# A Dynamic Service Reconfiguration Method for Satellite-Terrestrial Integrated Networks
## 1 Introduction  
With the rapid development of micro satellite systems and internet technology, integrating terrestrial networks and satellite networks that are able to cover multiple natural environments, such as space, air, earth, maritime, and deep space environments, and that can provide seamless high-speed network access, satellite-terrestrial integrated networks (STINs) have gradually become one of the most important research fields in the current industry and academic community [1]. The combination of emerging network technologies, such as SDN/NFV [2], 5G [3], edge computing [4], and the Internet of Things [5], also ensure that STINs are efficient and have convenient management and control abilities as well as flexible and expandable network service support [6]. Under the STIN architecture, differentiated independent network slices are provided according to a user's actual traffic demands and service requirements, or corresponding Virtual Network Functions (VNFs) are deployed. These aim to use limited network resources or service resources in order to meet the diverse traffic and service requirements of different users as well as to obtain a better network resource configuration and better service experience. However, the dynamic network topology of the STIN itself, as well as the changes in user traffic requests andtask requirements, mean that one-time resource deployment and optimization strategies are no longer able to adapt to the dynamic network states, resulting in the deterioration of network performance, the decline of user service quality experience, the increase in network operation, and maintenance costs, etc. The main reasons for the dynamics that are observed in STINs are the dual mobility of satellites and mobile users with varying requirements. For example, when the LEO satellites offering on-board services move out of the reachable range, the on-board services need to migrate to another satellite that is moving into range. Additionally, users change their access location and network requests along with the real-time tasks and the deployed service may not be able to meet the changing request, so it also needs to be reconfigured. That is, when facing the dynamics caused by user or node mobility that may seriously affect service traffic transmission or service deployment quality, it is necessary to find new adjustment methods to maintain the continuity and quality assurance of traffic transmission and service provisioning. Thus, we need to design a new adjustment method that is able to manage service migration and reconfiguration in STINs and that is able to alleviate problems of load imbalance, degradation, or service quality interruptions to a certain extent [7]. Specifically, the proposed method migrates and deploys the VNFs on the original service node to a new service node that has more sufficient resources or more reasonable paths. Then, the service traffic would be guided according to the new location and would be transmitted by the new service path. Differently to the general network framework of mobile networks, the dual mobility and high dynamics of STINs increase the difficulty of solving the above problem [8]. Therefore, we focus on the service migration and reconfiguration caused by the dual mobility and dynamics under the STIN architecture, and the main contributions are as follows:  
* Considering the dual mobility and high dynamics of STINs, we propose a detailed system model and problem statement for service migration in STINs, and the migration location and path selection problem is modeled and formulated as a classic integer linear programming (ILP) problem. * To achieve the optimal objective efficiently, we propose a novel fuzzy logic quantum genetic algorithm-based dynamic service migration and reconfiguration (FQGA-SR) method that uses its quantum coding and parallel computing advantages to solve the optimization problem. * Finally, the effectiveness and efficiency of the proposed algorithm are verified through simulation experiments.