# A Green Computing Business Aggregation Strategy for Low Earth Orbit Satellite Networks
## 5 Simulation Analysis
### Strategy Simulation and Analysis  
Figure 5 illustrates the performance of the proposed strategy in business aggregation. Firstly, as the business volume increases, the number of satellites involved in processing also rises correspondingly to meet the growing demand, ensuring sufficient computational capacity. Secondly, the strategy tends to select satellites with initially higher business volumes as aggregation points. This approach effectively minimizes the transmission energy required during the aggregation process by leveraging satellites that are already heavily loaded. Lastly, after the business aggregation is complete, the load disparity among the working satellites is significantly reduced. This improvement can be attributed to the reward-punishment mechanism embedded in the strategy, which ensures a fair and efficient distribution of tasks. By balancing the workload, this mechanism not only maximizes the overall system efficiency but also enhances the stability and reliability of the network under varying traffic conditions.  
To validate the performance advantage of the proposed strategy, this paper compares it with the following baseline strategies:  
* Ant Colony Strategy (_AC_): This strategy generates aggregation decisions and resource aggregations by simulating ant colony behavior for global optimization.
* MDP-QL Strategy (_MDP-QL_): This strategy constructs a Q-table to generate aggregation decisions and resource aggregations, aiming to achieve global optimization.  
Figure 6 analyzes the performance of different optimization strategies. Firstly, as the business volume increases, the overall optimization objectives of all strategies exhibit a downward trend and eventually converge. This decline is primarily caused by the increased transmission energy consumption and processing delay associated with higher business volumes, which leads to a reduction in the EDR value. Secondly, the proposed strategy consistently outperforms other strategies under varying business volume conditions. This advantage stems from the use of a reinforcement learning approach, which effectively balances the exploration and exploitation of optimal solutions in continuous state spaces, thereby achieving superior optimization results.  
Figure 4: Satellite simulation scene map.  
Table 3 shows that the proposed aggregation strategy performs best in low-traffic scenarios. When the business volume is low (e.g., 1000), the strategy achieves significant energy savings of 47.87% with a minimal delay increment of 0.0161 s. This highlights its efficiency in conserving energy while maintaining a low latency.  
As the business volume increases, energy savings decrease significantly due to a reduction in the number of dormant satellites. For instance, at 5000 businesses, the energy reduction ratio drops to 11.34%, and at 9000, it further decreases to 4.36%. Simultaneously,  
Figure 5: Performance of the recommended aggregation strategy.  
Figure 6: Energy–Delay Ratio (EDR) versus number of businesses for different optimization strategies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Businesses** & **Delay Increment (s)** & **Energy Reduction Ratio** \\ \hline
1000 & 0.0161 & 47.87\% \\
3000 & 0.0227 & 22.01\% \\
5000 & 0.0203 & 11.34\% \\
7000 & 0.0304 & 7.83\% \\
9000 & 0.0299 & 4.36\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Delay increments and energy reduction ratios for different business volumes.  
the delay increment stabilizes, peaking at 0.0304 s at 7000 businesses and slightly falling to 0.0299 s at 9000, which can be attributed to the activation of more satellites to handle the traffic load. These results demonstrate the strategy's effectiveness in low-traffic scenarios but also its limitations in high-traffic conditions.