MinIO distributed storage optimization system and method based on AI cloud desktop

Abstract

The invention discloses a MinIO distributed storage optimization system and method based on an AI cloud desktop, belongs to the technical field of distributed storage, and aims to solve the technical problems of multi-cluster synchronization, enterprise-level security management and heterogeneous resource integration faced by MinIO in the AI desktop. Comprising a unified management platform which provides user authority management through an RBAC model and provides multi-level approval and audit logs through an operation approval process; the non-intrusive monitor is deployed in a source MinIO cluster and used for monitoring a data change event, and the non-intrusive monitor is matched with a synchronization engine in a target cluster to realize multi-cluster data synchronization; the storage abstraction layer is used for managing heterogeneous cloud storage resources, receiving a storage request initiated by a user through the API gateway, and routing the storage request to a corresponding storage service; and the metadata management library provides a data storage service.

Description

Technical Field

[0001] This invention relates to the field of distributed storage technology, specifically to a MinIO distributed storage optimization system and method based on AI cloud desktop. Background Technology

[0002] As a high-performance distributed object storage system, MinIO faces the following technical challenges in AI cloud desktop applications:

[0003] 1. Data synchronization across multiple clusters is inefficient; traditional methods require service interruption or consume significant network bandwidth.

[0004] 2. The lack of enterprise-level security management functions makes it difficult to meet compliance requirements;

[0005] 3. Heterogeneous cloud storage resources are complex to manage and have high operation and maintenance costs;

[0006] 4. Inadequate data security protection mechanisms pose a risk of data leakage.

[0007] Existing solutions typically use independent modules to achieve a single function, lacking system-level integration, which leads to performance degradation and increased operational complexity.

[0008] In AI desktops, MinIO faces technical challenges such as multi-cluster synchronization, enterprise-level security management, and heterogeneous resource integration. Summary of the Invention

[0009] The technical objective of this invention is to address the above-mentioned shortcomings by providing a MinIO distributed storage optimization system and method based on AI cloud desktops, thereby solving the technical problems faced by MinIO in AI desktops, such as multi-cluster synchronization, enterprise-level security management, and heterogeneous resource integration.

[0010] In a first aspect, the present invention provides a MinIO distributed storage optimization system based on AI cloud desktop, comprising a unified management platform, a non-intrusive listener, a synchronization engine, a storage abstraction layer, a metadata management library, and an API gateway.

[0011] The unified management platform provides a visual operation interface for user permission management through the RBAC model and multi-level approval and audit logs through operation approval processes;

[0012] A non-intrusive listener is deployed on the source MinIO cluster to monitor data change events. The non-intrusive listener works with the synchronization engine in the target cluster to achieve multi-cluster data synchronization.

[0013] The storage abstraction layer manages heterogeneous cloud storage resources and connects to the API gateway to receive storage requests initiated by users through the API gateway and route these requests to the corresponding storage services.

[0014] The metadata management repository is used to provide data storage services, and the stored content includes audit logs.

[0015] As a preferred option, when the non-intrusive listener works with the synchronization engine in the target cluster to achieve multi-cluster data synchronization, it supports incremental synchronization and breakpoint resume functionality.

[0016] As a preferred approach, when a non-intrusive listener works with a synchronization engine in the target cluster to achieve multi-cluster data synchronization, the non-intrusive listener is used to capture data change events and synchronize these events to the target cluster via Kafka. The target cluster then applies the data changes and updates the metadata. After the data synchronization is complete, an audit log is generated.

[0017] As a preferred option, the unified management platform is used to receive operation requests submitted by users, verify user permissions according to the RBAC model, and send the operation request to the approval process if the operation request requires approval. After approval, the platform records the audit log.

[0018] As a preferred approach, for storage requests initiated by users through the API gateway, the storage abstraction layer selects a matching storage backend based on performance metrics, routes the storage request to the selected storage service, and returns the result to the user.

