Multi-Pool storage resource adaptive writing method and device, equipment and medium

By monitoring the operating status of the Pool dynamically adjusting the write weight and constructing a discrete probability distribution table, and using the pseudo-random number generator to select the target Pool, the problem of resource scheduling imbalance in the existing technology is solved, and the writing efficiency and system stability are improved.

CN120406844APending Publication Date: 2025-08-01PING AN TECH (SHENZHEN) CO LTD
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Patent Information

Application Number
CN202510508417.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-22
Publication Date
2025-08-01

AI Technical Summary

Technical Problem

The prior art cannot adjust the write weight in real time according to changes in the operating state of the Pool, resulting in low resource utilization and degradation of write performance. Especially in the medical and healthcare and financial technology business fields, time-lapse write peaks and business fluctuations lead to imbalance in system resource scheduling.

Method used

By monitoring the available storage space, read and write input and output rates, average response time and data access frequency of each Pool, the write weight of the Pool is dynamically adjusted, and converting it into probability values to build a discrete probability distribution table, and using the pseudo-random number generator to select the target Pool for data writing.

Benefits of technology

It realizes that in high-frequency trading and high-concurrent data recording scenarios, write efficiency, system stability and resource utilization are improved, resource allocation is avoided, and system robustness and write fault tolerance are enhanced.

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Abstract

The invention relates to the technical field of data storage, can be applied to business scenes such as medical health and financial science and technology, and discloses a multi-Pool storage resource adaptive writing method which comprises the following steps: monitoring available storage space, read-write input / output rate, average response time and data access frequency of each Pool; dynamically adjusting the current write-in weight of the Pool according to the monitoring result; converting the current write-in weights of all Pool into probability values, and constructing a discrete probability distribution table; selecting a target Pool through a pseudo-random number generator according to the probability distribution table; and writing the to-be-stored data into the target Pool. According to the method, the running state of each Pool is continuously monitored, and the writing weight is adjusted in real time, so that the problem of resource allocation rigidity under a fixed weight strategy is avoided; in combination with a discrete probability distribution table constructed based on normalized weight and a pseudo-random selection mechanism, selection of the target Pool has certain load balancing elasticity, and data can be more effectively guided to be written into the Pool with light load and excellent performance.
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