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Research on Network Fault Analysis based on Machine Learning

Research on Network Fault Analysis based on Machine Learning. Haibin Song (speaker), (haibin.song@huawei.com) Liang Zhang (zhangliang1@huawei.com). Example Scenario of Big data analysis. Goal ： Combine the offline and online analysis system to support the fast recovery of fault

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Research on Network Fault Analysis based on Machine Learning

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  1. Research on Network Fault Analysis based on Machine Learning Haibin Song (speaker), (haibin.song@huawei.com) Liang Zhang (zhangliang1@huawei.com)

  2. Example Scenario of Big data analysis • Goal：Combine the offline and online analysis systemto support the fast recovery of fault • Online Analysis： Deployed in customer side, detect the fault at real-time, could give out the advice • Offline Analysis： Deployed in TAC/GTAC, provide the service for the global customs, help engineer to locate the fault and give the advice Diagnose Conclusion(Advice) 1Online Analysis Operator NOC TAC/GTAC Advice IP Network Collecting data-set 2 Offline Analysis Send back the data-set Offline system Online system

  3. Offline Scenario for Fault Analysis Data upload Automatic Fault Analysis Visualization KPI Analysis Place A Traffic anomaly • Related analysis • 17:20 shutdown Command Place B 17:20 Fault information Correlation analysis Anomaly analysis items support 1 {BFD BFD_DOWN_TRAP , BFD CRTSESS , BFD DELSESS , BFD STACHG_TODWN , BFD STACHG_TOUP , OSPF NBRCHG , OSPF NBR_CHANGE_E , OSPF NBR_CHG_DOWN , OSPF NBR_DOWN_REASON , OSPF OGNLSA } 0.2523030 Global Fault Diagnosis Center

  4. Visualization——Get the insight to fault Value 1 Filter unnecessary information 2 Statistical analysis of events

  5. Anomaly Detection Value 1 Find the possible fault time 2 Find the possible device 3 Find the possible module Router Traffic anomaly 17:20 Time • Related log files • 17:20 shutdown Cmd

  6. Visualization—Event Summarization • Get the event summarization between different events, and find the relationship between them.

  7. Visualization—— Lag interval • Time lag is a key feature of hidden temporal dependencies within sequential data.

  8. Anomaly Detection——API • Help the operators find the root cause KPI among a list of KPIs, and find the fault time.

  9. Anomaly Detection——Multiple log files Interaction frequency matrix for ISIS protocol messages Output of the Rage Rank algorithm • Find the root cause router based on the interaction of the protocol

  10. Thank you Comments?

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