Task Scheduling of High Dynamic Edge Cluster in Satellite Edge Computing

In a traditional satellite mission processing mode, the data is transmitted to the ground data center for processing. However, with the expansion and enrichment of satellite services, the huge amount of data leads to longer data transmission time and greater bandwidth pressure. Furthermore, time-sensitive satellite missions do not respond in a timely manner. In recent years, the emergence of mobile edge computing (MEC) provides a new mode for users to utilize the computing power of network edge side nodes for data processing. At the same time, the satellite information network has realized the interconnection of low-orbit satellites, realized the integration of computing power of dispersed satellites, and provided enough computing power to assist the processing of satellite big data tasks and time-sensitive tasks. It can be seen that introducing the mobile edge computing model into the satellite information network has become an effective way to solve the above problems. In this paper, we first introduce a satellite moving edge computing framework and analyze the particularity of the scene. Secondly, we modeled the task scheduling problem of mobile edge computing clusters in the satellite dynamic environment and proposed a satellite task time optimization scheduling algorithm based on dynamic priority queue (SDPLS). Experiments show that the algorithm can adapt to highly dynamic satellite network conditions, shorten the task response delay, and realize the efficient utilization of satellite cluster computing resources.