## 5 Evaluation
### Evaluation Result
#### 5.2.2 Workload Deploy and Data Transmission  
Due to the heterogeneity of satellite hardware resource allocation, different workers' computing power and out-bandwidth vary. Generally speaking, workers with stronger computing power should bear more of a computing workload. In contrast, workers with higher out-bandwidth should transmit greater data volumes to synchronize inference processes among workers and minimize the idle time of each worker as much as possible. To observe and analyze the relationship between computing workload deployment and workers' computing powers, as well as the relationship between data transmission volume and worker out-bandwidth, we separately calculated the computing workload distribution and data transmission situation for VGG16 and AlexNet using our method, EdgeFlow, and CoEdge.  
When analyzing the relationship between computing workload distribution and worker computing power, we normalize the environmental configuration; the detailed calculation process is shown as (18). It is worth noting that when calculating the workload, we use the input height \(H_{int}\) of the network layer; when calculating the data transmission volume, we use the output height \(H_{out}\) of the network layer.  
\[\zeta_{i}=\frac{\sum_{j=1}^{N}\frac{h_{in}^{i}}{H_{in}}}{N}, \tag{17}\]  
Figure 8: The comparison result of inference latency.  
\[\xi_{i}=\frac{\zeta_{i}}{\sum_{j=1}^{M}\zeta_{j}}, \tag{18}\]  
In (17), \(\zeta_{i}\) is the average proportion of computing workload allocated to worker \(w_{i}\) at each layer, while \(h_{i}^{j}\) is the input height of \(w_{i}\) in layer \(l_{j}\) for executing a sub-model. According to \(\zeta_{i},i=1,\ldots,M\), a vector \(\zeta=\{\zeta_{1},\ldots,\zeta_{M}\}\) containing the average computing workload proportion of each worker per layer can be obtained. We use (18) to normalize the vector \(\zeta\). Similarly, we can standardize the computing power of each worker using (19); \(\tilde{\eta}_{i}^{p}\) is the standardized worker's computing power.  
\[\tilde{\eta}_{i}^{p}=\frac{p_{i}}{\sum_{i=1}^{M}p_{i}}, \tag{19}\]  
As shown in Figure 9, it is evident that the computation workload distribution obtained by our method is more in line with the computing power distribution of the worker.  
Like our logic of analyzing computation workload distribution and worker computing power, we analyze the relationship between data transmission volume and worker out-bandwidth. In (20), \(\xi_{i}\) is the average ratio of the volume of data output from the worker \(w_{i}\) to other workers at each layer to the total output data volume. \(h_{i}^{j}\) is the height at which the worker outputs to other workers in layer \(l_{j}\), as the data transmission from the worker to itself does not involve bandwidth and can be ignored. (21) and (22) are standardized operations, just like standardizing the distribution of computation workload. \(\tilde{\xi}_{i}\) (21) is the standardized value of the worker's output data volume, and \(\tilde{\theta}_{i}^{p}\) in (22) is the standardized value of the worker's out-bandwidth.  
\[\xi_{i}=\frac{\sum_{j=1}^{N}\frac{h_{i}^{j}}{H_{out}}}{N}, \tag{20}\]  
\[\xi_{i}=\frac{\xi_{i}}{\sum_{j=1}^{M}\xi_{j}}, \tag{21}\]  
\[\tilde{\vartheta}_{i}^{p}=\frac{b_{i}}{\sum_{i=1}^{M}b_{i}}, \tag{22}\]  
As shown in Figure 10, we have explained the relationship between the output data volume of the worker and its out-bandwidth. Based on our method, we can see that the volume of data output generated by workers is most closely aligned with its out-bandwidth.  
Figure 9: The computing workload distribution and workers’ computing power distribution.