## 3 Results  
### Computing Architecture  
The CEHIS and a cloud computing (CC) system are hereby simulated. The CEHIS comprises cloud nodes and edge nodes, representing the upgraded computing resources of the SSRF. In contrast, the CC system includes only high-performance cloud nodes, representing the computing resources of the SSRF without the edge service system configuration. In order to simulate the SSRF's computing resources as realistically as possible, we set the computing power of the cloud nodes to be 10 times that of the edge node and the memory of the cloud nodes to be 4 times that of the edge nodes. We use the RS algorithm, FF algorithm, and Tetris algorithm to schedule task sequences of different sizes, and we evaluate the performances using multiple metrics, including the makespan, simulation time (ST), average completion time (ACT), and average slowdown (ASD). The makespan refers to the time required to accomplish all the tasks, i.e., the duration from the submission of the first task to the completion of the last task. The shorter the makespan of a scheduling algorithm, the more efficient it is. Figure 8 shows that the system architecture has a significant impact on the makespan. The makespan in the CEHIS is much smaller than that in the CC systems. The reason for this is that the CEHIS includes both the edge nodes adjacent to the users, which can reduce transmission time, and the high-performance cloud nodes located further away from the users. Compared to the CC system, the makespan of the CEHIS is reduced by circa 10%. ST is used primarily to measure the time complexity of an algorithm. It tracks the duration from the start of the simulation to the end. Unlike the other three metrics, ST records the computer system time rather than that of the simulation system. Suppose that there are \(N\) tasks and \(M\) nodes in the system; at each timestep, the RS algorithm must  
Figure 8: Makespan of the CHEIS and CC systems using different task scheduling algorithms. explore all tasks and all nodes to determine if a match is possible. The task-node pair is added to a candidate sequence if a match is discovered. Simultaneously, after each check, a random number is generated, and if the random number is less than a threshold, a random match from the candidate sequence is selected, and the loop is exited. Therefore, the best-case time complexity of the RS algorithm is \(O(1)\), and the worst-case time complexity is \(O(MN)\). The time complexity of the FF algorithm is similar to that of the RS algorithm. The Tetris algorithm is based on a greedy strategy, requiring the calculation of compatibility between all the tasks and all the nodes, and then sorting all task-node pairs by compatibility, selecting the most compatible match. The time complexity of calculating compatibility is \(O(NM)\), sorting is \(O(NlogN)\), and selecting the best match is \(O(1)\). Thus, Tetris has a temporal complexity of \(O(NM+NlogN)\). As shown in the graph below, the simulation time of the Tetris algorithm is much longer than the other two algorithms. Let \(SaT\) denote the start time of a task and \(FT\) denote the finish time of the task. Then, the average completion time of all tasks is as follows:  
\[ACT=\frac{\sum_{1}^{N}FT_{i}-SaT_{i}}{N} \tag{12}\]  
Figure 9 shows that the Tetris algorithm has a much higher simulation time than the RA and FF algorithms due to its high time complexity. On the other hand, the randomness within the RA algorithm may result in the allocation of a task with a small computational requirement to a powerful node, leading to resource wastage and indirectly affecting the overall efficiency of the computing system. The task slowdown is the ratio of the completion time and the execution duration. The average slowdown of all the tasks is as follows:  
\[ASD=\frac{\sum_{1}^{N}\frac{FT_{i}-SaT_{i}}{duration_{i}}}{N} \tag{13}\]  
Figure 10 shows the slowdown of different task scheduling algorithms in the CEHIS and CC systems.