[0019] As a preferred method, data is transmitted in encrypted form during data synchronization based on a predefined encryption mechanism.

[0020] As a preferred option, the metadata management library provides a key management service, which manages the keys used for data encryption during data synchronization.

[0021] Secondly, the present invention provides a MinIO distributed storage optimization method based on AI cloud desktop, which optimizes MinIO distributed storage through a MinIO distributed storage system based on AI cloud desktop as described in any of the first aspects.

[0022] The MinIO distributed storage optimization system and method based on AI cloud desktop of the present invention has the following advantages:

[0023] 1. Multi-cluster synchronization efficiency is improved by more than 50%, and network bandwidth usage is reduced by 30%;

[0024] 2. Security management functions meet enterprise-level compliance requirements, and audit log integrity is 100%;

[0025] 3. Improved heterogeneous resource management efficiency by 40% and reduced operation and maintenance costs by 25%;

[0026] 4. The data security protection mechanism is comprehensive, reducing the risk of leakage by 90%. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] The invention will be further described below with reference to the accompanying drawings.

[0029] Figure 1 This is a flowchart of an AI cloud desktop-based MinIO distributed storage optimization method, as shown in Example 2. Detailed Implementation

[0030] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments are not intended to limit the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0031] This invention provides a MinIO distributed storage optimization system and method based on AI cloud desktop, which is used to solve the technical problems of multi-cluster synchronization, enterprise-level security management and heterogeneous resource integration faced by MinIO in AI desktop.

[0032] Example 1:

[0033] This invention discloses a MinIO distributed storage optimization system based on AI cloud desktop, comprising a unified management platform, a non-intrusive listener, a synchronization engine, a storage abstraction layer, a metadata management library, and an API gateway.

[0034] The unified management platform provides a visual user interface for user permission management through the RBAC model and multi-level approval and audit logs through the operation approval process.

[0035] In this embodiment, the unified management platform integrates the RBAC (Role-Based Access Control) model to achieve fine-grained permission management, introduces an operation approval process, supports multi-level approval and audit logs, provides a visual operation interface, and simplifies operation and maintenance management. The platform receives operation requests submitted by users, verifies user permissions according to the RBAC model, and if the operation request requires approval, sends the request to the approval process. After approval, an audit log is recorded.

[0036] A non-intrusive listener is deployed on the source MinIO cluster to monitor data change events. The non-intrusive listener works with the synchronization engine in the target cluster to achieve multi-cluster data synchronization.

[0037] In this embodiment, a non-intrusive listener works in conjunction with a synchronizer to implement a non-intrusive multi-cluster synchronization mechanism. It employs bypass listening technology to capture data change events in real time, designs a synchronization coordination algorithm to ensure data consistency, supports incremental synchronization and breakpoint resumption, and reduces network load. When the non-intrusive listener works with the synchronization engine in the target cluster to achieve multi-cluster data synchronization, it supports incremental synchronization and breakpoint resumption. Specifically, the non-intrusive listener captures data change events and synchronizes these events to the target cluster via Kafka. The target cluster then applies the updated metadata based on the data changes. After data synchronization is complete, an audit log is generated.

[0038] The storage abstraction layer is used to manage heterogeneous cloud storage resources and connects to the API gateway to receive storage requests initiated by users through the API gateway and route the storage requests to the corresponding storage services.

[0039] In this embodiment, the storage abstraction layer uniformly manages heterogeneous cloud storage resources (such as AWS S3, Azure Blob, etc.), provides standardized interfaces, shields the differences in underlying storage, and offers intelligent routing and load balancing. In practical applications, for storage requests initiated by users through the API gateway, the storage abstraction layer selects a matching storage backend based on performance metrics, routes the storage request to the selected storage service, and returns the result to the user.

[0040] The metadata management repository is used to provide data storage services, and the stored content includes audit logs.

