Implementation method and system for multi-dimensional management and automated deployment

Abstract

The present invention relates to a method and system for implementing multi-dimensional management and automated deployment. Search for physical servers in a network; based on the search results of physical servers in the network, obtain the hardware information of the physical servers using a preset protocol, and register resources based on the hardware information; based on the results of resource registration, group and manage the physical servers according to at least one dimension of preset hardware performance, geographical location, and network topology to achieve multi-dimensional screening and allocation; using the results of multi-dimensional screening and allocation, use the preset PXE technology to implement automated deployment of operating systems and software on physical servers; using the results of automated deployment of operating systems and software, dynamically allocate and schedule resources based on a preset scheduling algorithm. The solution of the present application helps to achieve rapid allocation, deployment, and management of physical resources, and improve resource utilization and operation and maintenance efficiency.

Description

Technical Field

[0001] The present invention relates to the fields of computer networks and cloud computing, and in particular to a method and system for implementing multi-dimensional management and automated deployment. Background Art

[0002] In today's digital age, cloud computing and virtualization technology have become the core driving force for the transformation of enterprise information technology architecture. Cloud computing has greatly changed the way enterprises operate IT by providing computing resources on demand, flexible and scalable, and convenient service models. As an important support for cloud computing, virtualization technology builds a virtual layer on top of physical hardware, abstracts computing, storage, and network resources into virtualized resources, and realizes efficient sharing and flexible allocation of resources.

[0003] At present, mainstream cloud computing solutions on the market, such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP), widely use virtualization technology. These solutions have shown many advantages in general computing scenarios, such as the elastic allocation of resources through the rapid creation and destruction of virtual machine instances, which enables enterprises to flexibly adjust resource configuration according to actual business needs, effectively reducing hardware procurement costs and operation and maintenance management difficulties. At the same time, the cloud computing platform based on virtualization provides a unified management interface and rich APIs, which greatly improves the ease of use and automation of resource management.

[0004] However, in certain specific fields and high-performance computing scenarios, virtualization technology has exposed obvious performance bottlenecks. For example, in the field of high-performance computing (HPC), scientific research and engineering computing tasks usually have extremely high computing performance requirements and need to fully utilize the full computing power of physical hardware. The additional abstraction layer introduced by virtualization will lead to certain performance overhead, such as reduced CPU instruction execution efficiency, increased memory access latency, and network and storage IO performance loss. Similarly, in big data processing scenarios, the real-time processing and analysis of massive data places extremely high demands on the system's computing, storage, and network performance. The performance loss caused by virtualization technology may cause the task execution time to be significantly extended, which cannot meet the business's demand for timeliness.

[0005] With the acceleration of digital transformation of enterprises, the demand for direct management and efficient use of physical server resources has become increasingly prominent. On the one hand, when dealing with high-performance computing tasks, enterprises urgently need a solution that can directly access physical hardware resources to reduce the performance overhead brought by virtualization and give full play to the ultimate performance of hardware. On the other hand, with the continuous expansion of the scale of data centers, the number of servers has increased dramatically, and traditional manual management methods have been unable to cope with complex operation and maintenance management challenges. Enterprises are in urgent need of an automated physical server resource management system to achieve centralized management of multiple physical servers, batch operating system installation, rapid application deployment, real-time resource monitoring and intelligent scheduling, improve resource delivery speed and operation and maintenance efficiency, and reduce management costs. In addition, in a multi-tenant environment, ensuring resource isolation and secure access control between tenants, as well as achieving high availability and flexible scalability of the system are also important needs currently faced by enterprises.

[0006] In summary, the limitations of existing cloud computing technology in specific scenarios have given rise to innovative demands for physical server resource management and automated deployment technology. The present invention aims to fill this technological gap and provide enterprises with a more efficient, flexible and reliable physical server resource management solution. Summary of the invention

[0007] In view of this, the purpose of the present invention is to provide a method and system for implementing multi-dimensional management and automated deployment to solve related technical problems existing in the prior art.

