 68; 69]. However, it is widely acknowledged that data collection systems, expected to keep up with these demands, are facing limitations. This study aims to develop a system that facilitates the data collection process for various studies, particularly in the academic domain, while simultaneously enabling the real-time observation and streaming of the collected data. Presently, REST is extensively employed in academic research across various fields, including case generation [70], methodologies [71], biological data [72], machine learning [73], etc. Furthermore, prominent companies, like Google, Amazon, Twitter, and Reddit, also utilize this architecture. As part of this study, REST enables the instantaneous streaming of the collected data. However, to avoid restricting researchers solely to Internet-based usage, the system incorporates the Dockerization technique, allowing for local implementation. Consequently, tests were conducted in local and online (server-based) configurations. A significant difference was found between the time it took for the eye-tracking model data to reach the results page in the locally configured system compared to the same system configured online. Numerous performance bottlenecks, such as Internet latency [74], computer configuration [75], and server location [76], present considerable challenges that are difficult to mitigate. Although the latency experienced online is significantly higher than that of the local configuration, it is believed that the experimental online latency is not substantial enough for users to discern [77] (the delay values are shown in Figure 3). Figure 4: Diagram of memory usage: blue line, memory usage dynamic; red dotted line, memory usage average. Time axis is expressed in minutes. \begin{table}
\begin{tabular}{c c} \hline \hline
**Conditions** & **Memory (Megabyte (MB))** \\ \hline Average increase between seconds & 0.0037 \\ Average memory usage & 72.62 \\ Minimum memory usage & 63.74 \\ Maximum memory usage & 83.62 \\ Standard deviation & 3.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Memory usage statistics. The system presented in this study, which is based on several different techniques, serves the purpose of the real-time streaming and storage of eye-tracking data. However, it is crucial to highlight the flexibility of the proposed system, which can be adapted for collecting and analyzing other data types in different experimental settings. Several studies in the literature use Docker technology to build cloud platforms and integrate them into a variety of experiments, similar to the approach used in this current study. The use of Docker technology allows for the integratation of AI and various models in studies. The system demonstrates a well-suited structure for numerous AI models in the literature. In particular, [PERSON] et al. (2022) successfully implemented facial emotion recognition using Convolutional Neural Networks [78]. In addition, [PERSON] et al. (2022) presented a Deep Learning-based ECG signal classification model [79]. Similarly, [PERSON] et al. (2020) focused on the task of speech emotion recognition, employing neural networks [80]. All these studies use models that are implemented using Docker technology and have substructures that can work in compatibility with the introduced system. Simultaneously, the system allows the real-time tracking of users' eye movements, enabling streaming over the Internet without being limited to a single task. The memory consumption of the experimental system should also be highlighted. During the experiment, the memory consumption of the system slowly increased, putting a certain load on the computer used for the experiment.