# Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network
## 4 Discussion  
The main objective of this paper is to solve the problems of low computational offload efficiency, high delay and energy consumption cost in Space-Air-Ground Integrated Network due to the complex and variable network offloading environment, the large amount of offloading tasks and the limited computational and storage resources of the network nodes, and to investigate a computation offloading solution that can adapt to these characteristics of the Space-Air-Ground Integrated Network. In this paper, we investigate a computation offloading solution that can accommodate these characteristics of the integrated network. In the following, we discuss and analyze the findings from the algorithms, network switching times and task offloading modes of the computation offloading application, and then explain and validate the effectiveness and applicability of our proposed DQN-based computation offloading decision scheme. For the comparison of computational offloading algorithms, the Lyapunov algorithm is the most widely used in computation offloading technology, which can maintain the stability of the offloading system when the amount of tasks increases and prevent the system performance from deteriorating sharply. The classical Q-learning algorithm can solve the Markov decision process model through the maximum cumulative reward obtained by the state-action, but the algorithm has limited applicability and is inefficient in the large-scale state space. We have compared the system cost of these three kinds of computation offloading algorithms when dealing with different amounts of offloading tasks to verify the effectiveness of the proposed computational offloading algorithm in updating the computation offloading decision process in the Space-Air-Ground Integrated Network. We set different weights for the delay-sensitive tasks that need to be offloaded on the offloading terminal. As can be seen from Figures 5 and 6, for the task offloading requirements with equal weighting of delay and energy consumption, and for offloading task volumes above 500 Mbit, the proposed computation offloading strategy reduces on average 68.33% compared to the traditional Lyapunov-based computational offloading decision scheme, and 11.21% compared to the Markovian decision method based on the classical Q-learning algorithm. Further, for delay-sensitive offloading tasks, the three computation offloading algorithms can effectively reduce the total cost of the offloading strategy under the condition that the delay-sensitive offloading tasks have a greater weight than the weight of energy consumption. The total cost of the offloading strategy of the three computation offloading algorithms shows an upward trend with the increase in the number of offloading tasks. In contrast, the upward trend of the DQN computation offloading algorithm slows down significantly, which can effectively reduce the total delay-energy consumption cost of the task offloading strategy. In Figure 6, when the amount of offloading tasks is less than 160 Mbit, the total cost of offloading strategy under the DQN computation offloading algorithm is slightly higher than the Q-learning algorithm and close to Lyapunov algorithm. This is because the DQN computation offloading algorithm is more complex than the other two algorithms, but the number of tasks to be offloaded in the Space-Air-Ground Integrated Network is huge. Therefore, the offloading strategy based on the DQN computation offloading algorithm can effectively reduce delay and energy consumption. For the comparison of the number of network switches during task offloading for different computation offloading methods, as shown in Figure 7, the Lyapunov-based computation offloading method has the highest number of switches and the steepest upward trend.