# Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks
## 5 Simulations and Results Analysis
### Performance Analysis
It is intuitive that more available computing resources would weaken the role of dynamic resource allocation. Figure 6 illustrates the rate of satisfied tasks at different computing resource budgets in LEO satellites. When available resources are less, all methods exhibit relatively lower satisfied task rates. Both RO-ARA and RO-DDPGRA use random offloading, showing an approximately linear growth with increased resources. By comparison, proposed method and DQNO-ARA consistently achieve higher satisfied rates, surpassing the other two RO methods. Likely, due to the exploitation in dynamic resource allocation, proposed method outperforms DQNO-ARA, nevertheless with the narrowed gap with more resources. In Figure 7, the rate of satisfied tasks under different maximum tolerance latency are compared. As the latency tolerance increases, the rates of all methods show an approximately linear growth. Given smaller tolerance, the rates of three other methods are almost identical except for the RO-ARA method. However, as the latency tolerance increases, proposed method exhibits significant advantages. In particular, at the tolerance of 290 ms, proposed method is improved by 7%, 22%, and 27% over three other ones, respectively. Note that even at the tolerance of 290 ms, the rates of satisfied tasks of RO-ARA and RO-DDPGRA are still relatively lower, due to the fact that some tasks are randomly offloaded locally. As such, there tasks cannot be completed within the tolerance, owing to the limited computing resources in ground terminals. Figure 5: Rate of satisfied tasks under different computing resources budgets in the cloud server. Figure 6: Rate of satisfied tasks under different computing resource budgets of LEO satellites. Figure 8 further compares the rate of satisfied tasks under different numbers of terminals. As the number increases, the rates for all methods show a declining trend. In particular, given more terminals, the RO-DDPGRA method surpasses the DQNO-ARA, suggesting that more existing terminals would make the resource allocation dominate computation offloading. More precisely, when the terminal number reaches 40, except for the RO-ARA, the rates of three other methods become close. That is, given the excessive terminal number, both computation offloading and resource allocation begin to take no effects on the performance. Nevertheless, given either more or smaller existing terminals, proposed method always outperforms benchmark ones. Figures 9 and 10 illustrate the proportion of offloaded tasks under different approaches, and the rate of satisfied tasks in terminals, satellites and the cloud server, respectively. Both figures reveal that in RO-ARA and RO-DDPGRA methods, both employing random offloading, the number of offloaded tasks is equal to that of locally executed ones. In Figure 10, the rate of satisfied tasks in terminals is consistently below 40%. However, the RO-DDPGRA method shows around 10% higher rate for tasks offloaded to satellites, and 8% higher for tasks offloaded to the cloud server, both over the RO-ARA, owing to RO-DDPGRA. In particular, RO-DDPGRA adopts the DDPG, which can deterministically optimize resources to tasks offloaded to the same satellite (or cloud server), while RO-ARA only evenly distributes resources. In contrast, both PROPOSED and DQNO-ARA methods have significantly lower rates of tasks locally executed, and noticeably higher rates of tasks offloaded. Moreover, PROPOSED exhibits approximately 8% (and 6%) higher  
Figure 8: Rate of satisfied tasks under different terminal numbers. Figure 7: Rate of satisfied tasks under different maximum tolerance latency settings. satisfaction for tasks offloaded to satellites (and the cloud server), respectively, than DQNO-ARA, due to the difference in the resource allocation. Likewise, PROPOSED can allocate resources in line with the parameterized continuous action, while DQNO-ARA can only distribute resources equally.