Ceph system performance optimization strategy and system based on deep reinforcement learning

Abstract

The invention discloses a Ceph system performance optimization system based on deep reinforcement learning. The Ceph system performance optimization system is composed of a data source module, a dataaccess mode learning module, an evaluation mechanism learning module and a system parameter adjustment learning module. The Ceph system performance optimization strategy based on deep reinforcement learning is realized through the following steps: S1, preprocessing a data source; s2, learning and classifying a Ceph file system running environment model; s3, carrying out evaluation mechanism learning; and S4, learning a Ceph file system parameter adjustment strategy. According to the data access method, deep reinforcement learning algorithm and interactive learning of an A2C model and a Ceph file system are combined to obtain the optimized parameters, and optimal system parameters adapted to data access mode may be selected; the method can adapt to different data access modes and hardware configurations, the optimal system parameters are obtained through intelligent learning, the system parameters can be obtained according to the optimal system parameters, and therefore the performanceof the Ceph file system is improved.

Description

technical field [0001] The invention relates to the technical field of distributed file systems, in particular to a Ceph system performance optimization strategy and system based on deep reinforcement learning. Background technique [0002] Ceph is a Linux PB-level distributed file system, which is widely used in various distributed storage fields. Therefore, when a user accesses a file, the Ceph file system will read and write the metadata corresponding to the file, and then obtain related data. Currently When using the Ceph file system, affected by the default configuration parameters, there are often execution conflicts between files, which will cause the Ceph file system to stop working or slow down the response. [0003] Secondly, the system performance of Ceph files is also affected by the hardware configuration of the server. When the hardware configuration is low, affected by processors, servers and other devices, the performance of Ceph files will also be reduced ac...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the deficiencies of the prior art, the present invention provides a Ceph system performance tuning strategy and system based on deep reinforcement learning, which solves the problem that when using the Ceph file system, under the influence of the default configuration parameters, there are often execution conflicts between files , which will cause the Ceph file system to stop working or slow down the response speed, reduce the system performance of the Ceph file, cause the Ceph file to be unusable or appear stuck, thereby increasing the load and speed of the system during operation