Platform operation and maintenance monitoring management method and system based on cluster management

Abstract

The invention provides a platform operation and maintenance monitoring management method and system based on cluster management, and relates to the technical field of data processing, and the method comprises the steps: building a basic operation environment of a Kubernetes cluster; the method comprises the following steps of: deploying a Prometheus monitoring system and a Grafana monitoring system on a Kubernetes cluster; the method comprises the following steps: configuring a Java client library for a Kubernetes cluster; the method comprises the following steps of: writing a java program by using a Java client library, monitoring a Kubernetes cluster by using a Prometheus monitoring system to obtain cluster monitoring data, and outputting a cluster fault detection result through a cluster fault detection model based on a support vector machine; judging whether a fault occurs or not according to a fault detection result, if so, generating alarm information, and entering the next step, otherwise, continuing monitoring; and the alarm information is visually displayed through a Grafana monitoring system.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a platform operation and maintenance monitoring management method and system based on cluster management. Background Art

[0002] With the widespread application of cloud-native technologies, Kubernetes has become the de facto standard for container orchestration. However, traditional operation and maintenance methods face great challenges in the management and monitoring of Kubernetes clusters, especially in complex distributed environments. The efficiency of monitoring and managing each component separately is low, and it is difficult to effectively cope with the needs of cluster scale growth. The Kubernetes-based operation and maintenance monitoring and management platform integrates cluster monitoring, log management, alarm system and other functions, provides a unified monitoring and management interface, helps operation and maintenance personnel to efficiently and comprehensively understand the operation status of the cluster, and improves the response speed of fault handling and performance optimization.

[0003] In the traditional operation and maintenance mode, cluster monitoring and management usually use multiple independent tools to monitor each component. For example, operation and maintenance personnel use Prometheus to collect system performance data (such as CPU, memory, network, disk, etc.), and use Grafana to visualize the monitoring data for analysis. In addition, log management systems (such as ELK: Elasticsearch, Logstash, Kibana) are used to collect, store and analyze containerized application logs.

[0004] However, the existing operation and maintenance monitoring methods can no longer meet the efficient management needs of modern Kubernetes clusters; each monitoring tool and management system runs independently, which requires operation and maintenance personnel to switch between multiple interfaces and systems, and they cannot quickly and comprehensively grasp the overall operation status of the cluster. Summary of the invention

[0005] In order to solve the technical problem that the existing operation and maintenance monitoring methods can no longer meet the efficient management requirements of modern Kubernetes clusters; each monitoring tool and management system runs independently, which requires operation and maintenance personnel to switch between multiple interfaces and systems and cannot quickly and comprehensively grasp the overall operation status of the cluster, the present invention provides a platform operation and maintenance monitoring management method and system based on cluster management.

[0006] The technical solution provided by the embodiment of the present invention is as follows:

[0007] First aspect:

[0008] An embodiment of the present invention provides a platform operation and maintenance monitoring management method based on cluster management, comprising:

[0009] S1: Build the basic operating environment of the Kubernetes cluster;

[0010] S2: deploying a Prometheus monitoring system and a Grafana monitoring system on the Kubernetes cluster, wherein the Prometheus monitoring system is used to collect metric data of each component in the cluster, and the Grafana monitoring system is used to visualize and analyze the monitoring data collected by Prometheus;

[0011] S3: Configure the Java client library for the Kubernetes cluster.

[0012] S4: Use the Java client library to write a Java program;

[0013] S5: Using the Prometheus monitoring system to monitor the Kubernetes cluster through the Java program to obtain cluster monitoring data;

[0014] S6: Construct a cluster fault detection model based on support vector machine;

[0015] S7: training the cluster fault detection model according to the cluster monitoring data by using a gradient descent method;

[0016] S8: Obtain real-time cluster monitoring data;

[0017] S9: takes the real-time cluster monitoring data as input and outputs the cluster fault detection result according to the trained cluster fault detection model;

[0018] S10: judging whether the Kubernetes cluster fails according to the fault detection result, if so, generating an alarm message and proceeding to the next step, otherwise, continuing monitoring;

[0019] S11: Visually display the alarm information through the Grafana monitoring system.

[0020] Second aspect:

[0021] An embodiment of the present invention provides a platform operation and maintenance monitoring and management system based on cluster management, comprising:

[0022] processor;

[0023] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the platform operation and maintenance monitoring management method based on cluster management as described in the first aspect is implemented.

[0024] The third aspect:

[0025] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the platform operation and maintenance monitoring management method based on cluster management as described in the first aspect is implemented.

