AI-based dynamic storage performance optimization scheme

Abstract

This invention provides an AI-based dynamic storage performance optimization solution, belonging to the field of dynamic storage technology. The steps are as follows: Step 1: Reporting configuration information. The SSD firewall reports its current configuration information, user data behavior, and distribution status to the AI ​​server. Step 2: Configure parameters. The AI ​​server, through its autonomous machine learning mechanism, configures the final firewall parameters and provides them to each individual disk. This invention reduces the computational load of dynamic storage, is suitable for dynamic storage of large amounts of data, and enables the performance, lifespan, and IOPS of the entire data center's storage devices to operate at optimal levels.

Description

Technical Field

[0001] This invention belongs to the field of dynamic storage technology, and in particular relates to an AI-based dynamic storage performance optimization scheme. Background Technology

[0002] Dynamic storage is a storage resource management model that flexibly allocates, expands, and shrinks storage resources based on actual business needs. It breaks the limitations of traditional fixed-capacity allocation and is the mainstream storage form in the era of cloud computing and virtualization. Dynamic storage performance refers to the various technical indicators and service capabilities exhibited by a dynamic storage system in supporting business read / write, data processing, and resource scheduling. It measures whether the on-demand allocated storage resources can meet the performance requirements of the business, while also considering stability and efficiency during dynamic scheduling. Optimizing dynamic storage performance brings value to the business side, cost side, operations and maintenance side, and architecture side.

[0003] Current dynamic storage performance optimization solutions typically involve the SSD firmware reporting FW configuration information to the storage array controller, which then configures the final FW configuration parameters for each individual disk. This dynamic storage performance optimization solution involves a large amount of computation and is gradually becoming unsuitable for the dynamic storage of large amounts of data. Summary of the Invention

[0004] To address the aforementioned technical issues, this invention provides an AI-based dynamic storage performance optimization solution. By configuring the FW address through an AI server, the computational load of dynamic storage is reduced, making it suitable for dynamic storage of large amounts of data.

[0005] AI-based dynamic storage performance optimization solutions include the following steps:

[0006] Step 1: Report configuration information. The SSD FW reports the current FW configuration information, user data behavior, and distribution status to the AI ​​server.

[0007] Step 2: Configure parameters. The AI ​​server uses its autonomous machine learning mechanism to configure the final FW configuration parameters for each individual disk.

[0008] Preferably, in a standalone data center environment, all SSDs can interact with the AI ​​server via built-in software.

[0009] Beneficial effects

[0010] Compared with the prior art, the present invention has the following beneficial effects: it reduces the computational load of dynamic storage, is suitable for dynamic storage of large amounts of data, and enables the storage devices of the entire data center to operate in the optimal state in terms of performance, lifespan, and IOPS. Attached Figure Description

[0011] Figure 1 This is a flowchart of an AI-based dynamic storage performance optimization solution. Detailed Implementation

[0012] The present invention will be further described below with reference to the accompanying drawings:

[0013] In the picture:

[0014] As attached Figure 1 As shown:

[0015] The AI-based dynamic storage performance optimization solution includes the following steps:

[0016] S1: Report configuration information. The SSD FW reports the current FW configuration information, user data behavior, and distribution status to the AI ​​server.

[0017] S2: Configuration parameters. The AI ​​server uses its autonomous machine learning mechanism to configure the final FW configuration parameters for each individual disk.

[0018] In this implementation plan, specifically, in an independent data center environment, all SSDs can interact with the AI ​​server through built-in software, enabling the storage devices of the entire data center to operate at their optimal performance, lifespan, and IOPS.

[0019] Any technical solution that achieves the above-mentioned technical effects by utilizing the technical solutions described in this invention, or by designing similar technical solutions by those skilled in the art under the inspiration of the technical solutions described in this invention, falls within the protection scope of this invention.

Claims

1. An AI-based dynamic storage performance optimization scheme, characterized in that, The method comprises the following steps: Step 1: reporting configuration information, the SSD FW reports the current configuration information, the data behavior of the user, and the responsible distribution to the AI server; Step 2: configuring parameters, the AI server configures the final FW configuration parameters to each independent disk through a self-learning mechanism.

2. The AI-based dynamic storage performance optimization scheme of claim 1, wherein, In the independent data center environment, all SSDs can interact with the AI server through the built-in software.

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