Intelligent micro-service monitoring and alarming method and system and computer storage medium

Abstract

The invention discloses an intelligent micro-service monitoring and alarming method and system and a computer storage medium, and the method comprises the steps: automatically selecting a micro-service monitoring index according to the type and function of a micro-service; collecting historical monitoring data and an alarm threshold value set by a developer according to the selected micro-service monitoring index, and constructing a training data set; inputting the training data set into a machine learning training model for model training to obtain a micro-service monitoring index threshold prediction model which is used for predicting an alarm threshold of a micro-service monitoring index; inputting real-time monitoring data of micro-service operation into the micro-service monitoring index threshold prediction model, and dynamically calculating an alarm threshold of a micro-service monitoring index by the micro-service monitoring index threshold prediction model; and automatically configuring an alarm rule according to the dynamically calculated alarm threshold value of the micro-service monitoring index. According to the invention, the complexity and challenge of monitoring and alarm configuration in a micro-service architecture are solved, and the operation and maintenance efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of software engineering and development, and specifically provides an intelligent microservice monitoring and alarming method, system, and computer storage medium. Background Art

[0002] In today's software development field, the microservice architecture has been widely welcomed due to its flexibility, scalability, and modularity. The microservice architecture decomposes an application into small, independent services, and each service is responsible for a specific business function. This architecture enables development teams to develop, deploy, and scale each service independently, thereby improving development efficiency and system maintainability.

[0003] However, the microservice architecture also brings some challenges, especially in terms of monitoring and alarming. Due to the large number of microservices, and each service may have its own metrics and alarm thresholds for latency, traffic, errors, resource saturation, etc., manually configuring and managing these monitoring metrics and alarm thresholds becomes very complex and time-consuming. In addition, as microservices change and are updated continuously, the monitoring metrics and alarm thresholds also need to be adjusted accordingly to adapt to the new operating environment and business requirements.

[0004] Existing microservice monitoring and alarming systems usually rely on developers to configure manually, which leads to the following problems:

[0005] Configuration omission: For some microservice modules, developers may forget to configure monitoring and alarming.

[0006] Configuration complexity: As the number of microservices increases, manually configuring monitoring metrics and alarm thresholds becomes very complex and error-prone.

[0007] High maintenance cost: Manual configuration requires a large amount of time and effort, increasing the operation and maintenance cost.

[0008] Response delay: Manual configuration may lead to alarm response delay and fail to detect problems in a timely manner.

[0009] False alarms and missed alarms: Due to the lack of a dynamic adjustment mechanism, existing alarm systems may generate false alarms (unnecessary alarms) or missed alarms (undetected problems).

[0010] Lack of personalization: The monitoring and alarming requirements of each microservice may be different, but existing systems often cannot provide personalized configuration.

[0011] In view of this, the present invention patent is specifically proposed. Summary of the Invention

[0012] In view of the above technical problems, the present invention provides an intelligent microservice monitoring and alarming method, system and computer storage medium, aiming to solve the complexity and challenges of monitoring and alarm configuration in the microservice architecture through automated and intelligent means, so as to improve the operation and maintenance efficiency, reduce human errors, and enhance the overall performance and stability of the system.

[0013] Specifically, the following technical solutions are adopted:

[0014] In the first aspect, the present invention provides an intelligent microservice monitoring and alarming method, including:

[0015] Automatically select microservice monitoring metrics according to the type and function of the microservice;

[0016] Collect historical monitoring data and the alarm thresholds set by developers according to the selected microservice monitoring metrics to construct a training data set;

[0017] Input the training data set into a machine learning training model for model training to obtain a microservice monitoring metric threshold prediction model for predicting the alarm thresholds of microservice monitoring metrics;

[0018] Input the real-time monitoring data of the microservice operation into the microservice monitoring metric threshold prediction model, and the microservice monitoring metric threshold prediction model dynamically calculates the alarm thresholds of the microservice monitoring metrics;

[0019] Automatically configure alarm rules according to the dynamically calculated alarm thresholds of the microservice monitoring metrics.

