A Fault Prediction Method Based on Load-capacity Model in the Communication Network

Ji, Xilin; Xiao, Liang; Zhou, Yun; Huo, Yonghua; Shi, Xiaodan; Yang, Yang

doi:10.23919/apnoms.2019.8893104

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Sun et al [24] described the mathematical model of the parallel system and voting system and proposed that the parallel system can improve the reliability of the system more than the voting system. Ji et al [25] analyzed the mechanism of fault cascading propagation in communication networks and proposed a fault prediction model based on the load capacity model. Zhang et al [26] proposed a novel load capacity model with a tunable proportion.…”

Section: Relevant Workmentioning

confidence: 99%

Reliability Analysis and Redundancy Optimization of a Command Post Phased-Mission System

Dui

Zhang

2022

Mathematics

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This paper divides the execution process of the command post system into four stages: information acquisition, information processing, decision control and response execution. It combines multilayer complex networks with a phased-mission system. Most studies have only evaluated the reliability of phased-mission systems. This paper evaluates and optimizes the reliability of a phased-mission system. In order to improve the mission success rate and maximize the reliability of a command post system, this paper provides a multitasking node criticality index, and the index is used to identify the key nodes in the command post’s four-stage network Then, the hot backup system of the node is selected to determine the redundant structure of the key node. Under the constraints of the operation and maintenance costs of key nodes, with the goal of maximizing the reliability of the information processing network layer, the multitask redundancy optimization model of each stage is established. Finally, the reliability of the missions before and after redundancy optimization is compared, using the case analysis of the four-layer network to verify the effectiveness of the proposed model.

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Section: Relevant Workmentioning

confidence: 99%