[0008] According to a first aspect of an embodiment of the present invention, a method for implementing multi-dimensional management and automated deployment is provided, the method comprising:

[0009] Search the network for physical servers;

[0010] Based on the search results of the physical servers in the network, the hardware information of the physical servers is obtained by using a preset protocol, and resource registration is performed based on the hardware information;

[0011] Based on the results of resource registration, physical servers are grouped and managed according to at least one of the preset hardware performance, geographical location, and network topology to achieve multi-dimensional screening and allocation;

[0012] Utilizing the results of the multi-dimensional screening and allocation, and utilizing the preset PXE technology to implement automatic deployment of the operating system and software on the physical server;

[0013] Utilize the results of automated deployment of operating systems and software to dynamically allocate and schedule resources based on preset scheduling algorithms.

[0014] Further, the search results based on the physical server in the network, obtaining the hardware information of the physical server using a preset protocol, and registering resources based on the hardware information, include:

[0015] Based on the search results of the physical servers in the network, the hardware information of the physical servers is obtained by using a predefined discovery mode in a preset PXE technology and IPMI and Redfish standard protocols;

[0016] Using the physical server hardware information, storing it in a preset terminal through a preset RESTful interface, and generating a unique identifier UUID for each physical server;

[0017] The hardware information of the physical server includes: processor model and core number, memory capacity and type, disk capacity and interface type, and network interface information.

[0018] Furthermore, based on the result of resource registration, physical servers are grouped and managed according to at least one dimension of preset hardware performance, geographical location, and network topology to achieve multi-dimensional screening and allocation, including:

[0019] Physical servers are grouped and managed based on at least one of the preset dimensions of hardware performance, geographic location, and network topology, and grouping information is obtained by users tagging resources through APIs or in the form of custom grouping rules to achieve multi-dimensional screening and allocation.

[0020] Furthermore, the use of the results of the multi-dimensional screening and allocation to implement automatic deployment of the operating system and software on the physical server using a preset PXE technology includes:

[0021] Use PXE to boot the target physical server and load the lightweight boot operating system;

[0022] The installation image of the target operating system is obtained from a preset image repository, and the installation image and a preset automated installation tool are used to perform installation and initialization configuration of the target operating system.

[0023] Furthermore, the result of the automatic deployment of the operating system and software is used to dynamically allocate and schedule resources based on a preset scheduling algorithm, including:

[0024] Using a preset load balancing algorithm, monitor the resource utilization of each physical server and give priority to allocating nodes with lower loads;

[0025] A preset affinity scheduling algorithm is used to allocate related tasks or related services to specific nodes according to the association relationship between nodes or business logic requirements among the physical servers.

[0026] According to a second aspect of an embodiment of the present invention, a system for implementing multidimensional management and automated deployment is provided, which is applied to any of the above-mentioned methods for implementing multidimensional management and automated deployment, and the system includes:

[0027] The acquisition module is used to search for physical servers in the network;

[0028] A first processing module, configured to obtain hardware information of the physical server based on the search results of the physical server in the network by using a preset protocol, and perform resource registration based on the hardware information;

[0029] The second processing module is used to group and manage the physical servers based on the result of resource registration according to at least one dimension of preset hardware performance, geographical location, and network topology to achieve multi-dimensional screening and allocation;

[0030] A third processing module is used to use the results of the multi-dimensional screening and allocation to implement automatic deployment of the operating system and software on the physical server using a preset PXE technology;

[0031] The fourth processing module is used to utilize the results of the automatic deployment of the operating system and software to dynamically allocate and schedule resources based on a preset scheduling algorithm.

