# A Green Computing Business Aggregation Strategy for Low Earth Orbit Satellite Networks
## 1 Introduction  
The 6 th generation mobile networks (6G) are expected to offer higher data transmission rates, broader coverage, and reduced latency to meet the extensive future demands in information transmission and processing [1]. Low Earth Orbit (LEO) satellite networks (LSNs) provide full coverage for remote regions and, compared to other satellite systems, offer lower latencies, making them a critical component of 6G [2].  
Despite these advantages, LSNs face significant challenges due to energy limitations, as LEO satellites rely on solar cells for power, leading to unstable energy supplies as satellites alternate between shadow and sunlight periods. This energy limitation affects the continuous operation and service reliability of LSNs [3].  
To address these energy constraints, efficient solar energy acquisition is crucial. Perovskite-based solar cells have shown potential for improving energy conversion in space applications by increasing solar radiation absorption through optimized heat stimulation [4]. Additionally, effective onboard energy management can further optimize performance. For instance, managing energy usage across sunlit and shadow periods enhances transmission efficiency and coverage in high-mobility, large-scale connection scenarios [5].  
Existing research shows that business aggregation can significantly reduce network energy consumption. The literature [6] points out that due to differences in business needs in different areas, about 45% of LEO satellite nodes can be shut down in areas with small business needs, thereby achieving energy saving effects. However, this study failed to consider how to deal with the resulting increase in latency when node shutdown is performed. Specifically, when some satellite nodes are shut down, services will be forwarded to other nodes, which may cause data transmission delays to increase and affectthe overall performance of the system. Therefore, how to balance system delay while optimizing energy efficiency has become an important research issue.  
Reference [7] further discusses the issue of saving network energy consumption through the establishment of dynamic inter-satellite links. This method avoids unnecessary link idleness by establishing link forwarding services on demand, thereby improving energy efficiency. However, ref. [7] failed to quantify the delay cost caused by such dynamic link establishment. Although this method effectively reduces energy consumption, the latency cost associated with frequent link establishment under high-load conditions has not been quantified, posing a significant challenge in balancing energy savings and delay performance.  
To address the shortcomings identified in references [6; 7], this paper combines their research insights and proposes a business aggregation and resource scheduling strategy based on reinforcement learning. Unlike these studies, this paper introduces a novel metric to quantify the trade-off between latency cost and energy savings, thereby addressing the lack of such quantification in the previous works. This strategy not only aims to reduce energy consumption while optimizing system delay but also takes into account green computing and real-time requirements. The work and contributions of this paper are as follows: First, an energy optimization strategy is proposed under the three-layer network architecture of the control satellite layer, business satellite layer, and user layer. An Energy-Delay Ratio (EDR) indicator is introduced to evaluate the effects of business aggregation optimization. Secondly, the Optimal Consolidation Strategy for Business Efficiency (OCSBE) is proposed. Finally, the effectiveness of aggregation strategies is verified through simulation analysis.