# Global Resource Scheduling for Distributed Edge Computing
## 5 Experimental Analysis  
Given the characteristics of the global resource scheduling problem in distributed edge computing, dataset preprocessing is essential to better reflect the model's characteristics and structure. A dataset was constructed, comprising 50 nodes located in different positions. To assess the algorithm's performance, several algorithms were employed for comparison, including the greedy algorithm [39], the basic ant colony optimization (ACO) [40], the genetic algorithm (GA) [41], and the hybrid ACO-GA algorithm (H3 AGA) [42]. The selective mutation ACO (SMACA), proposed in this paper, is then compared against this dataset. The current operations on this dataset include two aspects:  
1. Accuracy comparison To validate the accuracy of the algorithm proposed in this paper, a comparison is made based on the objective function values of the scheduling results obtained by different algorithms. A higher objective function value after scheduling indicates that the results of this algorithm are closer to the optimal solution. 2. Iteration cost comparison The iteration cost of the algorithm refers to the number of iterations, serving as a crucial performance metric. We establish the iteration times of different algorithms under the same target conditions for comparison. 3. Throughput analysis The topology of this experiment consists of 50 nodes, with connections between each pair of nodes, forming a graph-like data structure. The edges between any two nodes have weights, which carry different meanings in various application scenarios. As illustrated in Figure 1, in the context of IoT edge computing, the edge weights can represent latency, whereas in the intelligent transportation scenario, the edge weights may denote distance. The weights of edges play a crucial role in the results of task migration scheduling. This experiment primarily focuses on the system's task processing throughput, represented by the ratio of successfully scheduled tasks within a unit of time. Table 2 shows the algorithm parameter setting information of this experiment. The accuracy of the algorithm is assessed based on the objective function value. A higher objective function value indicates better accuracy for the algorithm under the same iteration number. In Figure 4, the \(x\)-axis represents the iteration number of the algorithm, while the y-axis represents the objective function value of different algorithms under the same iteration number. In the initial iterations, the accuracy of the GA is notably lower than the other three algorithms. However, with a gradual increase in the number of iterations, the accuracy of the GA improves. Compared with the three algorithms, the SMACA proposed in this paper demonstrates significantly better convergence speed than the ACO and H3 AGA. It approaches the maximum target value at the fastest rate. With the increase in the number of iterations, the objective function value of the ACO continues to rise, and its convergence time is the longest among the ant colony algorithms. The H3 AGA converges before the 400 th iteration, while the SMACA requires about 200-300 iterations to converge. In summary, the SMACA can substantially reduce the iteration time cost, exhibits the fastest convergence, and has a notable advantage in accuracy. This experimental result is attributed to SMACA dynamically adjusting when selecting the next node and balancing the convergence rate during pheromone updates. The result of the greedy algorithm is independent of multiple iterations, making it incomparable with other heuristic algorithms in terms of convergence speed and accuracy. Because the results of a single experiment may need to be more accurate due to randomness, this paper will repeat each experiment 20 times. The results of 20 experiments will be displayed in a boxplot, as shown in Figure 5. The abscissa in the figure represents different algorithms, and the ordinate represents the objective function value.