[0032] Furthermore, the system further comprises:

[0033] A lifecycle management module, used for monitoring the physical server throughout its lifecycle;

[0034] The life cycle management module includes:

[0035] The onboarding unit is used to automatically enter the hardware discovery and registration process when a new physical server is connected to the system;

[0036] A monitoring unit, used to collect performance indicators of the physical server in real time and provide visual charts;

[0037] A fault processing unit, used to automatically detect hardware faults and perform hardware diagnosis through a preset algorithm;

[0038] The offline unit is used to automatically clear the storage data of the server and remove it from the resource pool when the server needs to be retired.

[0039] Furthermore, the system further comprises:

[0040] Security and permission control module, including:

[0041] A user authentication unit, used to authenticate the user to be used based on a preset authentication method; wherein the preset authentication method includes: local user authentication, LDAP integrated authentication and OAuth authentication;

[0042] A permission control unit, which is used to divide users into different roles based on role-based access control and assign corresponding permissions according to the roles;

[0043] The data security unit is used to encrypt and initialize the preset storage medium and encrypt the preset API communication using the TLS encryption protocol.

[0044] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0045] It is understandable that the technical solution provided by the present invention is a method and device for automatically discovering physical servers through standard protocols, and classifying and resource-managing hardware information; a technical solution for batch deployment using image repositories and templated configuration files, and a mechanism for supporting the automatic deployment of the integrated operating system and specific application environment. Based on the preset scheduling algorithm, the application of load balancing and affinity intelligent scheduling algorithms in the allocation of physical server resources, the physical server resources are dynamically adjusted to meet real-time load changes. The solution of this application helps to realize the rapid allocation, deployment and management of physical resources, and improve resource utilization and operation and maintenance efficiency.

[0046] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0048] Figure 1 It is a flowchart of a method for implementing multi-dimensional management and automatic deployment according to an exemplary embodiment;

[0049] Figure 2 The diagram is a schematic diagram of a system composition for implementing multi-dimensional management and automated deployment according to an exemplary embodiment. DETAILED DESCRIPTION

[0050] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0051] Embodiment 1

[0052] See also Figure 1 , Figure 1 The present invention is a flowchart of a method for implementing multi-dimensional management and automatic deployment according to an exemplary embodiment, wherein the method comprises:

[0053] S1. Search for physical servers in the network;

[0054] S2. Based on the search results of the physical server in the network, the hardware information of the physical server is obtained using a preset protocol, and resource registration is performed based on the hardware information;

[0055] S3. Based on the result of resource registration, physical servers are grouped and managed according to at least one dimension of preset hardware performance, geographical location, and network topology to achieve multi-dimensional screening and allocation;

[0056] S4. Using the results of the multi-dimensional screening and allocation, the preset PXE technology is used to implement automatic deployment of the operating system and software on the physical server;

[0057] S5. Using the results of the automated deployment of the operating system and software, dynamically allocate and schedule resources based on a preset scheduling algorithm.

[0058] In the specific implementation, please refer to step SI-S2, based on the search results of the physical server in the network, the hardware information of the physical server is obtained by using the predefined discovery mode in the preset PXE technology and the IPMI and Redfish standard protocols;

[0059] The physical server hardware information is used to store in a preset end through a preset RESTful interface, and a unique identifier UUID is generated for each physical server for subsequent query and management; wherein the preset end includes: resource management data.

[0060] The hardware information of the physical server includes: processor model and core number, memory capacity and type, disk capacity and interface type, and network interface information.

[0061] In the specific implementation, as described in step S3, based on the result of resource registration, the physical servers are grouped and managed according to at least one dimension of preset hardware performance, geographical location, and network topology to achieve multi-dimensional screening and allocation, including:

[0062] Physical servers are grouped and managed based on at least one of the preset dimensions of hardware performance, geographic location, and network topology, and grouping information is obtained by users tagging resources through APIs or in the form of custom grouping rules to achieve multi-dimensional screening and allocation.

