## 6 Results and Analysis
### Results and Analysis  
Figure 5 illustrates the impact of DCRMM, MDA, and MSA on the stability of computing nodes. We conducted tests on the average decrease rates in CPU, Memory (Mem), and Storage for all nodes after executing 10 tasks. The average decrease rate is defined by Equation (21), where \(Node_{pre}\) represents the average utilization before task execution, and \(Node_{now}\) represents the average utilization after task execution. The average CPU decrease rate of DCRMM is 1.60% lower than MSA and 1.45% lower than MDA. The average Mem decrease rate is 2.43% lower than MSA and 0.14% lower than MDA. For Storage, the average decrease rate is 15.80% lower than MSA and 3.48% lower than MDA. This indicates that DCRMM can reduce the variability of computational resources on nodes, enhancing stability among nodes. \[S=Node_{pre}-Node_{now} \tag{21}\]  
Figure 6 depicts the relationship between node utilization and the number of tasks for DCRMM, MDA, and MSA. As shown in Equation (22), node utilization (\(U\)) is represented as the ratio of selected nodes (\(Nodes_{s}\)) to the total number of nodes (\(Node_{t}\)). From Figure 6, it is evident that with an increase in the number of tasks, the node utilization of DCRMM, MDA, and MSA all experiences a corresponding improvement. However, it is clear from the figure that the node utilization of DCRMM is significantly better than that of MDA and MSA, both at low and high task numbers. This is attributed to the fact that DCRMM can simultaneously consider static and dynamic metrics, enhancing the precision of node-task matching. \[U=\frac{Nodes_{s}}{Node_{t}} \tag{22}\]  
Figure 4: Number of Hops for Different Learning Parameters. Figure 5: Algorithm Stability Assessment. Figure 7 represents the relationship between the matching accuracy of selected nodes by DCRMM, MDA, and MSA and the number of tasks. As indicated in Equation (23), the matching accuracy (\(A\)) is represented as the ratio of nodes meeting the requirements of tasks (\(Node_{m}\)) to the selected nodes (\(Node_{s}\)). According to the figure, the matching accuracy of DCRMM can be maintained at 100%, while MDA has a matching accuracy of only below 60%, and MSA is below 40%. Moreover, with an increase in the number of tasks, both MDA and MSA show a decline in matching accuracy to varying degrees. This is because the DCRMM algorithm considers both dynamic and static metrics, performs node matching by calculating Euclidean distance, and prioritizes the removal of nodes that do not meet the requirements before computing the Euclidean distance. This ensures that all nodes in the filtered pool can meet the computational requirements of the tasks. \[A=\frac{Node_{m}}{Node_{s}} \tag{23}\]  
Figure 8 illustrates the average link delay of the RSCR. According to Figure 8, it can be observed that the average delay of the RSCR is comparable to that of the OSPF algorithm. However, when compared to the OSPF_delay algorithm, the average delay of the RSCR algorithm is lower by 63%. This is because the RSCR exhibits a preference for paths with fewer hops and lower congestion levels in route selection. Figure 6: Relationship Between Node Utilization and Number of Tasks. Figure 7: Relationship Between Node Accuracy and Number of Tasks. Figure 9 depicts the average packet loss rate of the RSCR algorithm. According to Figure 9, it is evident that the packet loss rate of RSCR is 15% and 25% lower than OSPF_all and OSPF_loss, respectively. In some scenarios, the packet loss rate of OSPF_all is more than twice that of the RSCR algorithm. This is attributed to the RSCR algorithm's capability to select relatively shorter paths, thereby reducing packet loss. Figure 10 illustrates the average link throughput of RSCR throughout the day. The throughput of RSCR is lower than OSPF_all and OSPF_bw, primarily because RSCR selects paths that are less preferred by the latter two, resulting in a larger denominator when calculating the average link throughput.