# An On-Orbit Task-Offloading Strategy Based on Satellite Edge Computing
## 2 Related Works  
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. For the purpose of this paper, we focus on static task scheduling. 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, [PERSON] 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. [PERSON] [11] presented a strategy for LEO satellite collaborative computing. The authors used time-expanded graphs to generate a steady-state matrix for dynamic LEOsatellite 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, [PERSON] 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. [PERSON] 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.