In recent years, the terrestrial Internet has rapidly developed, with applications such as smart cities and environmental monitoring ^[1][2][1,2] attracting widespread attention. However, terrestrial Internet services are concentrated mainly in urban areas, and providing quality services in remote areas, such as islands, oceans, and deserts, is challenging. The instabilities of terrestrial networks become apparent when faced with natural disasters, such as floods and earthquakes [3]. Satellite networks have significant advantages, such as wide global coverage and high destructive resistance. They have been used in emergency communications, navigation, positioning, and smart city applications ^[4][5][4,5], effectively compensating for the lack of terrestrial Internet services and providing critical support for 6G global interconnection ^[6][7][6,7]. However, previous research has considered primarily satellite networks as relay networks in a satellite–ground fusion architecture. Ground access terminal services and raw satellite remote sensing data, for example, are transmitted back to the ground cloud center for unified processing, despite the possibility of performing tasks directly on the satellite ^[3][8][9][3,8,9]. Both result in significant delays and the waste of valuable satellite communication resources ^[10][11][10,11].

Mobile edge computing (MEC) [12] is an emerging architecture that brings the traditional cloud-centric computing model down to the edge of the user node. It provides the services and computing power needed at the user’s periphery, creating service edge nodes with low latency and high processing rate for a better quality of service (QoS). MEC principles and low-latency, high-capacity low-Earth orbit (LEO) satellite networks are combined to create edge computing satellites (ECS). In this manner, satellite acquisition data can be processed in real-time at satellite edge nodes ^{[4][8][9][13]}[4,8,9,13], conserving bandwidth and allowing for quick mission reaction. The TIANSUAN Constellation test satellite [14], co-chaired by Beijing University of Posts and Telecommunications and other institutions, for example, has validated remote sensing image inference and computing services on orbit with the equipped KubeEdge and Sedna edge intelligence inference platform. The researchers also compared it with traditional ground-based backhaul analysis strategies and verified that on-orbit edge processing can effectively reduce transmission traffic and latency. At the same time, they also investigated the on-orbit verification of the distributed cognitive service-oriented architecture for the 6G core network.

However, satellite on-orbit processing tasks face the challenge of a single satellite’s limited computing power, resulting in delayed processing of tasks and difficulties in meeting user requirements due to long task delays. Additionally, reducing energy consumption in satellite edge computing is a major concern for scholars ^[15][16][15,16]. Therefore, utilizing multiple satellites for assisted computing is a valuable research direction to achieve smaller latency and energy consumption.

2. Task Scheduling in Satellite Edge Computing

Task scheduling in edge computing can be divided into two categories: static task scheduling and dynamic task scheduling [17]. When task information and network information are known, static task scheduling can be used directly for task offload scheduling. Dynamic task scheduling involves reassigning the scheduling policy at each scheduling moment when the number of tasks, network information, and other factors change at any time. Static tasks that are offloaded to the edge can be classified into two types: independent tasks and dependent tasks [18]. Independent tasks can be split into multiple tasks processed in parallel, and each node returns the result after completing the task processing. In contrast, dependent tasks include several subtasks with logical dependencies. In addition, the processing of a subtask can be performed when all the preceding subtasks of the subtask are completed. Due to the constraint relationship among subtasks, scheduling dependent tasks is more challenging than scheduling independent ones. With the rise of big data, dependent tasks are becoming more common, including target tracking and identification, which require combining and processing multiple tasks [18]. As a result, developing effective scheduling strategies for dependent tasks is crucial. Although static task scheduling in terrestrial MEC scenarios has been extensively studied, research on task scheduling for LEO satellite constellations is still in its early stages, particularly for dependent tasks. For independent-type task scheduling, Ren et al. [19] proposed an inter-satellite collaborative computation method for formation-flying satellites. The authors characterized the formation-flying satellite network using a weighted undirected graph, dividing the computational tasks into multiple parallel computational subtasks assigned to each satellite node and solving the delay optimization problem under the energy consumption constraint using the modified particle swarm algorithm (MPSO). However, because the work was limited to formation-flying satellites with a constant topology, its applicability for task scheduling of LEO satellite constellations with dynamic topologies is limited. Chen Wang [11] presented a strategy for LEO satellite collaborative computing. The authors used time-expanded graphs to generate a steady-state matrix for dynamic LEO satellite networks. Furthermore, a generalized discrete algorithm based on transmission capacity and computational power addressed the time-delay optimization problem for multi-satellite collaborative computing applications. The scheduling of independent tasks was simpler because there were no logical dependencies between tasks. For dependent task scheduling, Wu et al. [20] proposed a task collaborative scheduling algorithm for small satellite cluster networks. The research authors assigned dozens of jobs with logical relationships to separate satellites for collaborative computation. Considering that satellite nodes fail, the authors proposed an improved task scheduling strategy based on three heuristic algorithms so that the system can effectively guarantee that all tasks are completed by the deadline as much as possible while also providing some robustness to the scheduling algorithm. The effectiveness of the proposed algorithms was verified by comparing them with genetic algorithms, for example. However, again, the authors considered the same network of small satellite clusters and formation-flying satellites, and the topology between satellites remained fixed, which lacked a reliable reference value for the dynamic LEO satellite network. Guo et al. [21] first characterized the LEO satellite network using the weighted time extended graph (WTEG) model, in which a uniform delay weight parameter was added to each edge in the steady-state graph to analyze the delay of the on-orbit computation and transmission. A directed acyclic graph (DAG) was used to characterize the task model, and the nodes and edges of the task model were mapped to the steady-state graph to find the minimum task completion delay. The authors employed a binary particle swarm algorithm to optimally solve the optimal mapping problem and verify the algorithm’s feasibility in comparison with ground cloud processing and other basic scheduling algorithms. Han et al. [4] constructed a satellite edge cluster computing architecture using LEO and geostationary earth orbit (GEO) satellites as edge nodes for collaborative task computing and characterized the logical relationships and constraints among subtasks using the DAG model. Furthermore, the author designed a scheduling algorithm that considered the dynamic changes in the priority and link bandwidth of subtasks in different time slots. At each scheduling moment, the unresolved subtasks were assigned to the appropriate satellite nodes for processing to ensure that the corresponding metrics of interest were optimized. Most authors included all edge computing satellites in the spectrum of task scheduling assignable nodes in the work mentioned above. In fact, due to factors such as unequal population distribution and varying business demands, the load of each satellite node varies significantly. Enough computing power for new task processing is difficult for high-load satellites.