[0063] More specifically, the present invention provides a flexible and multi-dimensional grouping mechanism in physical server resource management. In terms of hardware characteristics, the server will be classified according to characteristics such as whether it is a high-performance computing node or a large-capacity storage node. From the physical location dimension, the servers are grouped according to information such as the rack number and the location of the data center. In a large data center, servers located in the same rack may have similar network delays and physical connection characteristics. Managing them in groups helps optimize local network communications and resource allocation. For example, servers located in the same cabinet are grouped together to facilitate unified power management, heat dissipation management, and network topology planning for the group of servers. For servers distributed in different data center locations, they are grouped according to factors such as the geographical location of the data center where they are located and network access conditions, so as to make reasonable decisions in resource scheduling and business deployment across data centers.

[0064] Business needs are also an important basis for grouping. Servers that undertake database services are grouped into the database server group. These servers focus on data storage, query and management, and have high requirements for data consistency, reliability and read and write performance. The application server group is responsible for running various business applications and providing service interfaces for users. It needs to have good processing capabilities and response speed to meet the needs of a large number of users accessing concurrently. This grouping based on business needs can better meet the performance and management requirements in different business scenarios.

[0065] Users have a high degree of autonomy in the resource management process. Through the API or management platform, users can customize grouping rules according to their own business logic and management needs. For example, an enterprise can define grouping rules for servers based on project names or department names, and divide servers related to a specific project into the same group, making it easier for the project team to manage and monitor its resources independently; or group servers according to the hardware purchase batches so that targeted operations can be performed during hardware maintenance and upgrades.

[0066] When it comes to labeling resources, users can add multiple tags to each server to describe its various properties.

[0067] In one embodiment, as described in step S4, using the result of the multi-dimensional screening and allocation, and using a preset PXE technology to implement automatic deployment of the operating system and software on the physical server, includes:

[0068] Use PXE to boot the target physical server and load the lightweight boot operating system;

[0069] The installation image of the target operating system is obtained from a preset image repository, and the installation image and a preset automated installation tool are used to perform installation and initialization configuration of the target operating system.

[0070] In the specific implementation, in the initial stage of the automatic deployment of the operating system, the PXE (Preboot Execution Environment) technology is used to boot the target server. PXE boot allows the server to obtain the information and files required for booting through the network without installing the operating system on a local storage device (such as a hard disk). When the target server starts, it sends a broadcast request to find a PXE server in the network. After receiving the request, the PXE server sends the boot file to the target server according to the pre-configured boot policy, and boots the server into a predefined discovery mode. In this mode, the server loads a lightweight boot operating system, such as TinyCore Linux. TinyCoreLinux is small in size and starts quickly. It can provide a basic operating environment so that the server has sufficient network functions to obtain a complete operating system installation image from the network later. This lightweight operating system runs in memory and does not rely on local hard disk storage, laying the foundation for the automated deployment process.

[0071] Secondly, after the target server loads the lightweight boot operating system, it connects to the image repository through the network. The image repository stores installation images of various mainstream operating systems, such as Ubuntu, CentOS, Windows Server, etc. The server downloads the corresponding installation image from the image repository according to the pre-configured deployment task or the operating system version specified by the user. This process involves network transmission protocols such as HTTP, FTP, or TFTP to ensure that the installation image can be accurately and quickly transmitted to the target server. The image repository can be a server in the local network or a storage service located in the cloud. It is designed to provide centralized operating system image management, facilitate rapid acquisition of required operating system resources in large-scale server deployment scenarios, and ensure the consistency and repeatability of deployment.

[0072] Finally, after the target operating system installation image is downloaded to the server, the automated installation tool comes into play. For example, tools such as Kickstart (mainly used for Red Hat series operating systems) and Cloud-Init (applicable to a variety of Linux distributions and cloud environments) can read pre-defined configuration files, which contain various parameters in the operating system installation process, such as disk partitioning scheme, user account creation, software package selection, etc. The automated installation tool automatically executes the operating system installation process according to the instructions of the configuration file, including formatting the disk, copying system files, installing drivers, etc. After the installation is completed, it will also perform initialization configuration according to the configuration, such as setting network parameters (IP address, subnet mask, gateway, etc.), configuring firewall rules, installing common software packages, etc. This series of automated operations makes the operating system installation process without manual intervention, greatly improving the deployment efficiency, while also ensuring the consistency of multiple server deployments and reducing the possibility of human errors. It is suitable for scenarios such as large-scale data centers and cloud computing environments that require rapid deployment of a large number of servers.

