A data processing method, device and storage medium

Abstract

The embodiment of the present application discloses a data processing method, device, and storage medium, which are applied in the field of cloud computing, reasonably expand or shrink data storage based on scheduling decision-making strategies, and increase or decrease data storage configuration accordingly, reducing Storage costs, and based on the data routing table determined by the scheduling decision, dynamically transfer to different data storages, obtain access resources from different data storages, reduce the storage memory of the data storage device, avoid resource redundancy and waste, and respond in a timely manner The upper-level business needs to access data and provide stable business services. The foregoing data processing method includes: obtaining the target business state; performing data resource scheduling processing on the target business state to obtain a scheduling decision strategy; determining a data routing table based on the scheduling decision strategy; sending the data routing table to the data storage device, so that the data The storage device determines the access resource matching the target service state based on the data routing table.

Description

technical field [0001] The embodiments of the present application relate to the field of computer technologies, and in particular to a data processing method, device, and storage medium. Background technique [0002] As the underlying infrastructure, data storage can be decoupled from upper-level business on the basis of layered architecture, which facilitates horizontal expansion and independent upgrade and maintenance. At present, the traditional approach is to dynamically adjust the configuration required by the upper-layer business through migration, expansion, and shrinkage based on the historical usage of online responses based on the layered architecture, such as request peak value and average time-consuming data. resource. [0003] However, when the current method is used to deal with the personalized business needs of the B-side (also known as: enterprise user merchants), due to the personalized business needs of the B-side business, the response to emergencies, th...

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Problems solved by technology

[0003] However, when the current method is used to deal with the personalized business needs of the B-side (also known as: enterprise user merchants), due to the personalized business needs of the B-side business, the response to emergencies, the randomness of demand changes and the Due to business characteristics such as sharp fluctuations in demand, the processing method based on historical usage can improve resource waste and response delay to a certain extent, but due to the design of the layered architecture, the underlying data storage cannot perceive the business of the upper layer As a result, in order to meet the data access requirements of the upper-level business, there is a large amount of redundancy and waste of resources in the underlying data storage, and it is difficult to provide stable business services

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