[0026] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0027] (1) In the present invention, Prometheus and Grafana are integrated into the platform to realize the automatic collection and visual analysis of the metric data in the cluster. By building a fault detection model based on support vector machines, the system can monitor the health of the cluster in real time, automatically detect anomalies and generate alarms, thereby effectively improving the speed of fault discovery and response, and meeting the efficient management requirements of modern Kubernetes clusters.

[0028] (2) In the present invention, Prometheus and Grafana are integrated on the Kubernetes cluster through the platform, and the collection and visualization analysis of monitoring data are concentrated in a unified interface. Operation and maintenance personnel can view all monitoring indicators and analyze alarm information in real time in a single Grafana interface without frequently switching between multiple systems, thereby more comprehensively and quickly grasping the overall operation status of the cluster and the health status of each component, thereby realizing efficient centralized monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 A flow chart of a platform operation and maintenance monitoring management method based on cluster management provided by an embodiment of the present invention;

[0031] Figure 2 A schematic diagram of the structure of a platform operation and maintenance monitoring and management system based on cluster management provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0033] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0034] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0035] In the embodiments of the present invention, sometimes a subscript such as W1 may be expressed in a non-subscript form such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0036] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0037] Reference Manual Attached Figure 1 , showing a flow chart of a platform operation and maintenance monitoring management method based on cluster management provided by an embodiment of the present invention.

[0038] The embodiment of the present invention provides a platform operation and maintenance monitoring management method based on cluster management, which can be implemented by a platform operation and maintenance monitoring management device based on cluster management, and the platform operation and maintenance monitoring management device based on cluster management can be a terminal or a server. The processing flow of the platform operation and maintenance monitoring management method based on cluster management can include the following steps:

[0039] S1: Build the basic operating environment of the Kubernetes cluster.

[0040] Among them, Kubernetes is an open source container orchestration platform for automating the deployment, expansion, and management of containerized applications. It was originally designed and open sourced by Google, and is now maintained and developed by the Cloud Native Computing Foundation (CNCF). Kubernetes clusters achieve application scalability and high availability by managing the life cycle of container operations, making container management in a distributed environment more convenient and efficient.

[0041] In a possible implementation, the basic operating environment specifically includes:

[0042] Make sure the Kubernetes cluster is running.

[0043] Install Java JDK and build tools for the Kubernetes cluster.

[0044] Java JDK (Java Development Kit) is a core toolkit for developing Java applications, released by Oracle. JDK contains the compiler, runtime environment, and some development tools for the Java programming language, and is a necessary tool for building and running Java applications. Java developers can compile, debug, and run Java code through JDK.

[0045] Configure API access permissions for the Kubernetes cluster.

[0046] Among them, API (Application Programming Interface) is a set of definitions and protocols that allow communication and data exchange between different software applications. API provides developers with a standardized way to access the functions of operating systems, libraries or services, allowing developers to easily call specific functions or services without having to understand the underlying implementation details.

[0047] In a possible implementation, the construction tool is specifically: Maven or Gradle.

[0048] Among them, Maven and Gradle are two popular build tools, which are widely used in the management and construction of Java projects. Both can simplify the project construction process and provide functions such as dependency management, project building, testing, packaging and deployment.

[0049] In the present invention, ensuring that the Kubernetes cluster is in operation is a prerequisite for the normal operation of the system. A stable cluster environment can avoid service interruptions and failures, and help improve the availability of the overall application. At the same time, installing Java JDK and build tools (such as Maven or Gradle) provides infrastructure for developing Java applications. Java JDK is the core tool for developing and running Java applications, and the build tool simplifies the management, construction, and dependency processing of the project, allowing developers to focus on business logic without having to deal with the cumbersome build process.

[0050] Furthermore, by configuring the API access permissions of the Kubernetes cluster, external applications (such as Java programs) can interact with Kubernetes securely. This integration capability enables developers to manage cluster resources in a programmatic manner, such as creating, updating, and deleting Pods and services, improving the flexibility and efficiency of operations and maintenance.

[0051] S2: Deploy the Prometheus monitoring system and the Grafana monitoring system on the Kubernetes cluster. The Prometheus monitoring system is used to collect the metric data of each component in the cluster, and the Grafana monitoring system is used to visualize and analyze the monitoring data collected by Prometheus.

[0052] Among them, Prometheus is an open source monitoring system and time series database, originally developed by Google and now maintained by the Cloud Native Computing Foundation (CNCF). It is designed to collect and store metric data, supports a powerful query language, and can effectively monitor and alert data.