[0073] In the specific implementation, a variety of predefined templates are also provided, including common operating systems, database environments (such as MySQL, PostgreSQL), and application frameworks (such as Django, Node.js). Users can customize templates to achieve consistency in batch deployment.

[0074] In one embodiment, as described in step S5, the use of the result of the automatic deployment of the operating system and software to dynamically allocate and schedule resources based on a preset scheduling algorithm includes:

[0075] Using a preset load balancing algorithm, monitor the resource utilization of each physical server and give priority to allocating nodes with lower loads;

[0076] A preset affinity scheduling algorithm is used to allocate related tasks or related services to specific nodes according to the association relationship between nodes or business logic requirements among the physical servers.

[0077] In specific implementation, the system uses a preset load balancing algorithm to achieve efficient allocation of physical server resources. By continuously monitoring the resource utilization of each physical server, including key indicators such as CPU usage, memory occupancy, and I / O operation frequency, the load status of the server can be mastered in real time. For example, at regular intervals (such as a few seconds or minutes), the system collects resource usage data of each server. Based on this data, the load balancing algorithm calculates the load weight or load level of each server to determine its load level. When a new task or service request arrives, the system will give priority to assigning it to the node with lower load. This allocation strategy is based on a variety of load balancing algorithms, such as the Round-Robin algorithm, which assigns tasks to each server in order to ensure that each server can get a balanced task allocation; the Weighted Round-Robin algorithm assigns different weights to servers according to the performance differences of the servers (such as the number of CPU cores, memory size, etc.), and the server with high performance will take on more tasks; the Least-Connections algorithm assigns new tasks to the server with the least current connections to balance the workload of each server. Through the comprehensive application of these algorithms, the system can dynamically adapt to changes in server load, effectively avoid performance degradation caused by overload of individual servers, and improve the resource utilization and response speed of the entire system. It is especially suitable for scenarios that handle a large number of concurrent tasks, such as high-traffic Web application server clusters, large-scale data processing centers, etc.

[0078] Furthermore, the system uses a preset affinity scheduling algorithm to intelligently assign related tasks or services to specific nodes based on the association between nodes or business logic requirements. In terms of node association, the hardware affinity between servers is considered. For example, for tasks that require frequent data exchange, if the servers are connected through a high-speed network (such as InfiniBand) or share specific hardware resources (such as high-performance storage devices), these tasks are assigned to server nodes with affinity to reduce data transmission delays and improve the overall performance of the system. From the perspective of business logic requirements, scheduling is performed according to different business application scenarios. For example, for tasks that need to access a specific data storage area or rely on a specific software environment, they are assigned to matching server nodes. The affinity scheduling algorithm can also consider the association between tasks, such as assigning tasks belonging to the same business process or with data dependencies to similar nodes to reduce communication overhead and coordination costs between tasks. This scheduling strategy not only improves system performance, but also enhances the stability and reliability of business applications. It is suitable for scenarios such as enterprise-level application systems, distributed computing environments, and service deployment in cloud computing platforms that require high performance and data consistency.

[0079] In one embodiment, by continuously monitoring the resource utilization (such as CPU, memory, and IO) of each physical server through a load-balancing scheduling algorithm, tasks can be accurately assigned to nodes with lower loads, thus avoiding idleness and waste of resources. In actual application scenarios, for example, in a data center that processes multiple computing tasks simultaneously, the system can intelligently assign new tasks to relatively idle servers based on the real-time load of the servers, so that the resources of each server can be fully utilized, thereby improving overall resource utilization; the affinity-based scheduling strategy ensures that the assigned nodes meet the business logic requirements, reduces data transmission time, improves system response speed, further optimizes resource allocation, and improves resource utilization efficiency.

