# Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network
## 5 Conclusions  
In the context of the Space-Air-Ground Integrated Network, due to the complex and variable offloading environment, the huge amount of offloading tasks, and the different storage resources and computing capacity of different orbital nodes, this paper theoretically analyzes the status of the offloading terminals and the offloading sites, and the selection of the offloading network and proposes a computation offloading decision scheme based on the Markov decision process and DQN algorithm. Specifically, segment the task data, select the optimal offloading network and the optimal offloading sites, transform the task processing process into the state transformation process of the Markov model, and use the DQN computation offloading algorithm to optimize the Markov decision process and seek the optimal offloading strategy. The experiment proved that the proposed computation offloading strategy reduces 68.33% on average compared to the traditional Lyapunov-based computational offloading decision scheme and 11.21% on average compared to the Markov decision method based on the classical Q-learning algorithm when the number of offloading tasks exceeds 500 Mbit and under the weighting requirements such as delay and energy consumption. When the number of offloading tasks exceeds 130 Mbit, the number of network switches required to select the optimal computation offloading decision and offload the network by the computation offloading scheme proposed in this paper is significantly lower than that of the Lyapunov-based and classical Q-learning algorithm based computation offloading methods. Moreover, when the number of network work requests exceeds 100, the proposed mixed offloading approach can satisfy a large number of task offloading requests, and the energy cost and delay cost are 61.54% and 71.01% lower than those of the offloading to the network backbone nodes and the offloading to the edge nodes, respectively. Therefore, the proposed computational offloading scheme is suitable for Space-Air-Ground Integrated Network, reduces the number of switching of offloading networks, effectively reduces the delay and energy consumption cost of task computational offloading, and improves the network resource utilization. The Space-Air-Ground Integrated Network is the trend of future networks as a huge system that integrates communication and aerospace telemetry to achieve full network coverage. In previous research, computation offloading techniques that enable efficient task processing have been applied to mobile phone terminals and vehicle terminals, but in the future, as various terminal devices continue to develop and advance, and human needs become more and more advanced, all terminal devices will be part of the Space-Air-Ground Integrated Network, and the rational application of computation offloading techniques to the Space-Air-Ground Integrated Network is of great theoretical importance. The application of computational offloading technology to Space-Air-Ground Integrated Network is of great theoretical significance and practical application. In this paper, the existing computation offloading techniques are improved and applied to the Space-Air-Ground Integrated Network, starting from the structural characteristics of the network. On the one hand, according to the delay and energy consumption requirements of the Space-Air-Ground Integrated Network's task offloading, the task can be matched with the offloading sites to effectively reduce delay and energy consumption while taking into account the requirements. On the other hand, the mixed consumption offloading model is able to cope with the huge volume of task processing requests in the future Inter-net of everything era with limited network resources. Theoretically, it can contribute significantly to the efficiency of task processing, thus addressing the impact and challenges of the inherent shortcomings of the Space-Air-Ground Integrated Network on the future development of the Internet. Conceptualization, J.L. and C.L. ; methodology, X.L. ; software, J.L.