Resource allocation strategy of space cloud network based on resource clustering

Abstract

SummarySpace information network (SIN) is difficult to fully utilize the limited on‐board resource due to its dynamic and heterogeneous nature, while the traditional resource management methods cannot adapt to the increasingly diverse task requirements. Space cloud network architecture is an effective technology to reduce the pressure on satellite resources. To effectively manage the space cloud network resources, we design a resources allocation strategy based on resource clustering. Firstly, we propose the space cloud network architecture. Then, we propose a genetic algorithm to cluster the space cloud resources. Finally, we propose a dynamic resource allocation method based on reinforcement learning for the dynamic characteristics of space cloud resources. The method improves the reinforcement learning algorithm through dynamic objective optimization to complete the optimization of multiple objectives in the process of space cloud resources allocation. The simulation results show that the algorithm proposed in this paper reduces the task execution delay by an average of 10.5% compared with the original DQN algorithm and increases the execution success rate by 2.17%.