Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency

Abstract

Satellite edge computing has attracted the attention of many scholars, but the limited resources of satellite networks bring great difficulties to the processing of edge-computing-dependent tasks. Therefore, under the system model of the satellite-terrestrial joint network architecture, this paper proposes an efficient scheduling strategy based on task degrees and a resource allocation strategy based on the improved sparrow search algorithm, aiming at the low success rate of application processing caused by the dependency between tasks, limited resources, and unreasonable resource allocation in the satellite edge network, which leads to the decline in user experience. The scheduling strategy determines the processing order of tasks by selecting subtasks with an in-degree of 0 each time. The improved sparrow search algorithm incorporates opposition-based learning, random search mechanisms, and Cauchy mutation to enhance search capability and improve global convergence. By utilizing the improved sparrow search algorithm, an optimal resource allocation strategy is derived, resulting in reduced processing latency for subtasks. The simulation results show that the performance of the proposed algorithm is better than other baseline schemes and can improve the processing success rate of applications.