# Offloading of Atomic Tasks in Satellite Networks:
## 4 Method
### Problem Conversion  
High orbit satellites have comprehensive coverage and better performance than that of low-medium orbit satellites. However, the number of high-orbiting satellites is sparse in practice. Therefore, if many atomic calculations are offloaded to high-orbiting satellites for execution, the processing capacity of high-orbiting satellites may not receive all the offloaded tasks. Therefore, we propose a re-offloading method for high-orbiting satellites considering the practical situation. As shown in the Figure 2, the satellites \(s_{1}\), \(s_{2}\), \(s_{5}\) and \(s_{6}\) are all medium and low orbit satellites, where \(s_{1}\) and \(s_{2}\) are satellites with atomic missions in the initial state. \(s_{1}\) and \(s_{2}\) first consider whether the computational tasks need to be offloaded in the received atomic missions. If the atomic mission needs to be offloaded, a data transfer link is established with the high-orbiting satellites to offload the mission to the high-orbiting satellites \(s_{3}\) and\(s_{4}\). If the number of computational tasks on the high-orbiting satellites \(s_{3}\) and \(s_{4}\) exceeds the limit and cannot continue to receive task offload requests from low- and medium-orbiting satellites, then the advantage of the task offload function will no longer exist. The computational tasks requiring task offloading need to wait for local computational resources to become available before they can be executed, as in the traditional case. In order to keep the advantages of mission offloading always available and to efficiently use idle satellite resources, we offload atomic tasks that can no longer be processed by high-orbiting satellites to other idle satellites for execution, as shown in Figure 2. The in-orbit satellites \(s_{3}\) and \(s_{4}\) offload computational tasks that they cannot process to other idle satellites \(s_{5}\) and \(s_{6}\). Due to the frequent dynamic changes in the space-based network, the location of satellites also changes dynamically, while the number of mission requests from satellites also changes over time. Therefore, we have to solve the problem of offloading atomic missions from satellites in dynamic network environments. According to the analysis in the above chapters, we can divide the minimum time optimization problem of atomic missions into two parts. The first part is the offloading of atomic missions. Offload some of the atomic missions from the medium and low orbit satellites to the high orbit satellites. This reduces the computational pressure on the low-medium orbiting satellites and makes full use of the high-orbiting satellites' large amount of computational power. The second part is the caching of atomic tasks, which means that there will be more duplication of atomic computing tasks on the medium and low orbiting satellites. Suppose that an atomic computing task on a low-medium orbiting satellite is offloaded to a high-orbiting satellite. In this case, we only need to wait for the high orbiting satellite to return the computation results to the medium and low orbiting satellites, avoiding duplicate buffering and reducing duplicate offloads to save data transfer time. At the same time, we observe that multiple Low Earth Orbit (LEO) satellites need to offload some of their atomic tasks to the High Elliptical Orbit (HEO) satellites, which is caused by the number of antennas in the HEO satellites. The antennas can only match the communication links established by one or a few satellites, which causes other satellites to wait until the other satellites end their occupation of the high-orbiting satellite links. Waiting for link resources in this way is unwise and a waste of resources in a spatial information network that changes dynamically in space and time. Therefore, we re-decompose the problem into three parts. In summary, this paper aims at the low utilization of spatial information network resources, poor timeliness, and long waiting time for atomic tasks problems.