## 3 System Architecture and Key Technologies
### CMMN-Based Heterogeneous Cloud Resource Integration and Scheduling
#### 3.1.2 Task Scheduling Optimization Based on CMMN Algorithm
In the data configuration phase, users add and link dimension and metric data with template axes through drag-and-drop operations, ensuring accurate data mapping and display. Users first select and add relevant datasets in the configuration area (Figure 9a), which include multidimensional and multi-metric information. Once added, the system generates and displays dimension and metric data, as shown in Figure 9b,c. Dimension data are presented hierarchically, while metric data provide the numerical basis for analysis. Users then drag dimension data from Figure 9b and metric data from Figure 9c into the axis sections of Figure 9d to create specific data views for analysis. Additional filtering and drill-down features in Figure 9d allow for data subset analysis and multi-level exploration. Interactive functions, such as hover and click events, enhance data exploration. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{3}{c}{**Cloud Completion Time (ms)**} & \multicolumn{3}{c}{**Average Cloud Utilization**} \\ \cline{2-7}  & **CMMN** & **Optimized** & **Improvement** & **CMMN** & **Optimized** & **Improvement** \\ \hline
100 \(\times\) 4 & 60,583.98 & 59,528.53 & 1.77\% & 0.9644 & 0.9685 & 0.43\% \\
200 \(\times\) 8 & 37,110.81 & 36,367.85 & 2.04\% & 0.9385 & 0.9496 & 1.18\% \\
300 \(\times\) 12 & 26,606.16 & 26,029.34 & 2.22\% & 0.9302 & 0.9351 & 0.53\% \\
400 \(\times\) 16 & 21,091.38 & 20,655.71 & 2.11\% & 0.9210 & 0.9299 & 0.97\% \\
500 \(\times\) 20 & 17,500.01 & 17,109.37 & 2.28\% & 0.9171 & 0.9215 & 0.48\% \\
600 \(\times\) 24 & 14,910.70 & 14,519.72 & 2.69\% & 0.9093 & 0.9141 & 0.53\% \\
700 \(\times\) 28 & 13,171.05 & 12,823.78 & 2.71\% & 0.9039 & 0.9123 & 0.93\% \\
800 \(\times\) 32 & 11,747.93 & 11,436.66 & 2.72\% & 0.9040 & 0.9094 & 0.60\% \\
900 \(\times\) 36 & 10,528.38 & 10,258.25 & 2.63\% & 0.9081 & 0.9119 & 0.42\% \\
1000 \(\times\) 40 & 9636.75 & 9399.24 & 2.52\% & 0.9012 & 0.9054 & 0.47\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Adaptive scheduling experimental results. ### Rendering Performance Optimization

The experiment involves block models at four different resolutions: D0, D1, D2, and D3. Each level increases the data volume by seven. The evaluation metrics include memory usage, the number of rendered objects, Draw Calls, and rendering time. The experimental results are shown in Figure 10, where columns with diagonal stripes represent rendering failures due to memory overflow. Table 2 presents the memory usage results. As the resolution of the fusion models increases, the data volume rises significantly, leading to failures in conventional rendering on the D2 and D3 datasets due to memory limitations. Similarly, the occlusion culling strategy also fails on the D3 dataset because of memory constraints. For the D1 resolution, conventional rendering uses 2281 MB of memory; with occlusion culling, memory usage drops to 444 MB, a reduction of 80.5%. After implementing batch rendering, memory usage

Figure 8: Dynamic data view interaction interface. Figure 9: Dynamic data view interaction interface: (**a**) Data position selection; (**b**) Dimension position selection; (**c**) Metric position selection; (**d**) Dimensions and metrics in the current chart. decreases to 295 MB, a reduction of 87%. When both strategies are applied simultaneously, memory usage is reduced to 144 MB, a reduction of 93.7%. Table 3 presents the results for the number of rendered objects. For the D3 resolution, without occlusion culling, 7.74 million objects were rendered, while with occlusion culling, only 250,000 objects were rendered, resulting in a 96.7% reduction. Table 4 presents the results for the number of Draw Calls. For the D1 resolution, 120,000 Draw Calls were made without batch processing, while with batch processing, the number was reduced to 50, a 99.9% reduction.