Research on Cloud Service Monitoring Based on Machine Learning Algorithm in 5G Era
The number of app services identified by dpi in the whole network is up to more than 20,000.It is very costly to monitor and trace the full amount of abnormalities and trace the ori-gin of more than 20,000 services.It is often only for some key small categories of services to guarantee,monitor and analyze,which will lead to the failure of other small categories of serv-ices to detect abnormalities and trace the origin in time,affecting users'online perception and causing complaints.Based on this,we proposed a cloud service abnormal monitoring method based on machine learning Layard criteria and T-value sorting algorithm.After experiments,this study can improve the monitoring and trace the origin efficiency by more than 97％,and effec-tively excavate 5G cloud poor quality services.
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