# Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency
## 2 Related Research  
At present, the allocation of edge computing resources has both domestically and internationally become a research focus for scholars. [PERSON] et al. used the Lagrange multiplier method to obtain optimal computing and communication resource allocation, aiming to attain the optimal resource allocation scheme. Deep reinforcement learning-based methods are used to learn optimal offloading decisions [8]. [PERSON] et al. proposed a software-defined satellite-terrestrial network (STN) to jointly manage network, cache, and computer resources, and used a deep Q-learning approach to solve it [9]. [PERSON] et al. proposed a three-layer network architecture combined with the software-defined network model to guide inter-satellite link connection and inter-satellite link resource scheduling. They proposed a K-means algorithm (AKG) and a spanning tree algorithm based on breadth-first search (BFST) [10]. [PERSON] et al. proposed a new algorithm for joint task offloading and communication computing resource optimization (JTO-CCRO). The resource optimization subproblem is a convex optimization problem that can be solved by the Lagrange multiplier method [11]. [PERSON] et al. proposed a new MEC framework for a terrestrial satellite Internet of Things (IoT). This framework utilizes Terrestrial Satellite Terminals (TSTs) to divide the computation offloading of Internet of Mobile Devices (IMDs) to low Earth orbit satellitesinto two stages: ground and space [12]. [PERSON] et al. proposed a dynamic offload strategy to minimize the overall latency of tasks in the satellite edge computing (SEC) network and jointly optimized the task offloading strategy, computing resources, and transmit power based on Lyapunov theory [13]. [PERSON] et al. studied the dynamic resource allocation problem of virtual network functions (VNFs) in the satellite edge cloud to minimize both network bandwidth cost and service end-to-end latency [14]. [15; 16; 17; 18; 19; 20] [PERSON] et al. studied the problem of user computing offloading in the low earth orbit (LEO) satellite network. They formulated a joint optimization problem to minimize task completion latency and energy consumption. However, this literature only considered the processing of the overall task and did not consider the dependencies between tasks. In addition, [PERSON] et al. allocated an execution time for each task according to task soft deadlines and task dependency constraints, solving the problem of task scheduling difficulties caused by task dependency constraints. They proposed an adaptive offloading of dependent tasks in a mobile edge computing environment and scheduling algorithm [21]. [PERSON] et al. proposed a novel Application-driven Task Offloading Strategy (ATOS) based on deep reinforcement learning. This strategy achieves joint optimization by adding a preliminary sorting mechanism and proposes a heuristic algorithm to determine the processing order of parallel subtasks by introducing a new factor [22]. [PERSON] et al. proposed a constrained multi-objective computation offload optimization solution to solve the task dependency problem under limited resources [23]. [PERSON] et al. proposed a task scheduling method based on improved evolutionary computation that minimizes the execution time of applications [24], and [PERSON] et al. proposed an intelligent task offloading scheme leveraging off-policy reinforcement learning empowered by a Sequence-to-Sequence (S2S) neural network [25]. [PERSON] et al. solved dependent task offloading and edge computing in multi-user scenarios. The task offloading problem is modeled as a Markov decision process (MDP) [26]. However, this literature only considered the processing of task dependencies and did not consider the resource allocation of tasks. In summary, when multiple users unload a large number of latency-sensitive tasks at the same time, the dependencies between tasks will greatly affect the completion time of the application.