# Global Resource Scheduling for Distributed Edge Computing
## 5 Experimental Analysis
In the statistics of large samples, the experimental results of the GA are very dependent on the randomness of mutation and the population size. The highest value of the GA algorithm is 2.04, the lowest value is 1.88, with a difference of 0.16. In contrast, SMACA has a highest value of 2.07 and a lowest value of 2.04, resulting in a difference of only 0.03. The experimental results of the ACO and H3 AGA are evenly distributed. Compared with other algorithms, the experimental results of SMACA are more concentrated and less random. Therefore, the algorithm proposed in this paper has excellent stability. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Id** & **Algorithm** & **Parameter** \\ \hline
1 & ACO & \(\tau=1,\rho=0.2\), \(\alpha=1\), \(\beta=2\), \(m=50\), \(T=1000\) \\
2 & GA & The crossing probability value is set as 0.75, the variation probability is 0.05, the population size is 1000, and the chromosome length is 50. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The parameter setting of the experiment. In the quest for the minimum scheduling cost, the criterion for assessing the merits of an algorithm is its ability to approach the actual minimum value. Due to the problem's large scale and other complexities, obtaining the true minimum value might be impractical. Therefore, a new concept, precision, is introduced here. Precision is defined as the difference between the minimum value obtained by the algorithm and the smaller minimum value obtained by the next iteration. A smaller difference indicates a closer approximation to the minimum value. Consequently, when various algorithms achieve the same accuracy, the algorithm requiring fewer iterations is closer to the actual minimum value. Applying this criterion to the problem at hand, we can formulate the following principle: when the  
Figure 4: Comparison of algorithm minimum cost. Figure 5: Distribution of multiple experimental results of different algorithms. iteration times of various algorithms are equal, the algorithm with a smaller accuracy is considered better. Due to the inherent uncertainty in the results of the GA, a precise comparison cannot be made. This paper focuses on comparing the accuracy of the other three algorithms. Figures 6 and 7 depict the actual performance comparison of each algorithm in two different formats. The \(x\)-axis represents the number of iterations, and the y-axis represents the precision value of the algorithm. As the number of iterations increases, the accuracy value of each algorithm improves. In Figure 6, as the number of iterations increases, the performance differences among different algorithms become more apparent. When the iteration count reaches 400, the precision differences between the SMACA algorithm and the other two algorithms are 130 and 180, respectively. Throughout the iterations, the accuracy of the SMACA consistently surpasses that of the ACO and H3 AGA. It can achieve lower accuracy values with fewer iterations, indicating that the SMACA exhibits superior convergence speed. The number of nodes is a crucial factor influencing the algorithm's performance. This paper conducts a comparative experiment on algorithm performance under different node numbers. In Figure 8, the \(x\)-axis represents the number of clusters (each cluster contains multiple nodes), and the \(y\)-axis represents the migration cost. As the number of nodes increases, the cost of resource migration also increases. This is because, with an increase in the number of nodes, the resource scheduling of any node is determined based on the objective function and constraint conditions, not only considering nodes that are close to each other. Therefore, this increase in cost is inevitable. When the number of nodes is 40, the difference between the SMACA algorithm and other algorithms is most significant, with the cost of the GA algorithm reaching 10,000. Compared with other algorithms, the SMACA proposed in this paper demonstrates superior performance in terms of average migration cost.