[0053] Among them, Grafana is an open source data visualization and monitoring tool that is widely used to build dynamic dashboards and provides support for multiple data sources (including Prometheus, InfluxDB, Elasticsearch, etc.). Grafana is widely popular for its powerful visualization capabilities and user-friendly interface.

[0054] In a possible implementation, S2 specifically includes: using a Helm tool or a Kustomize tool to deploy a Prometheus monitoring system and a Grafana monitoring system on a Kubernetes cluster.

[0055] S3: Configure the Java client library for your Kubernetes cluster.

[0056] Among them, the Java client library is a tool for interacting with the Kubernetes API, which simplifies the communication between Java applications and Kubernetes clusters.

[0057] Alternatively, to interact with the Kubernetes API using Java, you can use the official Kubernetes Java client library, which provides functionality for interacting with the Kubernetes API.

[0058] In the present invention, the use of the Kubernetes Java client library can significantly improve the efficiency and convenience of interacting with the Kubernetes API, allowing developers to quickly build and maintain integration with the Kubernetes cluster.

[0059] S4: Write Java programs using the Java client library.

[0060] S5: Use the Prometheus monitoring system to monitor the Kubernetes cluster through the Java program and obtain cluster monitoring data.

[0061] In a possible implementation, S5 specifically includes:

[0062] S501: Hardcode the authentication information of the Kubernetes cluster in the Java program to access the Kubernetes API and monitor the Kubernetes cluster through the Prometheus monitoring system.

[0063] S502: The Java program calls the Kubernetes API and obtains cluster monitoring data through the Prometheus monitoring system.

[0064] It should be noted that cluster monitoring data includes the CPU resource consumption of each node in the cluster, the memory usage of each node in the cluster, the health status and status changes of each Pod in Kubernetes, and the processing time and response time of service requests.

[0065] In the present invention, the authentication information is directly in the code, which is convenient for developers to debug and view, and helps to quickly locate problems. At the same time, Prometheus's powerful monitoring capabilities can be closely integrated with Java applications, providing rich indicators and alarm functions.

[0066] Furthermore, using a hard-coded approach in the development environment can maintain consistency and ensure that all developers use the same authentication information.

[0067] Optionally, you need to package the Java application into a container image and deploy it to the Kubernetes cluster. Test it to ensure that the Java application can correctly obtain data from the Kubernetes API and Prometheus.

[0068] In the present invention, by testing the containerized Java application in Kubernetes, it is possible to verify whether the application can correctly obtain data from the Kubernetes API and Prometheus, thereby ensuring the correctness of the function.

[0069] S6: According to the cluster monitoring data, a cluster fault detection result is output through a cluster fault detection model based on a support vector machine.

[0070] Among them, the cluster fault detection model based on support vector machine (SVM) is a method that uses machine learning technology to identify and predict potential faults in the cluster.

[0071] In a possible implementation, the cluster fault detection model based on support vector machine is specifically:

[0072]

[0073] Among them, f( ) represents the prediction function of the model for the input value, sign( ) is the symbol function, l represents the total number of samples, and a i represents a Langge multiplier, y i represents the output label corresponding to the i-th input sample, represents the kernel function, σ represents the smoothing parameter, and b represents the bias term.

[0074] In a possible implementation, the support vector machine-based cluster fault detection model is trained by a gradient descent method.

[0075] Among them, the gradient descent method is an optimization algorithm used to find the minimum or maximum value of a function and is widely used in machine learning and deep learning.

[0076] The specific steps for training by gradient descent are:

[0077] Initialize model parameters.

[0078] Construct the loss function of the model.

[0079]

[0080] Among them, L( ) represents the loss function of the model, represents the sum of squares of weights, C represents hyperparameters, l represents the total number of samples, and y i represents the true label of the i-th sample, f(x i ) represents the input sample x i The model predicted value.

[0081] According to the flow data under different working conditions, the model is trained by the improved gradient descent method:

[0082]

[0083]

[0084]

[0085] Among them, v t+1 represents the value of the momentum vector at the t+1 iteration, β represents the momentum coefficient, and v t represents the value of the momentum vector at iteration t, represents the gradient of the loss function with respect to weights and biases, w t+1 represents the updated weight vector in the t+1th iteration, w t represents the updated weight vector in t iterations, η represents the learning rate, b t+1 represents the updated bias term in the t+1 iteration, b t Represents the current bias term in iteration t.