[0020] As an optional implementation manner of the present invention, in an intelligent microservice monitoring and alarming method of the present invention, the automatically selecting microservice monitoring metrics according to the type and function of the microservice includes:

[0021] Add default microservice monitoring metrics for the microservice based on latency, traffic, errors, and resource saturation. The default microservice monitoring metrics include system CPU load, memory, disk, success rate and failure rate of traffic, and latency;

[0022] Add default language technology stack monitoring metrics for the programming language of the microservice.

[0023] As an optional implementation manner of the present invention, in an intelligent microservice monitoring and alarming method of the present invention, when inputting the real-time monitoring data of the microservice operation into the microservice monitoring metric threshold prediction model, the microservice monitoring metric threshold prediction model dynamically calculates the alarm thresholds of the microservice monitoring metrics and adjusts the alarm thresholds according to the microservice changes and business requirements.

[0024] As an alternative embodiment of the present invention, in an intelligent microservice monitoring and alarming method of the present invention, the adjusted alarm threshold and the corresponding microservice monitoring index data are used as a feedback data training set and fed back to the microservice monitoring index threshold prediction model for training;

[0025] The microservice monitoring index threshold prediction model optimizes and adjusts the machine learning model parameters according to the feedback data training set.

[0026] As an alternative embodiment of the present invention, an intelligent microservice monitoring and alarming method of the present invention includes: automatically identifying the developer information of the microservice module by analyzing the operators of the compilation process and the online release process events, and storing the developer information in the developer information database.

[0027] As an alternative embodiment of the present invention, in an intelligent microservice monitoring and alarming method of the present invention, the automatic configuration of the alarm rule according to the dynamically calculated alarm threshold of the microservice monitoring index includes:

[0028] Automatically generating an alarm condition according to the dynamically calculated alarm threshold of the microservice monitoring index;

[0029] Determining the alarm object according to the alarm condition and the developer information database;

[0030] Configuring the alarm method according to the alarm condition and the alarm object.

[0031] As an alternative embodiment of the present invention, an intelligent microservice monitoring and alarming method of the present invention includes:

[0032] Applying the automatically configured alarm rule to the service center of DevOps, monitoring the running status of the microservice in real time, and automatically triggering the alarm rule to notify the corresponding alarm object when the microservice monitoring index exceeds the alarm threshold.

[0033] In a second aspect, the present invention provides an intelligent microservice monitoring and alarming system, including:

[0034] A monitoring index automatic setting module that automatically selects microservice monitoring indexes according to the type and function of the microservice;

[0035] A machine learning model training module that collects historical monitoring data and the alarm threshold set by the developer according to the selected microservice monitoring index, constructs a training data set, inputs the training data set into the machine learning training model for model training, and obtains a microservice monitoring index threshold prediction model for predicting the alarm threshold of the microservice monitoring index;

[0036] An alarm threshold dynamic prediction module inputs the real-time monitoring data of microservice operation into the microservice monitoring index threshold prediction model, and the microservice monitoring index threshold prediction model dynamically calculates the alarm threshold of the microservice monitoring index;

[0037] An automated alarm configuration module automatically configures alarm rules according to the dynamically calculated alarm threshold of the microservice monitoring index.

[0038] In a third aspect, the present invention provides an electronic device, including a processor and a memory. The memory is used to store computer-executable programs. When the computer program is executed by the processor, the processor executes the intelligent microservice monitoring and alarm method.

[0039] In a fourth aspect, the present invention provides a computer-readable recording medium storing a computer-executable program, and when the computer-executable program is executed, the intelligent microservice monitoring and alarm method is implemented.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] The intelligent microservice monitoring and alarm method and system of the present invention have the following technical characteristics:

[0042] Automated developer identification: By integrating into the CI / CD process and the online release process, the system can automatically identify the developers of microservice modules, reducing the workload of manually associating developers and improving the accuracy of alarm notifications.

