Self-adaption evaluation method for data center network equipment health degree

Abstract

The invention discloses a self-adaption evaluation method for the data center network equipment health degree. The method includes the steps that first, a health degree model of equipment is built, various property parameters of the equipment and the weighed sum of corresponding weight values of the property parameters serve as health degree values, and the health degree values are converted into health indexes; second, network topology description information is acquired, the state information of all network interfaces of the equipment is acquired according to the position of the equipment in a network, and various property parameters of the message receiving condition and the offline condition of the equipment are calculated; third, parameter weight values are set, adjustment is performed according to the occurring probability of the property parameters, health indexes of the equipment are calculated according to the health degree model, and then the parameter weight values are adjusted according to the health indexes obtained through calculation till the health indexes are adjusted to be within a preset range. The self-adaption evaluation method has the advantages that an implementation method is simple, the health degree of the equipment in a large-scale network can be effectively and comprehensively evaluated, and self-adaption dynamic adjustment is performed according to a network structure.

Description

technical field [0001] The invention relates to the technical field of network health evaluation, in particular to an adaptive evaluation method for the health degree of network equipment in a data center. Background technique [0002] Large-scale data centers usually use a large number of core switches, aggregation switches and access switches to form a hierarchical high-speed Ethernet, and are connected to the data center computing server cluster and large-capacity storage system respectively, responsible for providing services between computing servers and computing servers. It provides high-speed information transmission support with the storage system, so the availability and transmission performance of the data center network are important factors affecting the external service capabilities of the data center. In the actual operation and maintenance of the data center network, due to the large scale of equipment and complex link structure in the large-scale network, th...

AI Technical Summary

Problems solved by technology

The advantage of this method is that by dividing complex network problems into simple local modules, it can quickly evaluate the overall status of the data center network equipment; the disadvantage is that it does not consider the hierarchical structure of network equipment deployment, and cannot perform dynamic analysis according to the hierarchical structure of network equipment. adjustment

[0008] 5) By classifying and analyzing various factors in the network such as hardware, service, and transmission one by one, it is possible to judge whether the network is faulty. Health status is graded and judged

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