Adaptive Online Service Function Chain Deployment in Large-scale LEO Satellite Networks

As global communication demands continue to rapidly expand, traditional terrestrial networks are facing significant challenges in terms of coverage, capacity, and reliability. To overcome these limitations, large-scale low-earth orbit (LEO) satellite networks have emerged as a promising solution, offering ubiquitous and seamless connectivity worldwide. However, this solution brings its own set of challenges, including limited satellite resources, diverse quality of service (QoS) requirements for random service function chain (SFC) requests, the complexity of managing large-scale networks, and the unpredictability of network changes. To tackle these challenges, this paper presents a novel adaptive online SFC deployment algorithm based on deep reinforcement learning. The proposed algorithm effectively handles real-time network changes and diverse service requirements while minimizing resource usage and enhancing QoS, leveraging the sharing of virtual network functions (VNFs) among multiple SFCs on satellite nodes and effectively balancing computing occupancy across satellites. To reduce complexity, we employ subnet segmentation to diminish the dimensionality of the state space. Simulation results validate the effectiveness of the proposed algorithm in significantly reducing resource occupancy and end-to-end delay, even in scenarios involving a large number of requests.