# Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network
## 2 Materials and Methods  
### Related Research  
In recent years, computation offloading decisions [9] which aim at reducing delay and energy consumption have become one of the hot topics of computation offloading technology research, mainly considering whether user terminals need to be offloaded, the number of offloads, and the choice of offloading location. [PERSON] et al. [10] combined the auction theory to transform the computation offloading problem into a matching problem, and proposed a multi-round sealed sequence combined auction mechanism to reduce the delay in the computation offloading process. [PERSON] and [PERSON] [11] minimized the application delay under the constraint of resource utilization, at the same time transformed computation offloading into NP-hard problems, and proposed FPTAS online learning algorithm to solve the problem of computation offloading delay in the dynamic environment. [PERSON] et al. [12] proposed a pre-computed offline solution to optimize the computation offloading process so that the terminal energy consumption could be minimized. [PERSON] et al. [13] proposed an optional offloading scheme based on the lightweight request and access framework to reduce the energy consumption of devices. In reference [14], a computation offloading decision based on the Lyapunov function was proposed. However, considering the topological dynamic characteristics of the network, this method is not suitable for the wireless channel model with randomly changing. [PERSON] et al. [15] proposed a low-complexity suboptimal algorithm, which used semi-deterministic relaxation and randomization to obtain the optimal offloading decision to solve the computation offloading problems in mixed fog/cloud computing systems. [PERSON] et al. [16] transformed the problem of computation offloading into the convex optimization problem, and proposed an integrated fog and cloud computation offloading strategy, which offloaded a series of applications to nearby fog nodes or cloud center and reduced the system delay. [PERSON] et al. [17] proposed an optimal joint communication and computing resource allocation algorithm for the problem of resource allocation in computation offloading. Further, the method of segmentation optimized partial offloading model was used to segment the data to achieve the optimal resource matching under low delay. [PERSON] et al. [18] proposed a mixed-integer nonlinear computing task offloading decision, which transformed the problem of low-delay computation offloading into the problem of task offloading placement and task resource allocation. This scheme effectively reduced the delay of the computation offloading process in ultra-intensive networks. [PERSON] et al. [19] aimed to reduce the delay of the computation offloading process in the actual Internet of things and proposed a completely polynomial-time approximate computation offloading strategy to rationally allocate resources for applications and shorten the computation processing time. In Reference [20], a task priority algorithm of maximum energy saving was proposed, which divided the tasks that need to be offloaded and applied the greedy algorithm to reduce energy consumption in the computation offloading process. [PERSON] et al. [21] considered the computation offloading problem of mobile edge servers with energy collection devices and proposed a task offloading strategy based on the Lyapunov algorithm and greedy scheduling algorithm. When the server received a task offloading request, a centralized and distributed resource scheduling strategy was adopted to reduce energy consumption. [PERSON] et al. [22] equalized the delay and energy consumption cost, and used the dichotomy method to allocate computational resources to the mobile terminals that need to be offloaded, while maximizing the number of offload tasks and effectively reducing the total system cost.