[0086] In the present invention, SVM can effectively process high-dimensional data and find the best decision boundary, thereby improving the accuracy of fault detection. At the same time, through the improved gradient descent method, the learning strategy can be adjusted according to the different characteristics and distribution of the data to improve the adaptability of the model.

[0087] Furthermore, by optimizing the loss function of the model, the complexity of the model can be effectively controlled and the risk of overfitting can be reduced.

[0088] S7: Based on the fault detection results, determine whether the Kubernetes cluster has a fault. If so, generate an alarm message and proceed to the next step. Otherwise, continue monitoring.

[0089] In the present invention, by combining fault detection and alarm, the operation and maintenance team can make more scientific decisions based on actual data. At the same time, by generating alarm information according to the fault detection results, the Kubernetes cluster can achieve more efficient and reliable operation and maintenance management, and improve the stability and security of the overall system.

[0090] S8: Use the Grafana monitoring system to visualize alarm information.

[0091] In the present invention, the alarm information is presented in the form of charts, graphs, etc., to help operation and maintenance personnel understand the fault conditions and trends more intuitively. At the same time, Grafana can integrate multiple data sources, centrally display monitoring indicators and alarm information, and provide a comprehensive cluster status view.

[0092] In a possible implementation manner, after S8, the method further includes:

[0093] According to the alarm information, an alarm is issued.

[0094] Optionally, an alert is sent out via email.

[0095] Alerts are sent via SMS service.

[0096] and / or sound an alarm via the display.

[0097] In the present invention, alarms are sent through multiple means such as email, text messages, and display screens, increasing the probability of information arrival and ensuring that relevant personnel can receive important notifications. At the same time, multiple notification methods can provide confirmation for faults and avoid information omissions caused by single channel failures.

[0098] In a possible implementation manner, after S8, the method further includes:

[0099] Records the log information of the Kubernetes cluster, including API call records, data processing process, and error information.

[0100] In the present invention, by systematically recording the log information of the Kubernetes cluster, the operation and maintenance team can not only improve the efficiency of fault response and troubleshooting, but also enhance the security and compliance of the system, thereby ensuring the long-term stability and reliable operation of the cluster.

[0101] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0102] (1) In the present invention, Prometheus and Grafana are integrated into the platform to realize the automatic collection and visual analysis of the metric data in the cluster. By building a fault detection model based on support vector machines, the system can monitor the health of the cluster in real time, automatically detect anomalies and generate alarms, thereby effectively improving the speed of fault discovery and response, and meeting the efficient management requirements of modern Kubernetes clusters.

[0103] (2) In the present invention, Prometheus and Grafana are integrated on the Kubernetes cluster through the platform, and the collection and visualization analysis of monitoring data are concentrated in a unified interface. Operation and maintenance personnel can view all monitoring indicators and analyze alarm information in real time in a single Grafana interface without frequently switching between multiple systems, so as to more comprehensively and quickly grasp the overall operation status of the cluster and the health status of each component, thereby realizing efficient centralized monitoring.

[0104] Reference Manual Attached Figure 2 , showing a structural schematic diagram of a platform operation and maintenance monitoring and management system based on cluster management provided by the present invention.

[0105] The present invention further provides a platform operation and maintenance monitoring and management system 20 based on cluster management, which is applied to the above-mentioned platform operation and maintenance monitoring and management method based on cluster management, and comprises:

[0106] Processor 201;

[0107] The memory 202 stores computer-readable instructions, and when the computer-readable instructions are executed by the processor 201, the platform operation and maintenance monitoring management method based on cluster management as described in the method embodiment is implemented.

[0108] The platform operation and maintenance monitoring and management system 20 based on cluster management provided by the present invention can execute the above-mentioned platform operation and maintenance monitoring and management method based on cluster management, and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0109] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0110] (1) In the present invention, Prometheus and Grafana are integrated into the platform to realize the automatic collection and visual analysis of the metric data in the cluster. By building a fault detection model based on support vector machines, the system can monitor the health of the cluster in real time, automatically detect anomalies and generate alarms, thereby effectively improving the speed of fault discovery and response, and meeting the efficient management requirements of modern Kubernetes clusters.

[0111] (2) In the present invention, Prometheus and Grafana are integrated on the Kubernetes cluster through the platform, and the collection and visualization analysis of monitoring data are concentrated in a unified interface. Operation and maintenance personnel can view all monitoring indicators and analyze alarm information in real time in a single Grafana interface without frequently switching between multiple systems, so as to more comprehensively and quickly grasp the overall operation status of the cluster and the health status of each component, thereby realizing efficient centralized monitoring.

