## 5 Performance Evaluation
### Experiment Setup
Then, we compare the performance of COIN-LEO with the other baseline strategies. The inference throughput and time of every strategy are shown in Figure 9a,b, respectively. As shown in Figure 9a, the inference throughput of COIN-LEO is the highest among all the strategies in every scenario, which clearly demonstrates the effectiveness of our strategy in improving throughput. Compared to the equally splitting algorithm, our proposed COIN-LEO showed an average improvement of 17.285% in inference time. Furthermore, compared to the random allocation algorithm, our improvement was even higher, with an average increase of 35.787%. In the special scenario we set, the algorithm's improvement was most pronounced, showing a 35.665% increase compared to the equal allocation algorithm. These results highlight the superior performance of our algorithm in relatively complex operational environments. Our approach outperformed the equal distribution strategy,  
Figure 7: Throughput of different strategy. showcasing the benefits of our optimized resource allocation and model-partitioning techniques. With these improvements, we achieved higher throughput, indicating more efficient and faster model inference in the distributed environment. Figure 8: Rewards and smoothed rewards when training COIN-LEO. Figure 9: Throughput and inference time for different scenarios. (**a**) Throughput for different scenarios; (**b**) inference time for different scenarios. We believe that one of the reasons for such a performance is that COIN-LEO can allocate tasks more efficiently based on the remaining resources on each satellite, as shown in Table 1. By considering the CPU and memory occupation of each satellite, COIN-LEO intelligently distributes the inference tasks to maximize the utilization of available resources. This allows for a more balanced and optimized workload distribution among the satellites, leading to improved inference efficiency and overall system performance. In Table 1, we show the average and median durations for each satellite to perform submodel inference in scenarios with CPU and memory occupation rates of 12-38, 50-45, 2-32 and 50-43, respectively. In the last column, we calculate the percentages relative to the results using the random distribution strategy as the baseline for other strategies. Since the second and fourth satellites in our scenarios have relatively high CPU and memory occupancy rates, logically they should be assigned relatively lighter inference tasks. The COIN-LEO algorithm indeed met our expectations. As observed, the COIN-LEO algorithm assigned the heaviest inference task to the most resource-abundant satellite, which was the third one, while the other satellites were responsible for inference tasks on the scale of tens of milliseconds. This allocation strategy fully utilized the remaining resources. In contrast, the equal distribution strategy performed poorly in this case. The random distribution strategy even assigned the heaviest task to the heavily loaded fourth satellite, which undoubtedly affected the final inference speed. Next, Figure 10 shows the cumulative distribution function (CDF) of time consumed for every task plot for different strategies under various scenarios. These CDF plots provide a visual representation of how the strategies perform in terms of inference time distribution across different satellites. By comparing the CDF plots for each strategy, we can clearly observe the differences in their performance and assess the effectiveness of COIN-LEO in improving inference efficiency across different resource-occupation scenarios. From the CDF plot, we can observe that when using our COIN-LEO strategy, the inference process of every task is faster compared to other strategies. The curve representing our strategy generally starts at a higher point on the \(x\)-axis, indicating that a larger proportion of inference times are lower compared to the other strategies.