Telecommunications Science ›› 2020, Vol. 36 ›› Issue (9): 51-58.doi: 10.11959/j.issn.1000-0801.2020270

• Research and Development • Previous Articles     Next Articles

A density clustering-based network performance failure big data analysis algorithm

Xiang LI,Yuan LI(),Zifei ZHANG,Zhe YANG   

  1. China Academy of Information and Communications Technology,Beijing 100191,China
  • Revised:2020-06-01 Online:2020-09-20 Published:2020-09-27


Facing frequent network security incidents,how to quickly find abnormal data in massive monitoring database and carry out network failure analysis becomes a research difficulty.A density-based network performance failure big data analysis algorithm was proposed,which extracted key performance characteristic indicators through entropy weight analysis,implemented data shaping through data cleaning and standardization,and extracted abnormal performance data on the basis of DBSCAN clustering algorithm.Relying on the real-time massive backbone network link performance data of multiple domestic operators to validated this algorithm,the results shows that compared with the manually manner,the recognition accuracy of the algorithm proposed to the network performance abnormal data is more than 90%,which can well fit for the analysis of real-time Internet network operation failure.

Key words: network performance, machine learning, density clustering, measurement analysis

CLC Number: 

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