Storage node load balancing method and device, equipment and storage medium

Through real-time monitoring of storage nodes and load trend prediction, dynamic adjustment of data access policies and replica distribution, the problem of node load imbalance in distributed storage systems is solved, and efficient load balancing and stability improvement is achieved.

CN120390014AInactive Publication Date: 2025-07-29SHANDONG LANGCHAO YUNTOU INFORMATION TECH CO LTD
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Patent Information

Application Number
CN202510873823.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-27
Publication Date
2025-07-29
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing distributed storage system cannot sense node load changes in real time in high-throughput scenarios, resulting in some nodes being overloaded while other nodes being low in loads, unbalanced resource utilization, and the existing load balancing mechanism is untimely and costly, which cannot meet the high-real-time business needs.

Method used

By monitoring the storage nodes in real time, collecting load information, and predicting load trends based on historical data, dynamically adjusting data access policies and data replica distribution to optimize load balancing of storage nodes.

Benefits of technology

It realizes accurate mastery and dynamic optimization of the load changes of storage nodes, improves load balancing capabilities, meets diversified business needs, and improves the system's high throughput processing capabilities and stability.

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Abstract

The invention discloses a storage node load balancing method and device, equipment and a storage medium, and relates to the technical field of load balancing, and the method comprises the steps: carrying out the real-time monitoring of the state of each storage node, and collecting the real-time load information; predicting the load trend of each storage node based on the real-time load information and the historical load data of each storage node to obtain a corresponding prediction result; and adjusting each storage node and a corresponding data access strategy according to the real-time load information and the prediction result so as to respond to the related data request based on the data copy in the adjusted storage node. Therefore, the storage nodes and the data access strategy can be adjusted in combination with the real-time load information of each storage node and the prediction result of the corresponding load trend; the load change trend of each storage node can be accurately mastered, the storage nodes and access strategies are dynamically optimized, the load balancing capacity of the storage nodes can be improved, and diversified service requirements are met.
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