Database dynamic resource tuning system and method based on deep neural network

Abstract

The invention discloses a database dynamic resource adjusting and optimizing system and method based on a deep neural network, and the system comprises an acquisition service device and an adjusting and optimizing service device, the tuning service device comprises a rule module, an authority module, an analysis module, a neural network module, a decision module, an auditing module, a task module and a message module. The method comprises the steps of 1) deep neural network model training and 2) database dynamic resource tuning. According to the database dynamic resource tuning system and method based on the deep neural network, the human intervention degree of database resource tuning is reduced, accurate control over database resources can be achieved, the tuning response efficiency is greatly improved, and the labor cost is saved.

Description

technical field [0001] The invention relates to the technical field of databases, in particular to a database dynamic resource tuning system and method based on a deep neural network. Background technique [0002] Data is an important resource and property of an enterprise. Data is stored in the database, and database resources have always been regarded as scarce resources, so it is particularly important to manage the database well. Database vendors provide database dynamic manageability functions, such as DB2 configurable database system memory, various parameters, etc. Oracle database system also provides memory management, instance parameter configuration, etc. [0003] By optimizing database resources, the probability of failure can be reduced and losses can be reduced. Database resource tuning is relatively complex, and it has always been completed under manual intervention. It is highly dependent on database administrators. The quality of tuning is closely related t...

AI Technical Summary

Problems solved by technology

The optimization of database resources is relatively complicated, and it has been almost completed under manual intervention. It is highly dependent on the database administrator. The quality of tuning is closely related to the experience of the database administrator. In a complex maintenance and tuning environment, it is difficult to do To real-time response and precise control of resources, maintenance and tuning costs are high

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