[0043] Automatic setting of monitoring indicators: The system automatically selects appropriate monitoring indicators according to the type and function of the microservice, ensuring the comprehensiveness and pertinence of monitoring and helping to detect problems of the microservice in a timely manner.

[0044] Machine learning model training: Using historical monitoring data and the feedback of developers' manual adjustment of monitoring alarms, the system can train a model that can predict alarm thresholds, enabling the alarm thresholds to be dynamically adjusted to adapt to different operating environments and improving the accuracy of alarms.

[0045] Dynamic adjustment of alarm thresholds: The system dynamically calculates alarm thresholds according to real-time monitoring data and the trained model, ensuring the timeliness and adaptability of alarm thresholds and helping to detect and respond to problems of microservices in a timely manner.

[0046] Automated alarm configuration: The system automatically configures alarm rules according to the dynamically calculated alarm thresholds, realizing the automation of alarm configuration, reducing manual intervention, and improving the operation and maintenance efficiency.

[0047] The intelligent microservice monitoring and alarm method and system of the present invention have the following technical effects:

[0048] Improve operation and maintenance efficiency: By means of automation and intelligence, the workload of manual configuration of monitoring and alarm is reduced, and the operation and maintenance efficiency is improved.

[0049] Reduce human errors: The automated process reduces the possibility of human configuration errors, improving the accuracy and timeliness of alarms.

[0050] Improve alarm accuracy: By training and optimizing machine learning models, the accuracy and timeliness of alarms are improved, which helps to detect and solve microservice problems in a timely manner.

[0051] Improve system stability: Through the automated monitoring and alarm mechanism, potential problems can be detected and solved in a timely manner, improving the stability and reliability of the system.

[0052] Strong adaptability: The system can dynamically adjust alarm thresholds and rules according to the changes of microservices, adapt to different operating environments, and improve the adaptability of the system.

[0053] In summary, an intelligent microservice monitoring and alarm method and system of the present invention solve the complexity and challenges of monitoring and alarm configuration in the microservice architecture through automation and intelligence, improve operation and maintenance efficiency, reduce human errors, and at the same time improve the accuracy and timeliness of alarms through machine learning technology, which helps to enhance the stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 The flowchart of an intelligent microservice monitoring and alarm method in Embodiment 1 of the present invention;

[0055] Figure 2 The schematic structural diagram of an electronic device in Embodiment 2 of the present invention;

[0056] Figure 3 The schematic diagram of a computer-readable recording medium in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention.

[0058] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely represents some embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0059] It should be noted that, without conflict, the embodiments in the present invention and the features and technical solutions in the embodiments can be combined with each other.

[0060] It should be noted that like reference numerals and letters indicate like items in the following figures, and thus, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0061] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "upper", "lower", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the present invention is usually placed during use, or the orientation or positional relationship commonly understood by those skilled in the art. Such terms are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention. In addition, terms such as "first", "second", etc. are only used for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0062] Example 1

[0063] As Figure 1 shown, an intelligent microservice monitoring and alarming method according to this embodiment includes:

[0064] Automatically select microservice monitoring metrics according to the type and function of the microservice;

[0065] Collect historical monitoring data and the alarm thresholds set by developers according to the selected microservice monitoring metrics to construct a training data set;

[0066] Input the training data set into a machine learning training model for model training to obtain a microservice monitoring metric threshold prediction model for predicting the alarm thresholds of microservice monitoring metrics;

[0067] Input the real-time monitoring data of the microservice operation into the microservice monitoring metric threshold prediction model, and the microservice monitoring metric threshold prediction model dynamically calculates the alarm thresholds of the microservice monitoring metrics;

[0068] Automatically configure alarm rules according to the dynamically calculated alarm thresholds of the microservice monitoring metrics.

[0069] An intelligent microservice monitoring and alarming method according to this embodiment has the following technical features:

[0070] Automatically set monitoring metrics: Based on the characteristics of the microservice, automatically set appropriate monitoring metrics for it, such as latency, traffic, errors, and resource saturation, etc.

