## 3 System Architecture and Key Technologies
### CMMN-Based Heterogeneous Cloud Resource Integration and Scheduling
#### 3.1.2 Task Scheduling Optimization Based on CMMN Algorithm
### Task Scheduling Performance Analysis

The task data are randomly generated to simulate the diversity of tasks in a heterogeneous cloud environment. The cloud ready time simulates the delay in resource availability, and since this delay is relatively short compared with the task execution time in real-world applications, it is set between 1 and 10 in the simulation. Task execution times on different clouds are set between 5 and 10,000 to accommodate both small and large tasks, making the simulation more representative of real-world scenarios. The initial proportion of independent tasks is set to 0.2, meaning 20% of tasks have no predecessor dependencies, which helps balance dependency relationships and parallelism within the task graph. A lower ratio would reduce parallelism, causing some heterogeneous cloud nodes to remain idle at certain times, while a higher ratio would weaken dependency relationships, reducing the effectiveness of complex task scheduling simulations. Additionally, the number of direct predecessor tasks for each task is limited to a maximum of six, applying only to direct predecessors, meaning that a task's predecessors may still have their own dependencies. This setup preserves the complexity of task dependencies while preventing extreme cases where many tasks rely on the same predecessor in scheduling simulations involving 100 to 1000 tasks. Each dataset is generated 100 times for experimentation, and the average results are calculated. The experimental results are shown in Table 1.

The experimental results focus on cloud completion time and average cloud utilization. Cloud completion time refers to the moment when the last task finishes execution on the cloud, indicating the efficiency of task execution, with smaller values representing higher efficiency. Average cloud utilization is calculated by dividing the average completion time of the last task on each cloud by the cloud completion time, measuring the load balance on the heterogeneous cloud platform. Higher values indicate a more balanced load distribution. From the data in Table 1, the improved CMMN algorithm shows advantages in both metrics across all datasets. Specifically, the cloud completion time was reduced from 22,288.72 to 21,812.85, saving 2.37% of time; the average cloud utilization rate was increased from 91.977% to 92.577%, an increase of 0.652%. These results show that the proposed improvement measures have improved the task execution efficiency and optimized the load balance of cloud resources to a certain extent. However, the overall improvement is not significant. The reason is that our optimization strategy is mainly based on two key aspects. On the one hand, we focus on optimizing the cloud ready time, but the average value of the cloud ready time is only 5.5, while the average value of the cloud execution time is 5002.5, and the cloud ready time only accounts for about 0.1% of the cloud execution time. Therefore, its effect on improving the overall performance is relatively small. On the other hand, another focus of the optimization strategy is to target special cases in task batch division, but these special cases account for a relatively low proportion of the entire task, which limits the contribution of the strategy to the overall improvement. ### Dynamic Data View Generation

The experiment aims to evaluate the method's effectiveness in a geological exploration 3D visual analysis system, assessing its practicality through user interaction. The process is divided into three steps: template selection, data configuration, and interaction implementation. In the template selection phase, suitable chart templates are chosen from a predefined library to display various geological data types, as shown in Figure 8. These templates support various chart forms, including line, bar, and advanced relationship diagrams and visualizations for geological features like 3D, boreholes, cross-sections, and tunnel models. Template selection is based on data characteristics and display requirements.