# Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency
## 1 Introduction  
At present, the rapid development of the Internet of Things and 5G mobile technology has promoted a wide range of applications, such as augmented reality (AR)/virtual reality (VR), 4K/8K video transmission, and other emerging applications [1; 2; 3]. These applications require low latency, high efficiency, and network reliability. When large amounts of data are offloaded to remote cloud centers, the user's quality of experience (QoE) will be affected [4]. The mobile edge computing (MEC) proposed by the European Telecommunications Standardization Organization (ETSI) has attracted people's attention. MEC involves migrating computing capabilities to the edge of mobile networks, effectively reducing response latency [5]. The terrestrial network also has shortcomings. The terrestrial network may not be able to provide ubiquitous coverage for forests, rural areas, mountains, and marine areas. Moreover, the anti-interference and anti-disaster capabilities of the terrestrial network are weak. In the face of emergencies, the traditional terrestrial network may encounter problems such as traffic overload or users moving out of the coverage of the base station. Satellite communication has the advantages of a long communication distance, a wide coverage area, and not being affected by terrain and geological disasters, etc. This enables the satellite network to provide resilient support when the terrestrial network is insufficient to meet demand. Satellite nodes have significant advantages in providing large-scale broadcast services for the ground and efficiently distributing multimedia services. In addition, the existence of the satellite-ground link provides a new network backhaul option for the ground network. When the capacity of the ground network is saturated or fails,the satellite network can provide a space-based backhaul service; that is, the ground data transmission is sent back to the ground through the satellite. This backhaul method can effectively relieve the pressure on the ground network and ensure the smoothness and stability of data transmission [2; 3; 4; 5; 6]. The core idea of introducing edge computing technology in the satellite-ground collaborative network is to extend the cloud computing platform to the network edge or even the user terminal itself. The multi-level, heterogeneous computing resources, global coverage, and optimized task unloading and resource allocation of the satellite network can provide users with more efficient and convenient computing services and improve their service experience. At the same time, it also helps reduce the redundant traffic of the network and optimize the performance of the network [5; 6; 7]. Due to the limited computing power of local devices, when a large number of user devices need computing tasks, users prefer to offload tasks to satellite edge computing nodes as much as possible, because this can reduce processing delays. However, due to the limited satellite resources, it is a challenge to develop efficient task scheduling and resource allocation algorithms, reduce processing delays, and improve the application success rate in satellite network edge computing. Therefore, the main contributions of this paper are as follows:  
* Considering the interdependence of tasks in satellite edge computing, tasks can be processed at satellite edge nodes. An application completion rate model is established in this paper. * A scheduling algorithm based on the in-degree of tasks (TBID) is proposed, and the processing order of tasks is obtained. The subtasks with an in-degree of 0 in the directed acyclic graph (DAG) are scheduled each time to form a subtask set, and the tasks in the task set are processed each time and then deleted. The application program chooses whether to process in the satellite or locally according to the deadline delay. * An improved sparrow search algorithm is proposed to solve the resource allocation problem of subtasks scheduled in each iteration. The opposition-based learning is introduced to increase the initial population diversity.