[0071] Dynamically adjust the alarm threshold: Utilize machine learning techniques to dynamically adjust the alarm threshold according to the actual operating conditions of the microservices and the feedback from developers, so as to reduce false alarms and missed alarms.

[0072] Automated alarm configuration: Through an automated process, reduce the workload of manually configuring monitoring alarms and improve the operation and maintenance efficiency.

[0073] Improve system stability: Through an automated monitoring and alarm mechanism, promptly detect and solve potential problems, and improve the stability and reliability of the system.

[0074] An intelligent microservice monitoring and alarm method in this embodiment aims to solve the complexity and challenges of monitoring and alarm configuration in the microservice architecture through automated and intelligent means, thereby improving the operation and maintenance efficiency, reducing human errors, and enhancing the overall performance and stability of the system.

[0075] As an alternative implementation of this embodiment, in an intelligent microservice monitoring and alarm method described in this embodiment, the automatically selecting microservice monitoring metrics according to the type and function of the microservices includes:

[0076] Add default microservice monitoring metrics for microservices based on latency, traffic, errors, and resource saturation. The default microservice monitoring metrics include system CPU load, memory, disk, success and failure rates of traffic, and latency.

[0077] Add default language technology stack monitoring metrics for the programming language of the microservices. For example, for GO and JAVA, add alarms for the default language technology stack, such as coroutine or thread leaks, GC exceptions, etc.

[0078] In an intelligent microservice monitoring and alarm method in this embodiment, input the real-time monitoring data of the microservice operation into the microservice monitoring metric threshold prediction model. The microservice monitoring metric threshold prediction model dynamically calculates the alarm threshold of the microservice monitoring metrics, and adjusts the alarm threshold according to the changes of the microservices and business requirements to adapt to different operating environments.

[0079] In an intelligent microservice monitoring and alarm method in this embodiment, use the adjusted alarm threshold and the corresponding microservice monitoring metric data as a feedback data training set, and feedback it to the microservice monitoring metric threshold prediction model for training;

[0080] The microservice monitoring metric threshold prediction model optimizes and adjusts the machine learning model parameters according to the feedback data training set.

[0081] An intelligent microservice monitoring and alarming method in this embodiment can achieve feedback loops and continuous optimization. If developers are not satisfied with the alarm threshold settings, they will manually adjust the alarm threshold. We use the threshold of this manual adjustment of the alarm item and the monitoring and alarming data as a new dataset and feedback it to the model training platform. The training platform adjusts the machine learning model according to the feedback. The system regularly evaluates the accuracy and timeliness of the alarms and further optimizes the model and alarm configuration according to the evaluation results. Through continuous learning and optimization, the system continuously improves the accuracy of the alarms and the operation and maintenance efficiency.

[0082] An intelligent microservice monitoring and alarming method in this embodiment includes: automatically identifying the developer information of microservice modules by analyzing the operators of the compilation process and the online release process events, and storing the developer information in the developer information database.

[0083] An intelligent microservice monitoring and alarming method in this embodiment realizes automatic developer identification: by integrating into the analysis compilation process and the online release process, the system can reduce the workload of manually associating developers and improve the accuracy of alarm notifications.

[0084] Furthermore, in an intelligent microservice monitoring and alarming method in this embodiment, the automatically configuring alarm rules according to the alarm threshold of dynamically calculating microservice monitoring metrics includes:

[0085] Automatically generating alarm conditions according to the alarm threshold of dynamically calculating microservice monitoring metrics;

[0086] Determining alarm objects according to the alarm conditions and the developer information database;

[0087] Configuring alarm methods according to the alarm conditions and the alarm objects.

[0088] Among them, the alarm conditions include monitoring the running status of the microservice, obtaining the microservice monitoring metrics in real time, and judging whether the microservice monitoring metrics exceed the alarm threshold. The alarm objects are the developers who automatically identify the microservice modules. The alarm methods include emails, text messages, instant messaging tools, etc.

