# Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network
## 3 Results
Black indicates the computation offloading method based on Lyapunov algorithm, red indicates the computation offloading method based on classical Q-learning algorithm, and green indicates the computation offloading method of DQN algorithm proposed in this paper, comparing the time delay and energy consumption cost of the three approaches under experimentally sensitive task requirements. Secondly, using the number of times of network switching as an indicator, we compare and analyze the impact of the DQN computation offloading method, Lyapunov algorithm, and Q-learning algorithm on the task offloading decision process under the Space-Air-Ground Integrated Network environment. In the process of offloading, network switching will increase the cost of task execution. To reduce the cost of delay and energy consumption, the number of network switching needs to be offloaded as few as possible. The results of this comparison are shown in Figure 7. The graphs of all three strategies show an increasing trend, i.e., it shows that the number of network switches performed by all three strategies gradually increases as the number of tasks increases in the Space-Air-Ground Integrated Network. The Lyapunov algorithm-based computation offloading strategy always has the highest number of network switches, and the DQN algorithm based computational offloading strategy proposed in this paper has significant advantages when the number of offloading tasks is high, with the lowest number of network switches and thus the lowest additional cost during the computational offloading of tasks. Then, using the delay and energy consumption as indicators, according to the characteristics of the three-layer architecture of the Space-Air-Ground Integrated Network and the terminal's dynamic movement, the effectiveness of the Markov computation offloading decision process to maximize the use of the three types of computing nodes in the network is verified. Classify task offloading into all offloading to edge computing nodes, all offloading to backbone nodes, and mixed computation offloading at edge backbone nodes. Figure 8 compare the energy consumption cost of the mixed computation offloading under a certain work request volume with that of the mode in which the particular offloading sites are selected separately, while Figure 9 compares the delay cost. The graphs for all three task offloading modes show an upward trend as the workload (i.e., the number of task requests) increases. The rising trend of the mixed compute offloading mode proposed in this paper rises slowly compared to the computation offloading mode that offloads all to the backbone nodes, while the computation offloading mode that offloads all to the edge  
Figure 7: The change in the number of subtasks switching frequency changes in offloading. Blue indicates the computation offloading based on Lyapunov algorithm, black indicates the computation offloading based on classical Q-learning algorithm, and red indicates the computation offloading method of DQN algorithm proposed in this paper. Compare the number of network switching with task amount during task offloading of the three approaches. nodes tends to saturate when the workload is high. Therefore, the mixed compute offload mode for edge backbone nodes in this paper has lower energy and delay cost. Figure 8: Comparison of energy consumption costs of different offloading sites. Blue indicates that all tasks are offloaded to the backbone nodes of the network, yellow indicates that all tasks are offloaded to the edge nodes of the network, and red indicates the mixed offloading mode proposed in this paper, where some tasks are offloaded to the backbone nodes and the others are offloaded to the edge nodes. Compare the energy consumption cost of the mixed computation offloading under a certain work request volume with that of the mode in which the particular offloading sites are selected separately. Figure 9: Comparison of delay costs of different offloading sites.