面向5G海量网管数据的故障溯源技术

doi:10.13190/j.jbupt.2018-193

北京邮电大学学报 ›› 2018, Vol. 41 ›› Issue (5): 131-136,142.doi: 10.13190/j.jbupt.2018-193

面向5G海量网管数据的故障溯源技术

陈墨¹, 金磊², 龚向阳¹, 满毅²

1. 北京邮电大学网络与交换技术国家重点实验室, 北京 100876;
2. 北京邮电大学电子工程学院, 北京 100876

收稿日期:2018-08-10 出版日期:2018-10-28 发布日期:2018-11-20
作者简介:陈墨(1990-),男,博士生,E-mail:chenmo2015@bupt.edu.cn;龚向阳(1970-),男,教授,博士生导师.
基金资助:
国家自然科学基金项目（61471055）

Research on Fault Tracing Technology for 5G Mass Network Management Data

CHEN Mo¹, JIN Lei², GONG Xiang-yang¹, MAN Yi²

1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2018-08-10 Online:2018-10-28 Published:2018-11-20

摘要/Abstract

摘要： 针对第5代移动通信系统（5G）环境下海量网管数据溯源难、关联挖掘冗余度大的问题，结合时间约束、滑动时间窗和分类层次技术，提出了一种基于网络拓扑的时序告警关联挖掘算法.该算法可以有效缩减候选集，实现对海量网管数据高效压缩和快速溯源.仿真结果表明，改进后的故障溯源候选集在拓扑上具有实际关联性，对比其他关联算法更有效.

关键词: 资源拓扑, 告警关联, 时序告警, 网管数据

Abstract: Aiming at solving the problem of difficult traceability of mass network management data and large redundancy of correlation mining in the fifth generation of mobile communications system (5G) environment, sequential alarm correlation mining algorithm based on network topology was proposed by combining with the time constraint, sliding time window and classification hierarchy technology, which effectively reduced candidate sets, and realized the efficiently compressing and rapidly tracing the mass network management data. Simulation verified that the improved candidate set of fault tracing had actual correlation in topology, which was more effective and reliable than other correlation algorithms.

Key words: resource topology, alarm association, timing alarm, network management data

中图分类号:

TN911.22

陈墨, 金磊, 龚向阳, 满毅. 面向5G海量网管数据的故障溯源技术[J]. 北京邮电大学学报, 2018, 41(5): 131-136,142.

CHEN Mo, JIN Lei, GONG Xiang-yang, MAN Yi. Research on Fault Tracing Technology for 5G Mass Network Management Data[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(5): 131-136,142.

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面向5G海量网管数据的故障溯源技术

Research on Fault Tracing Technology for 5G Mass Network Management Data

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