# A Dynamic Service Reconfiguration Method for Satellite-Terrestrial Integrated Networks
## 2 Related Works
### Service Migration in STINs  
At present, researchers have made some achievements in the studies on the service migration and reconfiguration of STINs. In [17], the geographic migration model of computing resources in the scenario of satellite intelligent urban vehicle networking is established. The road states and vehicle states are known through fog nodes, and a vehicle route selection scheme based on resource pricing is proposed. Its main innovations are that of regarding the moving vehicles as computing resources and proposing the pricing strategy algorithm under diverse demand conditions, while the computational efficiency of the migration strategy is slightly insufficient. The work in [18] proposes a space-based edge cloud service on orbit service architecture, which can carry out service migration dynamically according to the real-time uplink and downlink traffic changes. Aiming at optimizing the migration node delay and energy consumption, a space-based edge cloud service migration model is established and verified by simulation. However, the object and scope of service migration are merely for satellite nodes and their on-orbit services, and the migration between satellite and ground nodes is not considered. The work in [19] studied dynamic resource allocation and reconfiguration of communication services in the space-time network scenario, modelling the on-board user resource allocation problem as a multi-period generalized allocation problem, and proposed a deep Q-learning control framework to optimize resource allocation and reconfiguration. Considering the dynamics of the Internet of vehicles scenario, [20] studied the online dynamic VNF mapping and scheduling problem in the Internet of vehicles scenario of space-time cooperation, and proposed two VNF mapping and rescheduling algorithms based on Tabu search algorithm, which can effectively obtain the approximate optimal VNF migration scheme. In the 5G heterogenous scenario, the work in [21] focuses on the power control problem in 5G heterogeneous cells, proposes a novel deep reinforcement learning (DRL) scheme, which can efficiently configurate the cells power to gratify the user demand. In [22], the resource allocation problem in multi-cell networks was studied, a deep neural network for training the allocation model was proposed, and simulation results show the accuracy of its allocation method.  
It seems that the DRL algorithms continually interact with environments to obtain the optimal solution, and, for the most part, performed well in large scale optimal problems such as resources allocation in 5G multi-cell networks or mobile edge networks, whose user side is mobile, while the network side is fixed and possesses ample computing capacity. However, while the necessary and frequent interaction of DRL may lead to more consumption of communication and computing resources, it is still worth discussing whether it is suitable for the STINs scenario with limited on-board resources and longer communication delay.  
Although some researchers have studied the service migration and reconfiguration of STINs and similar network architectures from various perspectives, their pertinence and optimization effect are slightly insufficient and required further discussing. Especially since there is a high dynamicity coupled with dual mobility both in the network side and the user side of STINs, which makes it challenging to obtain an efficient service migration and reconfiguration method. The quantum computing algorithms have higher computational efficiency than other iterative-based algorithms, since the superposition coding manner of quantum computing supports parallel computing capability [23]. Thus, in this work, a novel optimal method FQGA-SR with STIN model is proposed, so as to achieve higher computational efficiency and better performances in service migration and reconfiguration problems.