## 5 Performance Evaluation  
### Experiment Setup  
In this section, we set up the experimental test environment to evaluate our approach. We built a small-scale cluster with four nodes on a cloud platform to simulate the scenario of four satellites. Our cloud platform is a self-developed satellite network simulation platform based on cloud computing. It is highly efficient and capable of simulating link connectivity, latency and bandwidth variations. With this platform, we can create realistic scenarios and accurately evaluate the performance of satellite networks under various conditions, allowing us to make informed decisions and optimize the network design and operations. To simulate a realistic low-Earth-orbit satellite environment, we set the inter-satellite link delay to 15 ms, and the inter-satellite link rate is 50 MB/s. The software environment includes Python 3.10, TensorFlow 2.10.0, Keras 2.10.0 and Gymnasium 0.28.1. With this cluster in place, we varied the CPU and memory occupation rates of the environment to create different scenarios. Because there are no other algorithms of collaborative inference for LEO edge computing in the literature, we choose two baseline algorithms for comparison. The first baseline algorithm is called the equally splitting strategy, in which the complete model is split into four submodels that have the same number of layers. The second baseline algorithm is called the randomly splitting strategy, in which the layers of the complete model are randomly split. The strategy for selecting satellites for collaborative inference is the same as COIN-LEO. Through these comparisons, we sought to evaluate the robustness and adaptability of our approach in varying resource-occupation conditions. The goal was to identify how well our model could handle fluctuations in CPU and memory usage and whether it could consistently make efficient decisions for model partitioning in diverse settings. We conducted tests in four different scenarios. In the first scenario, each node's CPU occupation was around 1%, representing an idle state, while the memory occupation was approximately 34%. In the second scenario, the CPU occupation was about 25%, and the memory occupation was around 45%. Moving on to the third scenario, the CPU occupation increased to approximately 50%, while the memory occupation remained at 45%. size of all the images inferred and the total time consumed. It is measured by completing 1000 inference tasks continuously and recording the total time. Furthermore, the inference performance by a single satellite (not collaborative inference) is presented. The inference throughput of every strategy is shown in Figure 7. As shown in Figure 7, the inference throughput was only 0.6423 MB/s when all inference was performed on a single node. In contrast, when using the random distribution strategy for collaborative inference, the throughput reached 0.7506 MB/s. When using the equally splitting strategy for collaborative inference, the inference throughput reached 0.9738 MB/s. And when using our COIN-LEO for collaborative inference, the inference throughput reached 1.1935 MB/s, which is the highest among all the model-splitting strategies. Compared to the single-node inference performance, the improvement in inference throughput is as high as 85.80% with our COIN-LEO strategy. This indicates a significant improvement in inference throughput using distributed collaborative DNN inference, which is because collaborative inference can utilize several satellites' computing resources. Figure 8 illustrates the evolution of rewards during the training process of our algorithm. We conducted 100,000 training iterations with sampling performed every 10 iterations. We used a 'done' return value of true in DRL as an indicator of the end of an episode step. As depicted in Figure 8, the blue line represents the rewards, while the orange line represents the smoothed reward with a window size of 10. From Figure 8, it is evident that COIN-LEO's reward exhibits impressive increase and convergence.