# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 1 Introduction  
With the rapid development of satellite communication and networking technologies, satellite networks have become a crucial part of the global information infrastructure, supporting applications in communications, navigation, and remote sensing [1, 2]. In particular, the deployment of large Low Earth Orbit (LEO) satellite constellations in recent years has further underscored the vital role of satellite networks in worldwide connectivity and Earth observation [3]. Traditional satellite task processing methods rely on ground stations for centralized mission planning and single-satellite execution [4]. This paradigm requires frequent satellite-ground communications and has limited real-time responsiveness [5]. Recent advances in integrated space-ground network architectures enable resource sharing and cooperative processing directly between satellites [6, 7]. Consequently, multi-satellite cooperative computing has emerged as a new research focus, wherein multiple satellitesinteract via inter-satellite links to distribute and execute computational tasks collaboratively, essentially forming a \"space-based cloud\" computing environment [8; 9; 10]. By decomposing complex missions and distributing subtasks across satellites for parallel execution, multi-satellite cooperation markedly reduces the burden on any single satellite and diminishes reliance on ground control, thereby significantly improving real-time responsiveness [11]. Therefore, incorporating multi-satellite cooperative computing for task decomposition and resource allocation has become a key avenue to meet the demands of future large-scale and complex space missions [12]. With the rapid development of satellite communication and networking technologies, satellite networks have become a crucial part of the global information infrastructure, supporting applications in communications, navigation, and remote sensing [1]. However, efficient multi-satellite cooperative computing is challenging regarding task decomposition, resource allocation, and real-time scheduling [13; 14]. First, determining how to partition a complex mission into subtasks and assign them to different satellites is a complicated decision problem that must account for heterogeneous satellite resources and inter-task dependencies [15]. Second, the multi-satellite network environment is highly dynamic: satellite positions and link conditions change over time, and task requests may arrive unpredictably. This dynamism demands that scheduling strategies adapt rapidly [16]; otherwise, a precomputed plan can quickly become suboptimal or invalid. In addition, the joint optimization problem of multi-satellite collaboration is typically non-convex, involving discrete task assignment and continuous resource allocation variables, which makes it exceptionally computationally complex. Indeed, the complexity of typical multi-satellite scheduling grows exponentially with the number of satellites and tasks, rendering it an NP-hard problem. This complexity hampers the ability to obtain exact optimal solutions within real-time constraints. In response to the above challenges, researchers have proposed various heuristic and meta-heuristic algorithms (e.g., genetic algorithms [17], tabu search [18], multi-objective evolutionary algorithms [19; 20]) to tackle non-convex task allocation challenges. These algorithms have achieved specific progress in computational efficiency. However, most such studies rely on simplified assumptions and static scenarios without adequately accounting for real-time changes in satellite states and task demands. As a result, their scheduling strategies may perform suboptimally in actual dynamic environments and often focus on optimizing only a single performance metric (e.g., minimizing task completion time) without considering other objectives such as energy consumption. Overall, current methods still exhibit shortcomings in aspects like real-time responsiveness, solving complex non-convex optimizations, and cross-system collaboration.