[0089] In addition, it is also possible to use a rule engine to automatically configure alarm rules: use the rule engine to define alarm rules, and these rules can trigger alarms according to specific metrics and alarm thresholds of the microservice. The rule engine can be configured to respond to real-time data and automatically adjust the alarm threshold according to the predefined logic. Advantages of the rule engine: relatively simple and does not require a complex machine learning model.

[0090] Specifically, an intelligent microservice monitoring and alarming method in this embodiment includes:

[0091] Apply the automatically completed configuration alarm rules to the DevOps service center, monitor the running status of microservices in real time, and when the microservice monitoring metrics exceed the alarm threshold, automatically trigger the alarm rules to notify the corresponding alarm objects.

[0092] An intelligent microservice monitoring and alarming method in this embodiment has the following technical features:

[0093] Automated developer identification: By integrating into the CI / CD process and the online release process, the system can automatically identify the developers of microservice modules, reducing the workload of manually associating developers and improving the accuracy of alarm notifications.

[0094] Automatic setting of monitoring metrics: The system automatically selects appropriate monitoring metrics according to the type and function of microservices, ensuring the comprehensiveness and pertinence of monitoring and helping to detect problems of microservices in a timely manner.

[0095] Machine learning model training: Using historical monitoring data and the feedback of developers' manual adjustment of monitoring alarms, the system can train a model that can predict the alarm threshold, enabling the alarm threshold to be dynamically adjusted to adapt to different operating environments and improving the accuracy of alarms.

[0096] Dynamic adjustment of alarm threshold: The system dynamically calculates the alarm threshold according to real-time monitoring data and the trained model, ensuring the timeliness and adaptability of the alarm threshold and helping to detect and respond to problems of microservices in a timely manner.

[0097] Automated alarm configuration: The system automatically configures alarm rules according to the dynamically calculated alarm threshold, realizing the automation of alarm configuration, reducing manual intervention, and improving the operation and maintenance efficiency.

[0098] An intelligent microservice monitoring and alarming method in this embodiment has the following technical effects:

[0099] Improve operation and maintenance efficiency: Through automated and intelligent means, the workload of manually configuring monitoring alarms is reduced, and the operation and maintenance efficiency is improved.

[0100] Reduce human errors: The automated process reduces the possibility of human configuration errors, improving the accuracy and timeliness of alarms.

[0101] Improve alarm accuracy: By training and optimizing the machine learning model, the accuracy and timeliness of alarms are improved, helping to detect and solve problems of microservices in a timely manner.

[0102] Improve system stability: Through the automated monitoring and alarm mechanism, potential problems can be detected and solved in a timely manner, improving the stability and reliability of the system.

[0103] Strong adaptability: The system can dynamically adjust the alarm thresholds and rules according to the changes in microservices, adapt to different operating environments, and improve the adaptability of the system.

[0104] In summary, an intelligent microservice monitoring and alarming method according to this embodiment solves the complexity and challenges of monitoring and alarm configuration in the microservice architecture through automated and intelligent means, improves the operation and maintenance efficiency, reduces human errors, and at the same time improves the accuracy and timeliness of alarms through machine learning technology, which helps to enhance the stability and reliability of the system.

[0105] This embodiment also provides an intelligent microservice monitoring and alarming system, including:

[0106] A monitoring metric automatic setting module that automatically selects microservice monitoring metrics according to the type and function of microservices;

[0107] A machine learning model training module that collects historical monitoring data and the alarm thresholds set by developers according to the selected microservice monitoring metrics, constructs a training data set, inputs the training data set into a machine learning training model for model training, and obtains a microservice monitoring metric threshold prediction model for predicting the alarm thresholds of microservice monitoring metrics;

[0108] An alarm threshold dynamic prediction module that inputs the real-time monitoring data of the microservice operation into the microservice monitoring metric threshold prediction model, and the microservice monitoring metric threshold prediction model dynamically calculates the alarm thresholds of microservice monitoring metrics;

[0109] An automated alarm configuration module that automatically configures alarm rules according to the dynamically calculated alarm thresholds of microservice monitoring metrics.