[0080] In one embodiment, it also includes lifecycle management, automatically entering the hardware discovery and registration process when a new physical server is connected to the system; and real-time collection of performance indicators of the physical server (such as CPU temperature, fan speed, power consumption) and providing visual charts.

[0081] Furthermore, it also provides fault handling functions by automatically detecting hardware faults and performing hardware diagnosis through preset algorithms; specifically, it automatically detects hardware faults (such as disk damage, memory errors); and supports remote restart and hardware diagnosis (via IPMI).

[0082] Furthermore, offline-related services are also provided. When a server needs to be retired, its storage data is automatically cleared and removed from the resource pool.

[0083] In the specific implementation, the full life cycle of physical servers from online, monitoring, troubleshooting to offline is automated. When the server is online, hardware discovery and registration are automatically completed, and server performance indicators (such as CPU temperature, fan speed, power consumption) are monitored in real time. When hardware failures (such as disk damage, memory errors) occur, they are automatically detected and supported for remote restart and hardware diagnosis. When offline, storage data is automatically cleared and removed from the resource pool. This series of automated operations greatly reduces manual intervention, reduces the workload of operation and maintenance personnel, improves operation and maintenance efficiency, and also reduces the errors that may be caused by human operations.

[0084] Furthermore, a security and permission control module is provided, including:

[0085] Multiple user authentication methods, including local user authentication, the system builds its own user account and password database, and compares and verifies with local data when the user logs in. It is suitable for independent scenarios with a small number of users; LDAP integrated authentication, OAuth authentication.

[0086] RBAC is used to divide users into roles such as administrators and ordinary users. Administrators have comprehensive system management permissions, such as user management, system configuration, data maintenance, etc.; ordinary users only have basic business operation permissions, such as data viewing and partial information modification. Permission allocation is based on the principle of least privilege, ensuring that users can only operate within the scope of authorization, ensuring system security and data integrity, and preventing illegal operations and data leakage. It is suitable for all types of systems that require strict permission management.

[0087] In addition, when deploying the operating system, the storage medium is encrypted and initialized, and all API communications are encrypted using TLS to ensure data transmission security.

[0088] Furthermore, each functional module of the system runs independently, such as hardware discovery, deployment, monitoring and other modules do not interfere with each other. This allows each module to be developed, tested, and updated separately without affecting each other. For example, improving the support of the hardware discovery module for new hardware does not affect other modules, which facilitates function expansion and optimization, and can flexibly respond to changes in demand during enterprise development, which is suitable for scenarios where business is constantly expanding and technology is continuously updated.

[0089] In addition, it provides a rich set of RESTful interfaces to facilitate integration with other systems, such as IT service management tools, monitoring tools, etc. Enterprises can use this to build a unified management platform to achieve data sharing and collaborative work. Taking the integration with monitoring tools as an example, system monitoring data can be transmitted in real time, which facilitates centralized monitoring and management, improves management efficiency and scientific decision-making, and is suitable for multi-system collaboration needs in complex enterprise environments.

[0090] Furthermore, users can develop custom plug-ins to expand the functions of specific scenarios. For example, enterprise-specific business logic or special hardware adaptation can be realized through plug-ins. The plug-in mechanism enriches system functions and meets diverse personalized needs without affecting the stability of the system's core architecture, enhancing system adaptability and competitiveness. It is suitable for enterprises or innovative projects with unique business needs.

[0091] The technical solution of this application supports automated operating system installation and software deployment on target servers with the help of predefined templates and configuration files, including mainstream Linux distributions, Windows Server, etc. It also provides a variety of predefined templates (such as common operating systems, database environments, and application frameworks), and users can also customize templates to achieve batch deployment consistency. This rapid deployment capability is particularly important in large-scale server deployment scenarios. For example, when an enterprise needs to quickly launch a large number of servers to expand its new business, it can significantly shorten the deployment time, increase the speed of resource delivery, and enable the enterprise to respond to market demand more quickly.

