## 4 Discussion and Conclusions  
In this work, we focused on the key findings and implications of our study on optimizing data processing and analysis for the synchrotron radiation facilities, with a particular emphasis on two critical aspects: the computing architecture and the intelligent task scheduling. \begin{table}
\begin{tabular}{c c c c c} \hline  & **RA** & **FF** & **Tetris** & **DRL** \\ \hline Makespan & 666 & 673 & 666 & 632 \\ ST & 1.67 & 2.07 & 3.77 & 86.13 \\ ACT & 39.42 & 38.01 & 38.75 & 38.82 \\ AS & 1.21 & 1.19 & 1.17 & 1.17 \\ \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation based on 30 jobs scheduled with the DRL. Figure 10: Average slowdown of the CEHIS and CC systems using different task scheduling algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline  & **RA** & **FF** & **Tetris** & **DRL** \\ \hline Makespan & 1316 & 1331 & 1433 & 1251.41 \\ ST & 6.26 & 6.36 & 18.56 & 223.19 \\ ACT & 54.52 & 42.97 & 45.82 & 48.84 \\ AS & 2.03 & 1.59 & 1.32 & 1.32 \\ \hline \end{tabular}
\end{table} 
Table 5: Performance evaluation based on 50 jobs scheduled with the DRL. Our proposed CEHIS significantly improves the makespan over the CC approach, resulting in an ideal solution for the large-scale scientific facilities, such as the SSRF. The performances of different scheduling algorithms (RS, FF, and Tetris) highlight the importance of selecting the appropriate algorithms based on the job workload and the computational demands. The policy gradient-based DRL algorithm effectively optimizes the makespan and the system efficiency without compromising other performance metrics. However, its complexity and demanding simulation time require further investigation to broaden its applicability. In conclusion, the CEHIS, equipped with the intelligent task scheduler, positively impacted the SSRF, and particularly resulted in the following:  
* Improved efficiency. The shorter makespan suggests that the CEHIS can process and analyze data more efficiently, enabling faster completion of the experiments and the analysis tasks. * Enhanced resource utilization. The CEHIS allows for more effective allocation and utilization of computing resources, thereby reducing the resource wastage and the overall costs. * Better real-time responsiveness. With edge nodes located closer to the data sources, the CEHIS can provide faster response times and lower latency, successfully satisfying the real-time requirements of the SSRF beamlines. * Scalability. The CEHIS offers a more scalable solution, as it can flexibly allocate resources between the cloud and edge nodes, making it better suited to handle the future increasing data volumes and computational demands. * Encouraging interdisciplinary research. The improved efficiency and responsiveness introduced by the CEHIS can facilitate collaborations across different research fields, fostering innovation and scientific discovery. Our findings offer vital insights into the optimization of the data processing and analysis infrastructure at the large-scale scientific facilities, laying the groundwork for the development and deployment of the cloud-edge hybrid architectures and intelligent scheduling algorithms. Conceptualization, A.S., C.W. and R.T.; methodology, J.Y., C.W. and A.S.; software, J.Y., J.C. and A.S.; validation, R.W., C.W. and X.L. ; formal analysis, J.Y. and C.W. ; investigation, J.Y. and C.W. ; resources, A.S., R.W. and X.L. ; data curation, J.C. and R.W. ; writing--original draft preparation, J.Y., C.W. and A.S.; writing--review and editing, A.S. and R.T.; visualization, A.S. and R.T.; supervision, A.S., C.W. and R.T.; project administration, A.S. and R.T.; funding acquisition, A.S., C.W. and R.T. All authors have read and agreed to the published version of the manuscript. This research was funded by the Photon Science Research Center for Carbon Dioxide, CAS, the Youth Innovation Promotion Association, CAS (Grant no. 2022290), and the Natural Science Foundation of Shanghai (Grant no. 19 ZR1463200). Not applicable.