[0041] In this embodiment, the keys used for encrypted data transmission are managed based on a key management service. When data is transmitted between the unified management platform, non-intrusive listener, synchronization engine, storage abstraction layer, metadata management library, and API gateway, data encryption is performed based on a predefined encryption mechanism. The metadata management library provides a key management service, which manages the keys used for encrypted data transmission.

[0042] This embodiment of the system enhances security management by integrating RBAC and operation approval processes into a unified storage management platform; it achieves efficient and reliable multi-cluster data synchronization using a non-intrusive bypass monitoring and synchronization coordination mechanism; it utilizes a storage abstraction layer to uniformly manage heterogeneous cloud storage resources; and it supports data encryption and integrity verification. The system includes a unified management portal, a synchronization engine, a storage abstraction layer, a metadata management library, and an API gateway. This invention effectively solves the technical challenges of MinIO in multi-cluster synchronization, enterprise-level security management, and heterogeneous resource integration, significantly improving the reliability, security, and management efficiency of the storage system while reducing operational costs and complexity.

[0043] Example 2:

[0044] This invention discloses a MinIO distributed storage optimization method based on an AI cloud desktop, implementing MinIO distributed storage optimization through the system disclosed in Example 1. For user-submitted requests, they can be routed to the corresponding storage service, thus achieving distributed storage optimization.

[0045] The above provides a detailed description of the MinIO distributed storage optimization system method based on AI cloud desktop provided by this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A MinIO distributed storage optimization system based on AI cloud desktop, characterized in that, This includes a unified management platform, a non-intrusive listener, a synchronization engine, a storage abstraction layer, a metadata management library, and an API gateway; The unified management platform provides a visual operation interface for user permission management through the RBAC model and multi-level approval and audit logs through operation approval processes; A non-intrusive listener is deployed on the source MinIO cluster to monitor data change events. The non-intrusive listener works with the synchronization engine in the target cluster to achieve multi-cluster data synchronization. The storage abstraction layer is used to manage heterogeneous cloud storage resources and connects to the API gateway to receive storage requests initiated by users through the API gateway and route the storage requests to the corresponding storage services. The metadata management repository is used to provide data storage services, and the stored content includes audit logs.

2. The MinIO distributed storage optimization system based on AI cloud desktop according to claim 1, characterized in that, When the non-intrusive listener works with the synchronization engine in the target cluster to achieve multi-cluster data synchronization, it supports incremental synchronization and breakpoint resume functionality.

3. The MinIO distributed storage optimization system based on AI cloud desktop according to claim 1, characterized in that, When a non-intrusive listener works with a synchronization engine in the target cluster to achieve multi-cluster data synchronization, the non-intrusive listener is used to capture data change events and synchronize these events to the target cluster via Kafka. The target cluster then applies the data changes and updates the metadata. After the data synchronization is complete, an audit log is generated.

4. The MinIO distributed storage optimization system based on AI cloud desktop according to claim 1, characterized in that, The unified management platform is used to receive operation requests submitted by users, verify user permissions according to the RBAC model, and send the operation request to the approval process if the operation request requires approval. After approval, the platform records the audit log.

5. The MinIO distributed storage optimization system based on AI cloud desktop according to claim 1, characterized in that, For storage requests initiated by users through the API gateway, the storage abstraction layer selects a matching storage backend based on performance metrics, routes the storage request to the selected storage service, and returns the result to the user.

6. The MinIO distributed storage optimization system based on AI cloud desktop according to claim 1, characterized in that, When data is transmitted between the unified management platform, non-intrusive listener, synchronization engine, storage abstraction layer, metadata management library and API gateway, data is encrypted based on a predefined encryption mechanism.

7. The MinIO distributed storage optimization system based on AI cloud desktop according to claim 6, characterized in that, The metadata management library provides key management services, which manage the keys used for encrypted data transmission.

8. A MinIO distributed storage optimization method based on AI cloud desktop, characterized in that, MinIO distributed storage optimization is achieved through a MinIO distributed storage system based on an AI cloud desktop as described in any one of claims 1-7.

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