# DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
## 5 Simulation and Results Analysis  
In this simulation, we used SFCSim [36] as the testing platform, an open source NFV resource allocation simulation software based on the NetworkX network simulation library for complex network simulation. The deep reinforcement learning model was built using PyTorch 2.2.1 and operated on a Linux system equipped with a Ryzen 9 5900X, RTX 2080 Ti, and 64 GB of memory. The underlying physical network parameters were set as follows: For the LEO satellite network, the orbital height was set to 550 km with an inclination of 53 degrees. There were 20 different orbits with 18 satellites per orbit, totaling 360 satellites in the network. CPU, memory, and storage space were set to 64 cores, 32 GB, and 128 GB respectively, with a link bandwidth limit of 2.5 Gbps. The service function chain request parameters were set as follows: SFC source and destination nodes were randomly distributed across 10 different geographical locations. The number of VNFs per SFC followed a discrete uniform distribution \(U(3,5)\), traffic demands followed \(U(50,200)\) Mbps, and resource consumption coefficients followed discrete uniform distributions: \(coeff^{CPU}\sim U(1,3)/25\) Mbps, \(coeff^{memory}\sim U(1,3)/25\) Mbps, and \(coeff^{storage}\sim U(1,3)/100\) Mbps. The duration and maximum allowable delay were set as \(lifecycle\sim U(1,10)\) and \(Delay\sim U(1,10)\). The duration of the maximum allowable delay was set as \(lifecycle\sim U(1,10)\) and \(Delay\sim U(1,10)\). \(U(50,100)\). For the algorithm's optimization objectives, we set \(\alpha=0.15\), \(\beta=0.5\), \(\gamma=20\), configuring these parameters to maximize the balance between service traffic revenue and reconfiguration costs while considering delay. The detailed simulation environment parameters are summarized in Table 1.  
This simulation study was conducted in a simulated LEO satellite network environment. Satellite topology information was generated using relevant constellation parameters, which were subsequently transformed into a communication network model with physical network parameters through the integration of the SGP4 satellite toolkit and the open source SFCSim library. User service function chain deployment requests were generated by incorporating service parameters within the SFCSim framework. Leveraging the dynamic deployment capabilities of the SFCSim platform, we trained our algorithmic model in the simulation environment. The neural network architecture was implemented using PyTorch's comprehensive suite of functionalities, with network parameters optimized through the Adam optimizer. Our optimization objective focused on maximizing the operator's cumulative profit, which comprises two components: the accumulated revenue from network service provision, and the capital expenditure incurred due to SFC readjustments. The detailed constraints governing this optimization framework were thoroughly discussed in Section 3. In this study, we compared our approach against ABSA and NSBSO algorithms. The ABSA algorithm [37] considers network function affinity within SFCs and optimizes deployment by co-locating functionally related components on the same node through simulated annealing optimization. The NSBSO algorithm [38] introduces node stability metrics to handle satellite network topology dynamics and employs an iterative greedy approach for solution optimization. We evaluated algorithm performance under the specified environmental conditions, focusing on optimization objectives: operator total profits maximization, capital expenditure from SFC readjustments, and comprising cumulative service provision revenue. As shown in Figure 2 and Table 2, we analyzed these performance metrics across different user request arrival rates.