[0112] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0113] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0114] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0115] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0116] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0117] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0118] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0119] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0120] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0121] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0122] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0123] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0124] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the platform operation and maintenance monitoring management method based on cluster management as described in the method embodiment is implemented.

[0125] A computer-readable storage medium provided by the present invention can implement the steps and effects of the platform operation and maintenance monitoring management method based on cluster management in the above method embodiment. To avoid repetition, the present invention will not go into details.

[0126] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0127] (1) In the present invention, Prometheus and Grafana are integrated into the platform to realize the automatic collection and visual analysis of the metric data in the cluster. By building a fault detection model based on support vector machines, the system can monitor the health of the cluster in real time, automatically detect anomalies and generate alarms, thereby effectively improving the speed of fault discovery and response, and meeting the efficient management requirements of modern Kubernetes clusters.

[0128] (2) In the present invention, Prometheus and Grafana are integrated on the Kubernetes cluster through the platform, and the collection and visualization analysis of monitoring data are concentrated in a unified interface. Operation and maintenance personnel can view all monitoring indicators and analyze alarm information in real time in a single Grafana interface without frequently switching between multiple systems, so as to more comprehensively and quickly grasp the overall operation status of the cluster and the health status of each component, thereby realizing efficient centralized monitoring.

[0129] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0130] There are a few points to note:

[0131] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention. Other structures may refer to the general design.

[0132] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0133] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0134] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A platform operation and maintenance monitoring management method based on cluster management, characterized in that: include: S1: Build the basic operating environment of the Kubernetes cluster; S2: deploying a Prometheus monitoring system and a Grafana monitoring system on the Kubernetes cluster, wherein the Prometheus monitoring system is used to collect metric data of each component in the cluster, and the Grafana monitoring system is used to visualize and analyze the monitoring data collected by Prometheus; S3: Configure the Java client library for the Kubernetes cluster. S4: Use the Java client library to write a Java program; S5: Using the Prometheus monitoring system to monitor the Kubernetes cluster through the Java program to obtain cluster monitoring data; S6: Outputting a cluster fault detection result according to the cluster monitoring data through a cluster fault detection model based on a support vector machine; S7: judging whether the Kubernetes cluster fails according to the fault detection result, if so, generating an alarm message and proceeding to the next step, otherwise, continuing monitoring; S8: Visually display the alarm information through the Grafana monitoring system.

2. The platform operation and maintenance monitoring management method based on cluster management according to claim 1 is characterized in that: The basic operating environment specifically includes: Ensure that the Kubernetes cluster is running; Install Java JDK and build tools for the Kubernetes cluster; Configure API access permissions for the Kubernetes cluster.

3. The platform operation and maintenance monitoring management method based on cluster management according to claim 2 is characterized in that: The construction tool is specifically: Maven or Gradle.

4. The platform operation and maintenance monitoring management method based on cluster management according to claim 1 is characterized in that: The S2 specifically includes: using the Helm tool or the Kustomize tool to deploy the Prometheus monitoring system and the Grafana monitoring system on the Kubernetes cluster.

5. The platform operation and maintenance monitoring management method based on cluster management according to claim 1, characterized in that: The S5 specifically includes: S501: hard-coding the authentication information of the Kubernetes cluster in a Java program to access the Kubernetes API, and monitoring the Kubernetes cluster through the Prometheus monitoring system; S502: The Java program calls the Kubernetes API and obtains cluster monitoring data through the Prometheus monitoring system.

6. The platform operation and maintenance monitoring management method based on cluster management according to claim 1, characterized in that: The cluster fault detection model based on support vector machine is specifically: ； Among them, f( ) represents the prediction function of the model for the input value, sign( ) is the symbol function, l represents the total number of samples, and a i represents a Langge multiplier, y i represents the output label corresponding to the i-th input sample, represents the kernel function, σ represents the smoothing parameter, and b represents the bias term.

7. The platform operation and maintenance monitoring management method based on cluster management according to claim 1 is characterized in that: The cluster fault detection model based on support vector machine is trained by gradient descent method.

8. The platform operation and maintenance monitoring management method based on cluster management according to claim 1, characterized in that: After S8, the method further includes: An alarm is issued according to the alarm information.

9. The platform operation and maintenance monitoring management method based on cluster management according to claim 1, characterized in that: After S8, the method further includes: The log information of the Kubernetes cluster is recorded, and the log information includes API call records, data processing process and error information.

10. A platform operation and maintenance monitoring and management system based on cluster management, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the platform operation and maintenance monitoring management method based on cluster management as described in any one of claims 1 to 9 is implemented.

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