[0110] An intelligent microservice monitoring and alarming system according to this embodiment:

[0111] The monitoring metric automatic setting module: Based on the characteristics of microservices, it automatically sets appropriate monitoring metrics for them, such as latency, traffic, errors, and resource saturation.

[0112] The alarm threshold dynamic prediction module: Utilizing machine learning technology, it dynamically adjusts the alarm thresholds according to the actual operation of microservices and the feedback from developers to reduce false alarms and missed alarms.

[0113] The automated alarm configuration module: Through an automated process, it reduces the workload of manually configuring monitoring alarms and improves the operation and maintenance efficiency.

[0114] An intelligent microservice monitoring and alarming system according to this embodiment aims to solve the complexity and challenges of monitoring and alarm configuration in the microservice architecture through automated and intelligent means, thereby improving the operation and maintenance efficiency, reducing human errors, and enhancing the overall performance and stability of the system.

[0115] As an alternative implementation of this embodiment, the monitoring index automatic setting module in this embodiment automatically selects microservice monitoring indexes according to the type and function of the microservice, including:

[0116] Add default microservice monitoring indexes for microservices based on latency, traffic, errors, and resource saturation. The default microservice monitoring indexes include system CPU load, memory, disk, success rate and failure of traffic, and latency;

[0117] Add default language technology stack monitoring indexes for the programming language of the microservice. For example, for GO and JAVA, add alarms for the default language technology stack, such as goroutine or thread leakage, GC exceptions, etc.

[0118] The alarm threshold dynamic prediction module in this embodiment inputs the real-time monitoring data of the microservice operation into the microservice monitoring index threshold prediction model. The microservice monitoring index threshold prediction model dynamically calculates the alarm threshold of the microservice monitoring index, and adjusts the alarm threshold according to the microservice changes and business requirements to adapt to different operating environments.

[0119] An intelligent microservice monitoring and alarm system in this embodiment uses the adjusted alarm threshold and the corresponding microservice monitoring index data as a feedback data training set, and feeds it back to the microservice monitoring index threshold prediction model for training;

[0120] The microservice monitoring index threshold prediction model optimizes and adjusts the machine learning model parameters according to the feedback data training set.

[0121] An intelligent microservice monitoring and alarm system in this embodiment can achieve feedback loop and continuous optimization. If the developer is not satisfied with the alarm threshold setting, the developer will manually adjust the alarm threshold. We use the threshold of this manually adjusted alarm item and the monitoring and alarm data as a new data set and feed it back to the model training platform. The training platform adjusts the machine learning model according to the feedback. The system regularly evaluates the accuracy and timeliness of the alarms, and further optimizes the model and alarm configuration according to the evaluation results. Through continuous learning and optimization, the system continuously improves the accuracy of the alarms and the operation and maintenance efficiency.

[0122] An intelligent microservice monitoring and alarm system in this embodiment includes a developer automatic identification module: by analyzing the operators of the compilation process and the online release process events, automatically identify the developer information of the microservice module, and the developer information is stored in the developer information database.

[0123] An intelligent microservice monitoring and alarm system in this embodiment, the developer automatic identification module realizes automatic developer identification: by integrating into the analysis compilation process and the online release process, the system can reduce the workload of manually associating developers and improve the accuracy of alarm notifications.

[0124] Furthermore, the automated alarm configuration module of this embodiment automatically configures alarm rules according to the alarm thresholds dynamically calculated for microservice monitoring metrics, including:

[0125] Automatically generate alarm conditions based on the alarm thresholds dynamically calculated for microservice monitoring metrics;

[0126] Determine the alarm objects according to the alarm conditions and the developer information database;

[0127] Configure the alarm methods according to the alarm conditions and the alarm objects.

[0128] Among them, the alarm conditions include monitoring the running status of the microservice, obtaining the microservice monitoring metrics in real time, and determining whether the microservice monitoring metrics exceed the alarm thresholds. The alarm objects are the developers who automatically identify the microservice modules. The alarm methods include emails, text messages, instant messaging tools, etc.

