Network equipment fault prediction method, device and equipment and readable medium

Abstract

The invention discloses a network equipment fault prediction method, which comprises the following steps of: obtaining a system alarm log, and extracting a parameter characteristic value from the alarm log; marking the time slice as a positive sample and a negative sample according to whether a fault occurs based on the parameter characteristic value; sending the positive sample and the negative sample into an online random forest, and respectively training the positive sample and the negative sample based on the positive sample hyper-parameter and the negative sample hyper-parameter to obtaina prediction model; and acquiring a real-time log, extracting a parameter characteristic value from the real-time log, and inputting the parameter characteristic value into the prediction model for prediction. The invention further discloses a corresponding device, computer equipment and a readable storage medium. According to the method, the fault prediction learning model is established by analyzing the state quantity of the network equipment, and the switch fault is predicted by utilizing the fault prediction learning model obtained by training, so that the network reliability of the datacenter is improved, the system operation stability is guaranteed, and the service downtime risk and the operation complexity are reduced.

Description

technical field [0001] The present invention relates to the technical field of network equipment maintenance, and in particular, to a method, apparatus, device and readable medium for fault prediction of network equipment. Background technique [0002] Cloud computing and Internet services have developed rapidly, and the number of network devices has exploded, including a variety of switches including TOR switches and aggregation switches. With the passage of time, the aging problem of network equipment has become increasingly prominent, and the probability of equipment failure will also increase rapidly, especially for equipment that has been used for more than 3 years, the failure rate will increase sharply. Occasional network failures have gradually become the norm. Among the total network equipment failures, switch failures dominate and have the most serious impact, causing catastrophic effects such as service downtime and data loss, which greatly increases the complexit...

AI Technical Summary

Problems solved by technology

However, the above methods cannot cover all switches, and sometimes O&M personnel need to quickly diagnose and locate

These methods either face deployment issues, or require a lot of time to locate and resolve switch failures

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