Research And Application Of Algorithm For Alarm Convergence In Operation And Maintenance Monitoring System

With the rapid development of information technology,the operation and maintenance monitoring system will produce a large number of alarm information when the network,service or equipment has an exception,which not only causes great pressure to the gateway,but also greatly increases burden to the site reliability engineer.In the operation and maintenance monitoring system,the alarm convergence is defined as analyzing,merging and discarding the alarm information,resulting in reducing the scale of the alarm information,which is significant to reduce the pressure of network operation and maintenance.This paper divides the alarm convergence into three sub-tasks,namely,large-scale alarm detection,merging timing-related alarm information and merging granularity-related alarm information.This paper proposes an alarm trend prediction algorithm in order to solve the problem of large-scale alarm detection.Based on the distribution of historical alarm data,a statistical model is established to calculate the current alarm threshold by maximum likelihood estimation.If the amount of actual alarm is bigger than this threshold,we will predict that a large-scale alarm occurr,the method adaptively compensates and optimizes the model coefficients,and also adds the quantile method to reduce the disturbance of normal alarm noise.Timing-related data mining algorithm based on Apriori is adopted to merging timing-related alarm information,and proposed a new confidence formula which being adaptive to the new maintenance scene.It overcomes the shortcomings which large confidence error will happen when there is a long time alarm item.It is shown that this method is superior to Apriori algorithm with traditional confidence formula.A timingrelated data mining algorithm on association rule based on Apriori is designed to merge granularity-related alarm information.Each alarm information is bound to the policy name information,such as the unit,host,namespace and so on.In this paper,according to the different granularity of alarm information,calculates the similarity between the strategy names,and merges the alarm information with same or similar similarity,and further reduces the size of the alarm information.Refered to the above algorithm framework,this paper designs and realizes the data mining device of alarm convergence based on data preprocessing,model training,model testing and result analysis.At the same time,this paper takes into account the needs of engineers to visualize the analysis of alarm information,finally designes and implementes the alarm convergence data visualization system,which greatly improves the efficiency of information analysis and processing.This paper combines the alarm trend prediction algorithm,the optimized timing-related data mining algorithm on association rule based on Apriori and policy-associated data mining algorithm,and completes the alarm convergence task in stages,and realizes the alarm convergence data mining device and the visualization system.The academic research and industrial application in the field of alarm convergence are of some inspiration.