Self-adaptive capacity expansion and contraction method and system for cloud server

CN119473542APending Publication Date: 2025-02-18MARCO POLO TRAVEL TECH CO LTD
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Patent Information

Application Number
CN202411511828.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-10-28
Publication Date
2025-02-18

AI Technical Summary

Technical Problem

In the existing cloud computing technology, the traditional cloud server deployment method has problems such as insufficient management, waste of costs, poor flexibility, and the need for additional human resources for operation and maintenance, making it difficult to effectively deal with emergencies.

Method used

By obtaining the characteristic information of the cloud server, forming vector data and storing it in a time series database, vector operations and rule operations are performed on the vector data according to preset decision rules, and expansion events are generated, and the cloud server is opened and closed to realize adaptive expansion Reduce the volume.

Benefits of technology

It realizes flexible scaling of cloud servers, improves response rate and accuracy, reduces operating costs, and reduces dependence on human resources.

✦ Generated by Eureka AI based on patent content.

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    Figure CN119473542A_ABST
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Abstract

The invention discloses a cloud server adaptive capacity expansion and contraction method and system, and the method comprises the steps: obtaining the feature information of a cloud server, enabling the feature information to form vector data, enabling the vector data to serve as time sequence data, storing the time sequence data in a time sequence database, receiving a query request of the vector data, and enabling the vector data to serve as the time sequence database; performing vector operation on vector data corresponding to the query request according to a preset decision rule to obtain a query result, performing rule operation on the query result to generate a capacity expansion and contraction event, and starting and stopping the cloud server based on a capacity expansion and contraction instruction corresponding to the capacity expansion and contraction event to complete self-adaptive capacity expansion and contraction processing of the cloud server. The time sequence database can store the vector data and respond to the query request, and different decision rules can be adopted according to the feature information of different service systems to carry out capacity expansion and shrinkage processing on the server nodes, so that the cloud server is flexibly started and stopped, the response rate and accuracy of the cloud server are improved, and the user experience is improved. And the operation cost is also reduced to a certain extent.
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