With the increasing number of satellites in orbit, traditional scheduling method s can no longer satisfy the increasing data demands of users. The timeliness of remote sensing images with large data volumes is poor in the backhaul process through low-earth-orbit (LEO) satellite networks. To address the above problems, we p ropose an edge-computing load-balancing method for LEO satellite networks based on the maximum flow of virtual links. First, the min imum rectangle composed of computing nodes is determined by the source and destination nodes of the transmission task under the configuration of the 2D-Torus topology of LEO satellite networks. Second, edge computing virtual links are established between computing nodes and users. Th ird, the Ford-Fulkerson algorithm is used to obtain the maximum flow of the topology with virtual links. Finally, a strategy is generated for computing and transmission resource allocation. The simulation results show that the proposed method can optimize the total capacity of the multi-node information backhaul in the remote sensing scenario of LEO satellite networks. The effectiveness of the proposed algorithm is verified in several special scenarios.

This paper proposes a complete network capacity analysis framework for Low-Earth-Orbit (LEO) mega-constellations, where a network capacity estimation problem considering the link packet loss rate is formulated with the support of the time-variant network topology model and the task distribution model. The problem is solved in two steps. Firstly, without considering the link packet loss rate, an improved fullypolynomial-time approximation (IFPTA) algorithm is proposed to give a sub-optimal solution of the multicommodity-flow (MCF) problem, in which a simpler definition of a commodity is given and proved to be equivalent to the original problem. Secondly, a Jackson-network-based capacity fallback approach is proposed for controlling link packet loss rate beneath a given threshold. Numerical results illustrate the superiority of the proposed IFPTA algorithm in accuracy and time complexity compared to existing solutions. In addition, the capacity characteristics of mega-constellations are analyzed by utilizing the proposed capacity analysis framework, including the relationship between constellation size and capacity, network capacity bottleneck, and influence of task distributions.INDEX TERMS LEO mega-constellation; network capacity; multi-commodity flow problem; Jackson network. I.

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