Checking alarm method and device based on quartile algorithm and calculation equipment

Abstract

The embodiment of the invention relates to the technical field of service monitoring, and discloses a transaction alarm method and device based on a quartile algorithm and calculation equipment. The method comprises the following steps: dynamically obtaining a first upper limit baseline value and a first lower limit baseline value of a monitoring index of a previous time slice according to historical monitoring data of the monitoring index by applying the quartile algorithm; respectively obtaining a second upper limit baseline value and a second lower limit baseline value of the current time slice through dynamic curve fitting according to the first upper limit baseline value and the first lower limit baseline value; comparing the real-time monitoring data of the monitoring index with the second upper limit baseline value and the second lower limit baseline value to obtain the abnormal state of the real-time monitoring data; and performing graded alarm according to the abnormal state of the real-time monitoring data. Through the above mode, according to the embodiment of the invention, the baseline alarm of dynamic curve value fitting analysis is added, manual setting of a fixed threshold value and definition of the baseline deviation degree are not needed, and the accuracy and timeliness of the alarm are ensured.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of business monitoring, and in particular to a method, device and computing device for abnormality alarm based on the quartile algorithm. Background technique [0002] With the advent of the Internet era, more and more business systems are carried in the data center. All kinds of key monitoring index data are alarmed based on fixed thresholds, and the abnormal fluctuation of index data can better reflect potential risks and hidden dangers. Taking the traffic index of the business system as an example, when the business network environment is abnormally affected, the access traffic from external users will not be able to reach the business system in the data center, and the traffic will drop sharply. When there is a problem with the components of the system itself and cannot respond to the user's request, the traffic will also suddenly drop abnormally at this time. In addition, when...

AI Technical Summary

Problems solved by technology

Traffic analysis equipment in traditional operation and maintenance has a certain traffic alarm function. The alarm baseline is a fixed threshold and the alarm dimension is single. It cannot identify abnormal business or network behaviors. The alarm results cannot meet the needs of business system maintenance personnel for refined operation and maintenance.

Alarm thresholds are often manually set according to different business characteristics. Customer business experience and maintenance personnel experience play a key role in threshold setting. The traffic characteristics of monitored objects are different. Threshold setting is difficult, lacks intelligent automatic adjustment, and monitors many objects and indicators. , the workload of maintenance personnel is heavy

At the same time, there is a lack of dynamic baseline alarm thresholds adjusted based on historical behavior analysis and busy or idle time

If the fixed alarm threshold is set too high, it is only meaningful for traffic peaks, while the traffic in other periods is out of control; if the fixed alarm threshold is set small, it cannot meet the peak status alarm, and the peak traffic will be in the alarm state for a long time, losing the alarm meaning

The current fixed threshold mode is responsible for the alarm identification of all network traffic, resulting in the disadvantage of being unable to provide efficient and accurate alarms. The operation and maintenance risks are very high, and a large number of potential risks make the operation and maintenance work difficult to deal with.

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