# DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
## 2 Related Works
For instance, reference [23] tackled resource utilization challenges in integrated satellite-ground environments through VNF sharing technology. Reference [24] developed collaborative SFC deployment methodologies for multi-domain satellite-terrestrial integrated networks. Reference [25] proposed task-oriented algorithms optimized for specific satellite missions, focusing on minimizing ground station link resource consumption and server utilization. Reference [26] introduced a distributed VNF deployment algorithm that simultaneously addresses network bandwidth cost minimization and end-to-end delay optimization in satellite edge computing contexts. In multi-layer satellite network environments, reference [27] investigated SFC deployment strategies for small-scale software-defined satellite networks, introducing dual deployment paradigms: a multi-domain (MD) architecture utilizing MEO satellites as transit intermediaries, and an adaptive cluster framework accommodating diverse operational conditions. Reference [28] tackled the temporal dynamics of satellite networks through temporal graph representation of network topology, developing a novel SFC deployment algorithm based on this theoretical framework. Reference [29] advanced a goal-weighted path selection methodology to enhance SFC traffic routing efficiency in multi-layer satellite architectures. Reference [30] implemented a hidden Markov decision model to resolve challenges inherent in satellite network dynamics and substantial inter-satellite link latencies. Distributed optimization has emerged as a crucial optimization paradigm, finding extensive applications in large-scale network optimization and multi-agent systems. This approach decomposes the overall optimization problem into multiple parallelizable subproblems, where each subsystem makes decisions based on local information and limited communication to achieve global optimization objectives. In [31], researchers introduced a distributed optimization framework for nonlinear multi-agent systems based on small-gain methods. The framework incorporates input-to-state stability (ISS) theory and small-gain techniques to prove system stability, effectively handling coupling constraints among agents while ensuring optimization convergence. Additionally, reference [32] proposed a distributed optimization-based approach for UAV swarm source seeking under communication constraints, achieving source localization through formation control and consensus filters. This method significantly reduces communication overhead compared to traditional approaches requiring gradient and Hessian matrix estimations. The dynamic SFC orchestration problem in LEO satellite networks, which is the focus of our research, exhibits distinct characteristics from these typical distributed optimization scenarios. The time-varying nature of network topology introduces dynamic characteristicsto both optimization objectives and constraints. Furthermore, SFC deployment necessitates end-to-end service quality guarantees, resulting in strong coupling relationships among decision variables. Resource allocation and path selection decisions require optimization based on global network states, making direct application of conventional distributed optimization methods challenging. Nevertheless, the concept of distributed optimization still provides important insights for solving large-scale satellite network optimization problems, especially when considering system scalability and computational efficiency. Current research exhibits several limitations. The unique architectural requirements of large-scale satellite networks have resulted in slower progress compared to terrestrial network research. Furthermore, existing studies often treat SFC deployment and redeployment as separate processes, driven by specific optimization objectives. Research specifically addressing SFC readjustment in satellite networks remains insufficient.