Low Earth Orbit (LEO) satellite networks are promising for satellite-based cloud platforms. Due to frequent link switching and long transmission distances in LEO satellite networks, applying the TCP/IP architecture introduces challenges such as packet loss and significant transmission delays. These issues can trigger excessive retransmissions, leading to link congestion and increased data acquisition delay. Deploying Named Data Networking (NDN) with connectionless communication and link-switching tolerance can help address these problems. However, the existing congestion control methods in NDN lack support for congestion avoidance, lossless forwarding, and tiered traffic scheduling, which are crucial for achieving low-delay operations in satellite-based cloud platforms. In this paper, we propose a Congestion Control method with Lossless Forwarding (CCLF). Addressing the time-varying nature of satellite networks, CCLF implements zero packet loss forwarding by monitoring output queues, aggregating packets, and prioritizing packet scheduling. This approach overcomes traditional end-to-end bottleneck bandwidth limitations, enhances network throughput, and achieves low-delay forwarding for critical Data packets. Compared with the Practical Congestion Control Scheme (PCON), the CCLF method achieves lossless forwarding at the network layer, reduces the average flow completion time by up to 41%, and increases bandwidth utilization by up to 57%.

With more satellites, richer payload resources, and more diverse service functions, satellite systems are increasingly operated as large space–ground networks. These networks must schedule arriving missions under changing topology, gateway access, beam availability, weather-affected links, spectrum compatibility, and mission time windows. Offline optimization can compute high-quality schedules when the mission set, satellite visibility windows, and resource states are known before execution, but repeated replanning is costly for asynchronous arrivals. Online heuristics make faster decisions from local route rules, but they do not evaluate how an accepted service path changes the capacity left for later requests. Reinforcement-learning schedulers can adapt from delayed scheduling outcomes. However, many generic policies rely on fixed-step state updates or flat compound-action scores, whereas online satellite scheduling makes decisions at irregular arrivals over continuously evolving topology and capacity-coupled service paths. We propose TopoAgent, an online reinforcement-learning agent for heterogeneous satellite mission scheduling. TopoAgent models each request as a service-path decision, propagates compound feasibility through the satellite–gateway–beam hierarchy, and uses a capacity-aware policy to choose among feasible paths. A deterministic constraint manager places the selected path in time, while SRV guides the policy toward assignments that preserve reusable beam capacity. In a high-fidelity simulator, TopoAgent achieves a 74.7% mission completion rate and a 75.5% high-priority completion ratio over five seeds.