Storage system self-adaptive parameter tuning method based on deep learning

Abstract

The invention discloses a storage system self-adaptive parameter tuning method based on deep learning. The method comprises the steps of I / O bandwidth management of the storage system, I / O load feature recognition and system parameter self-adaptive adjustment and optimization, wherein the I / O bandwidth management is that the bandwidth is allocated through a reinforced fair queuing scheduling algorithm, and an application program can obtain reasonable bandwidth resources. The I / O load feature identification is to perform performance modeling on the storage system by means of a multiple linear regression theory, and dynamically extract a performance model of the storage system by detecting load features. According to the system parameter self-adaptive adjustment and optimization, parametershaving large influence on the performance of the storage system are selected through a Latin hypercube sampling method and a greedy parameter selection algorithm, then an optimization model is trainedthrough a deep neural network, and parameter configuration with the optimal system performance is obtained. The I / O bandwidth can be reasonably managed, load characteristics are detected and identified, important parameters are automatically selected, and the performance of the storage system is optimized.

Description

technical field [0001] The invention relates to a method for optimizing the performance of a storage system, in particular to a computer storage system with large-scale load deployment, such as a method for optimizing load characteristics and parameter adaptation in a large data storage system. Background technique [0002] Storage systems are key components of modern computer systems and have a significant impact on application performance and efficiency. Most storage systems have many configurable parameters to control and affect application performance. However, with the rapid development of computer applications and network technology and the continuous improvement of the system structure, the scale of the storage system is becoming very large and the distribution is becoming wider and wider. The software and hardware implementation of the storage system has also become more complex, which contains hundreds or thousands of configurable parameters. Highly complex and hig...

AI Technical Summary

Problems solved by technology

Many optimization techniques are difficult to effectively deal with discrete and categorical parameters, which ultimately makes performance optimization results very unsatisfactory

[0006] The current parameter configuration and tuning methods cannot distinguish the load characteristics of specific applications, and cannot be used in different operating environments. The generality is poor, making it impossible to ensure the service quality of applications in the storage system.

Images