A novel node selection method for wireless distributed edge storage based on SDN and a maldistributed decision model

Yejin Yang; Miao Ye; Qiuxiang Jiang; Peng Wen; Yejin Yang; Miao Ye; Qiuxiang Jiang; Peng Wen

doi:10.3934/era.2024056

Electronic Research Archive

2024, Volume 32, Issue 2: 1160-1190. doi: 10.3934/era.2024056

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A novel node selection method for wireless distributed edge storage based on SDN and a maldistributed decision model

1.
School of Information and Communications, Guilin University of Electronic Technology, China
2.
Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, Guilin University of Electronic Technology, Guilin 541004, China
3.
Ministry of Education Key Laboratory of Cognitive Radio and Information Processing, Guilin University of Electronic Technology, Guilin 541004, China

Received: 26 September 2023 Revised: 20 December 2023 Accepted: 08 January 2024 Published: 29 January 2024

In distributed edge storage, data storage data is allocated to network edge devices to achieve low latency, high security, and flexibility. However, traditional systems for distributed edge storage only consider individual factors, such as node capacity, while overlooking the network status and the load states of the storage nodes, thereby impacting the system's read and write performance. Moreover, these systems exhibit inadequate scalability in widely adopted wireless terminal application scenarios. To overcome these challenges, this paper introduces a software-defined edge storage model and a distributed edge storage architecture grounded in software-defined networking (SDN) and the Server Message Block (SMB) protocol. A data storage node selection and distribution algorithm is formulated based on a maldistributed decision model that comprehensively considers the network and storage node load states. A system prototype is implemented in combination with 5G wireless communication technology. The experimental results demonstrate that, in comparison to conventional distributed edge storage systems, the proposed wireless distributed edge storage system exhibits significantly enhanced performance under high load conditions, demonstrating superior scalability and adaptability. This approach effectively addresses the scalability limitation, rendering it suitable for edge scenarios in mobile applications and reducing hardware deployment costs.
- distributed edge storage,
- software-defined networking,
- node selection algorithm,
- 5G wireless communication
Citation: Yejin Yang, Miao Ye, Qiuxiang Jiang, Peng Wen. A novel node selection method for wireless distributed edge storage based on SDN and a maldistributed decision model[J]. Electronic Research Archive, 2024, 32(2): 1160-1190. doi: 10.3934/era.2024056

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Abstract

In distributed edge storage, data storage data is allocated to network edge devices to achieve low latency, high security, and flexibility. However, traditional systems for distributed edge storage only consider individual factors, such as node capacity, while overlooking the network status and the load states of the storage nodes, thereby impacting the system's read and write performance. Moreover, these systems exhibit inadequate scalability in widely adopted wireless terminal application scenarios. To overcome these challenges, this paper introduces a software-defined edge storage model and a distributed edge storage architecture grounded in software-defined networking (SDN) and the Server Message Block (SMB) protocol. A data storage node selection and distribution algorithm is formulated based on a maldistributed decision model that comprehensively considers the network and storage node load states. A system prototype is implemented in combination with 5G wireless communication technology. The experimental results demonstrate that, in comparison to conventional distributed edge storage systems, the proposed wireless distributed edge storage system exhibits significantly enhanced performance under high load conditions, demonstrating superior scalability and adaptability. This approach effectively addresses the scalability limitation, rendering it suitable for edge scenarios in mobile applications and reducing hardware deployment costs.

References

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