[0092] See also Figure 2 , Figure 2 The present invention is a schematic diagram of a system for implementing multi-dimensional management and automated deployment according to an exemplary embodiment, wherein the system includes:

[0093] An acquisition module 20, used for searching a physical server in the network;

[0094] A first processing module 21 is used to obtain hardware information of the physical server based on the search results of the physical server in the network by using a preset protocol, and perform resource registration based on the hardware information;

[0095] The second processing module 22 is used to group and manage the physical servers based on the result of resource registration according to at least one dimension of preset hardware performance, geographical location, and network topology to achieve multi-dimensional screening and allocation;

[0096] The third processing module 23 is used to use the results of the multi-dimensional screening and allocation to implement automatic deployment of the operating system and software on the physical server using a preset PXE technology;

[0097] The fourth processing module 24 is used to utilize the results of the automatic deployment of the operating system and software to dynamically allocate and schedule resources based on a preset scheduling algorithm.

[0098] The system further comprises:

[0099] A lifecycle management module, used for monitoring the physical server throughout its lifecycle;

[0100] The life cycle management module includes:

[0101] The onboarding unit is used to automatically enter the hardware discovery and registration process when a new physical server is connected to the system;

[0102] A monitoring unit, used to collect performance indicators of the physical server in real time and provide visual charts;

[0103] A fault processing unit, used to automatically detect hardware faults and perform hardware diagnosis through a preset algorithm;

[0104] The offline unit is used to automatically clear the storage data of the server and remove it from the resource pool when the server needs to be retired.

[0105] The system further comprises:

[0106] Security and permission control module, including:

[0107] A user authentication unit, used to authenticate the user to be used based on a preset authentication method; wherein the preset authentication method includes: local user authentication, LDAP integrated authentication and OAuth authentication;

[0108] A permission control unit, which is used to divide users into different roles based on role-based access control and assign corresponding permissions according to the roles;

[0109] The data security unit is used to encrypt and initialize the preset storage medium and encrypt the preset API communication using the TLS encryption protocol.

[0110] It is understandable that the technical solution provided by the present invention is a method and device for automatically discovering physical servers through standard protocols, and classifying and resource-managing hardware information; a technical solution for batch deployment using image repositories and templated configuration files, and a mechanism for supporting the automatic deployment of the integrated operating system and specific application environment. Based on the preset scheduling algorithm, the application of load balancing and affinity intelligent scheduling algorithms in the allocation of physical server resources, the physical server resources are dynamically adjusted to meet real-time load changes. The solution of this application helps to realize the rapid allocation, deployment and management of physical resources, and improve resource utilization and operation and maintenance efficiency.

[0111] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0112] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0113] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0114] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0115] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0116] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0117] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0118] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0119] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for implementing multi-dimensional management and automated deployment, characterized in that: The method comprises: Search the network for physical servers; Based on the search results of the physical servers in the network, the hardware information of the physical servers is obtained by using a preset protocol, and resource registration is performed based on the hardware information; Based on the results of resource registration, physical servers are grouped and managed according to at least one of the preset hardware performance, geographical location, and network topology to achieve multi-dimensional screening and allocation; Utilizing the results of the multi-dimensional screening and allocation, and utilizing the preset PXE technology to implement automatic deployment of the operating system and software on the physical server; Utilize the results of automated deployment of operating systems and software to dynamically allocate and schedule resources based on preset scheduling algorithms; The method of obtaining hardware information of the physical server based on the search result of the physical server in the network by using a preset protocol and registering resources based on the hardware information includes: Based on the search results of the physical servers in the network, the hardware information of the physical servers is obtained by using a predefined discovery mode in a preset PXE technology and IPMI and Redfish standard protocols; Using the physical server hardware information, storing it in a preset terminal through a preset RESTful interface, and generating a unique identifier UUID for each physical server; The hardware information of the physical server includes: processor model and core number, memory capacity and type, disk capacity and interface type, and network interface information.

