灾害场景下基于SDN和无人机辅助的无线边缘存储系统性能优化方法

doi:10.11896/jsjkx.240900004

Abstract

Abstract: Traditional edge distributed storage systems often suffer from cumbersome network configuration and high operational overhead in measuring network state information.During peak demand periods for data storage and retrieval by terminal devices,network links can become overloaded,adversely affecting data transmission performance.Furthermore,existing distributed sto-rage systems typically consider only the remaining storage space of nodes when selecting storage nodes,neglecting the impact of network state and node load on system storage performance.To address these issues,this paper designs and implements an edge-distributed storage system assisted by software-defined network(SDN) and drones.The system uses SDN technology to measure network state,node load,and storage node load information.Drones fly above heavily loaded network nodes to offload traffic and balance the load across different links.For the selection of heavily loaded network nodes and storage nodes,this paper proposes a node selection algorithm based on a multi-attribute decision model that comprehensively considers network state and node load.The algorithm identifies heavily loaded network nodes and suitable storage nodes,and deployment of drones helps achieve traffic offloading and load balancing.Experimental tests on a wireless Mesh network topology demonstrate that the proposed wireless edge-distributed storage system outperforms existing edge-distributed storage systems in terms of storage performance.The proposed system significantly reduces storage time and maintains good performance even under increased traffic load,demonstrating excellent load-balancing capabilities.

Key words: Edge distributed storage, Software defined network, Node selection, Load balancing, Wireless Mesh

CLC Number:

TP393

SUN Shiquan, YE Miao, ZHU Cheng, WANG Yong, JIANG Qiuxiang. Performance Optimization of Wireless Edge Storage System Based on SDN and Drone Assistance in Disaster Scenarios[J].Computer Science, 2025, 52(11): 306-319.

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