# Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority
## 1 Introduction  
Sixth-generation technology combines fifth-generation technology with satellite networks to achieve global coverage and is considered a cheap and fast internet technology. The goal of 6G technology is to provide mobile users with multimedia and networked all-weather information services [1; 2; 3]. Applying edge computing technology to both ground networks and satellite networks endows the network with characteristics such as low latency, low energy consumption, high speed, and real-time insight into wireless networks [4]. This will undoubtedly be extensively utilized in the architecture of 6G networks. The introduction of edge computing technology into the satellite network, that is, the extension of the cloud computing platform to the network edge, can provide users with heterogeneous computing resources and enable users to obtain computing services anywhere in the world, thereby improving user service experience and reducing redundant network traffic [5]. Due to the limited computing power and energy consumption of Mobile Edge Computing (MEC) servers, only a portion of service requests can be deployed at the same time, and the amount of computing resources allocated to each service is also limited. End users wish to offload as many tasks as possible to satellite edge computing nodes to reduce transmission latency and enhance user experience. To make full use of the resources of satellite MEC servers and enhance user experience, reasonable task deployment algorithms have become a key focus of current research. [PERSON] et al. proposed a system cost model for Low-Earth-Orbit (LEO) satellite edge computing systems, which can be represented as a mixed-integer nonlinear programming problem. The model decomposes the problem intocomputation offloading and resource allocation and then proposes the Joint Computation Offloading and Resource Allocation (JCORA) strategy. Experimental results show that this model can significantly reduce the average cost of a system [6]. [PERSON] et al. proposed a game theory approach to optimize the computational offloading strategy in satellite edge computing, constructing a computational offloading game framework. When response time and energy consumption based on computing tasks are taken as indicators of optimized performance, the average cost of equipment can be significantly reduced [7]. [PERSON] et al. proposed a hybrid cloud and edge computing LEO satellite (CECLS) network with a three-layer computing architecture and studied the computing offload decision to minimize the total energy consumption of ground users [8]. However, some new applications have emerged that require a large amount of computing resources. For example, virtual reality and HD video require a large amount of computing resources for rendering and video encoding and decoding [9]. Autonomous vehicles rely on large amounts of computing resources for automatic control [10]. Deep learning tasks deployed on IoT devices rely on large amounts of computing resources for model training [11; 12]. The above tasks cannot be supported by a single MEC node due to their huge computing loads. These tasks can be optimized and selected for deployment to the remote cloud through algorithms. However, due to the long transmission distance between the end user and the cloud server, the transmission delay is relatively large. Therefore, when allocating tasks, they are no longer deployed in the remote cloud, but are split into multiple subtasks that are assigned to satellite nodes, which can make full use of the satellite resources, thus significantly reducing the latency of task execution. In this paper, the correlation of subtasks is represented by a directed acyclic graph (DAG), and the DAG graph is always single-input and single-output. The essence of multi-DAG task scheduling is to reasonably arrange subtasks with priority relationships, map them to appropriate nodes for execution, and achieve optimization in terms of resources or execution time.