## 5 Performance Evaluation
### Experiments
#### 5.2.2 Comparison with Other Algorithms  
For a comparison with other algorithms, some of the parameters of RaDROO are \(\lambda~{}=~{}0.5,\alpha=0.5,\gamma_{1}=0,\gamma_{2}=1\).  
The comparison between algorithms takes the obtained target value as the evaluation criterion. Figure 5 shows the optimal cost results obtained by different algorithms at the same time as the number of tasks received increases. Figures 6 and 7 present a comparison of the results obtained from the four algorithms at task volumes of 150 and 170. When the number of tasks is small, and only known allocatable resources are considered, all algorithms provide better results. As the total number of tasks that may be uploaded simultaneously rises, the Brute-Force algorithm fails to obtain an optimal solution because it does not take into account the lack of computational resources. In contrast, RO, SO, DRO, and RaDROO can obtain their optimal solutions, and the optimal cost obtained increases with the increasing number of tasks.  
In the two observations with 150 and 170 tasks, we can clearly see from Figures 6 and 7 that the cost results obtained by the RaDROO algorithm are slightly higher than those of the traditional DRO algorithm. Obviously, as the number of tasks gradually increases, the Brute-Force algorithm that fails to consider offloading the second-stage tasks to the cloud for computations can no longer produce solutions. At the same time, the results obtained by the RaDROO algorithm and the DRO algorithm are close to those of the SO algorithm, while significantly lower than those of the RO algorithm. The SO algorithm, as it calculates the average of the fuzzy sets, is capable of obtaining excellent results, but it may not align closely with real-world scenarios. On the other hand, the RaDROO algorithm considers various factors and still manages to achieve results that are close to those of the SO algorithm, demonstrating its superiority. This indicates that the RaDROO algorithm is still capable of achieving favorable results. By incorporating CVaR to enhance risk resistance, it generates superior outcomes even under uncertain computational resource conditions.  
The RaDROO algorithm, although yielding better results, comes with a trade-off in terms of computational time, as shown in Figure 8. The time required to solve the RO, SO, and Brute-Force algorithms is relatively close and generally lower. The DRO algorithm derives the optimal two-stage mean using fuzzy sets within the framework of the min-max theory of robust algorithms. The RaDROO algorithm considers CVaR on top of this, which further increases the complexity of the algorithm and takes more time to solve the problem.  
The results obtained clearly indicate that RaDROO yields superior outcomes compared to other algorithms. It is worth noting that there is a drawback in terms of the time delay associated with obtaining the offloading decisions. However, this issue can be effectively mitigated by enhancing the performance of the managed physical devices.  
Overall, although RaDROO incurs a certain time cost, it strikes a balance between computational time and risk consideration by incorporating CVaR into the decision-making process. It achieves improved robustness compared to traditional methods, while maintaining competitive performance. By considering CVaR, RaDROO effectively manages the risks associated with uncertain factors and makes informed task offloading decisions within the SAGIN environment.  
Figure 4: V values between 110 and 200 tasks.  
Figure 3: V values under different \(\alpha\).  
Figure 2: Expected costs under different \(\alpha\).  
Figure 5: Optimal costs of the compared algorithms between 110 tasks and 200 tasks.  
Figure 6: Optimal costs under 150 tasks.  
Figure 7: Optimal costs under 170 tasks.