Network dynamic service monitoring method and device

Abstract

The invention discloses a network dynamic service monitoring method. The method comprises the following steps: extracting already finished network service data in a specific period from a network service data record as historical service data, adjusting and calculating a success rate reference value to obtain an adjusting success rate reference value in accordance with the success rate reference value or a failure rate reference value which is obtained according to the historical service data, and determining whether the adjusting success rate reference value is smaller than or equal to a preset fluctuation ratio alarm threshold; or performing adjustment and calculation on the failure rate reference value to obtain an adjusting failure rate reference value, and determining whether the adjusting failure rate reference value is greater than the preset fluctuation ratio alarm threshold; if so, determining that severe abnormities occur in network services within a monitoring period; and determining the severity of the severe abnormities through severity scores. By using the dynamic service monitoring method, the network service monitoring accuracy and flexibility are improved, and the manual processing workload generated due to false reporting is effectively reduced.

Description

technical field [0001] The application relates to the field of electronic commerce, and specifically relates to a network dynamic service monitoring method and a network dynamic service monitoring device. Background technique [0002] With the wide application of the Internet, electronic commerce (Electronic Commerce) has increasingly become the mainstream business model. Due to the seriousness of business activities, it is necessary to ensure the accuracy and timely transmission of network information, resulting in a large number of network business monitoring requirements. [0003] For e-commerce, the so-called network business monitoring refers to the strict control and real-time monitoring of information transmission and business processes through the network to ensure the smooth progress of various business activities that rely on the network. Because e-commerce includes a large amount of data flowing in the network automatically and without human participation, it is v...

AI Technical Summary

Problems solved by technology

But on the whole, these small number of failed businesses generally do not mean that there is a system risk in the e-commerce system, and taking alarm measures against them will reduce the accuracy of the alarm and affect the normal operation of the business system

In the dynamic business monitoring method provided by the prior art, the alarm is only given according to the relationship between the success rate and failure rate and the corresponding alarm threshold, and the above situation cannot be distinguished

[0009] Secondly, the dynamic business monitoring method described in the above prior art can only judge whether the monitored time period is an abnormal event. If the monitored time period is judged as an abnormal event, the severity of the abnormal event cannot be further judged.

[0010] Third, in the dynamic service monitoring method described in the above prior art, there is a dilemma in the selection of the alarm threshold

If the alarm conditions are too harsh (the alarm threshold of the business success rate is set too high, or the alarm threshold of the business failure rate is set too low), some situations requiring alarms may be missed

On the contrary, if the alarm condition is too low (the alarm threshold of the business success rate is set too low, or the alarm threshold of the business failure rate is set too high), the situation that should not be judged as an abnormal event may issue an alarm, resulting in a large number of Alarm, increasing the workload of alarm

[0011] Finally, in the above-mentioned dynamic business monitoring method provided by the prior art, the same warning method is adopted for all alarms, and targeted alarm measures cannot be selected according to the sensitivity of different businesses to abnormal situations and the severity of abnormal situations.

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