# A Dynamic Service Reconfiguration Method for Satellite-Terrestrial Integrated Networks
## 2 Related Works  
### Service Migration in Mobile Edge Networks  
At present, there are many well executed, relevant studies in the fields of mobile edge networks, the Internet of things, fog computing, and in the multi-source heterogeneous and dynamic STIN, its research is still in the start-up stage. As an example, in [9], the service migration problem in mobile edge computing networks is formulated as a Markov decision process (MDP). The work in [10] models the VNFs migration problem as an integrated linear programming (ILP) problem and proposed a greedy algorithm to minimize the migration cost while satisfying the computing and network resource capacity constraints. The mobile edge computing scenario service migration cost is studied in [11]. In this work, they take into account the service migration revenue and total cost of service at the same time and design a dynamic service migration algorithm based on deep reinforcement learning. On the premise of guaranteed service continuity and quality of service, this algorithm is used to optimize the service migration cost and total cost of network operation. In [12], the researchers study the service migration of a mobile backhaul network in fog computing scenarios. In order to meet the continuous network service demands of mobile terminal users with high capacity and low delay, a delay-awareness bandwidth slicing technique is proposed to address the service migration traffic limitation problem and to optimize the network performance of high-priority migration traffic. This study highlights the priority setting of migrating traffic and non-migrating traffic to optimize the traffic transmission efficiency of service migration. In [13], service migration in mobile edge networks is modelled as a reinforcement-learning model to optimize the service migration strategy and communication strategy in order to improve long-term efficiency. Simulation results show that the proposed method based on reinforcement learning has better results than the Q-learning method and the Deep Q-learning network method. The work in [14] proposed a service migration optimization algorithm based on machine learning, taking network delay and energy consumption as optimization targets while considering the changes of network bandwidth and mobile terminal energy. The work in [15] proposed an online multi-user service migration optimization algorithm based on Lyapunov to solve the service migration problem in multi-user vehicle scenarios without considering vehicle trajectory predictions. Considering the mobility of fog nodes, the work in [16] studied the deployment of application components in NFV-based hybrid cloud-fog computing system and proposed a component deployment algorithm based on Tabu search to find the optimal deployment strategy. Nevertheless, the mobility issue studied in this work is the increasing or decreasing of the number of connected nodes, rather than the mobility model.  
The application scenarios of the works mentioned above have several similarities with the STINs, such as multi-source heterogeneous network architecture and the mobility of mobile users. However, the mobility of satellites trigger the structure and network state changes of the STINs, while the network sides of 5G, mobile edge network, and the IoT are fixed, which makes it different to studies of the service migration problem in STINs.