2. The method according to claim 1, characterized in that Based on the result of resource registration, physical servers are grouped and managed according to at least one dimension of preset hardware performance, geographical location, and network topology to achieve multi-dimensional screening and allocation, including: Physical servers are grouped and managed based on at least one of the preset dimensions of hardware performance, geographic location, and network topology, and grouping information is obtained by users tagging resources through APIs or in the form of custom grouping rules to achieve multi-dimensional screening and allocation.

3. The method according to claim 1, characterized in that: The method of utilizing the results of the multi-dimensional screening and allocation and utilizing a preset PXE technology to implement automatic deployment of an operating system and software on the physical server includes: Use PXE to boot the target physical server and load the lightweight boot operating system; The installation image of the target operating system in the preset image repository is obtained, and the installation image and the preset automated installation tool are used to perform installation and initialization configuration of the target operating system.

4. The method according to claim 1, characterized in that: The result of the automatic deployment of the operating system and software is used to dynamically allocate and schedule resources based on a preset scheduling algorithm, including: Using a preset load balancing algorithm, monitor the resource utilization of each physical server and give priority to allocating nodes with lower loads; A preset affinity scheduling algorithm is used to allocate related tasks or related services to specific nodes according to the association relationship between nodes or business logic requirements among the physical servers.

5. A system for implementing multi-dimensional management and automated deployment, applied to the method for implementing multi-dimensional management and automated deployment as claimed in any one of claims 1 to 4, characterized in that: The system comprises: The acquisition module is used to search for physical servers in the network; A first processing module, configured to obtain hardware information of the physical server based on the search results of the physical server in the network by using a preset protocol, and perform resource registration based on the hardware information; The method of obtaining hardware information of the physical server based on the search result of the physical server in the network by using a preset protocol and registering resources based on the hardware information includes: Based on the search results of the physical servers in the network, the hardware information of the physical servers is obtained by using a predefined discovery mode in a preset PXE technology and IPMI and Redfish standard protocols; Using the physical server hardware information, storing it in a preset terminal through a preset RESTful interface, and generating a unique identifier UUID for each physical server; The hardware information of the physical server includes: processor model and number of cores, memory capacity and type, disk capacity and interface type, and network interface information; The second processing module is used to group and manage the physical servers based on the result of resource registration according to at least one dimension of preset hardware performance, geographical location, and network topology to achieve multi-dimensional screening and allocation; A third processing module is used to use the results of the multi-dimensional screening and allocation to implement automatic deployment of the operating system and software on the physical server using a preset PXE technology; The fourth processing module is used to utilize the results of the automatic deployment of the operating system and software to dynamically allocate and schedule resources based on a preset scheduling algorithm.

6. The system according to claim 5, characterized in that The system further comprises: A lifecycle management module, used for monitoring the physical server throughout its lifecycle; The life cycle management module includes: The onboarding unit is used to automatically enter the hardware discovery and registration process when a new physical server is connected to the system; A monitoring unit, used to collect performance indicators of the physical server in real time and provide visual charts; A fault processing unit, used to automatically detect hardware faults and perform hardware diagnosis through a preset algorithm; The offline unit is used to automatically clear the storage data of the server and remove it from the resource pool when the server needs to be retired.

7. The system according to claim 5, characterized in that The system further comprises: Security and permission control module, including: A user authentication unit, used to authenticate the user to be used based on a preset authentication method; wherein the preset authentication method includes: local user authentication, LDAP integrated authentication and OAuth authentication; A permission control unit, which is used to divide users into different roles based on role-based access control and assign corresponding permissions according to the roles; The data security unit is used to encrypt and initialize the preset storage medium and encrypt the preset API communication using the TLS encryption protocol.

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