[0129] In addition, the automated alarm configuration module can also use a rule engine to automatically configure alarm rules: use the rule engine to define alarm rules, and these rules can trigger alarms according to specific metrics and alarm thresholds of the microservice. The rule engine can be configured to respond to real-time data and automatically adjust the alarm thresholds according to predefined logic. Advantages of the rule engine: relatively simple and does not require complex machine learning models.

[0130] Specifically, an intelligent microservice monitoring and alarm system of this embodiment includes an alarm module:

[0131] Apply the automatically completed configured alarm rules to the DevOps service center, monitor the running status of the microservice in real time, and when the microservice monitoring metrics exceed the alarm thresholds, automatically trigger the alarm rules and notify the corresponding alarm objects.

[0132] An intelligent microservice monitoring and alarm system of this embodiment has the following technical features:

[0133] Developer automatic identification module: By integrating into the CI / CD process and the online release process, the system can automatically identify the developers of microservice modules, reducing the workload of manually associating developers and improving the accuracy of alarm notifications.

[0134] Monitoring metric automatic setting module: The system automatically selects appropriate monitoring metrics according to the type and function of the microservice, ensuring the comprehensiveness and pertinence of monitoring and helping to detect problems of the microservice in a timely manner.

[0135] Machine learning model training module: Using historical monitoring data and the feedback of developers' manual adjustment of monitoring alarms, the system can train a model that can predict alarm thresholds, enabling the alarm thresholds to be dynamically adjusted to adapt to different operating environments and improving the accuracy of alarms.

[0136] Alarm threshold dynamic prediction module: The system dynamically calculates the alarm threshold according to real-time monitoring data and the trained model, ensuring the timeliness and adaptability of the alarm threshold, and helping to detect and respond to problems of microservices in a timely manner.

[0137] Automated alarm configuration module: The system automatically configures alarm rules according to the dynamically calculated alarm threshold, realizing the automation of alarm configuration, reducing manual intervention, and improving operation and maintenance efficiency.

[0138] An intelligent microservice monitoring and alarm system according to this embodiment has the following technical effects:

[0139] Improve operation and maintenance efficiency: Through automated and intelligent means, the workload of manually configuring monitoring and alarms is reduced, and operation and maintenance efficiency is improved.

[0140] Reduce human errors: The automated process reduces the possibility of human configuration errors, improving the accuracy and timeliness of alarms.

[0141] Improve alarm accuracy: By training and optimizing the machine learning model, the accuracy and timeliness of alarms are improved, helping to detect and solve problems of microservices in a timely manner.

[0142] Improve system stability: Through the automated monitoring and alarm mechanism, potential problems can be detected and solved in a timely manner, improving the stability and reliability of the system.

[0143] Strong adaptability: The system can dynamically adjust the alarm threshold and rules according to the changes of microservices, adapt to different operating environments, and improve the adaptability of the system.

[0144] In summary, an intelligent microservice monitoring and alarm system according to this embodiment solves the complexity and challenges of monitoring and alarm configuration in the microservice architecture through automated and intelligent means, improves operation and maintenance efficiency, reduces human errors, and at the same time improves the accuracy and timeliness of alarms through machine learning technology, helping to enhance the stability and reliability of the system.

[0145] Example 2

[0146] Next, an embodiment of the electronic device of the present invention is described. This electronic device can be regarded as a specific physical implementation manner of the above method and device embodiments of the present invention. For the details described in the embodiment of the electronic device of the present invention, they should be regarded as a supplement to the above method or device embodiments; for the details not disclosed in the embodiment of the electronic device of the present invention, they can be implemented with reference to the above method or device embodiments.

[0147] Figure 2It is a schematic structural diagram of an electronic device according to an embodiment of the present invention. The electronic device includes a processor and a memory. The memory is used to store computer-executable programs. When the computer program is executed by the processor, the processor executes an intelligent microservice monitoring and alarming method according to Embodiment 1.

[0148] As Figure 2 shown, the electronic device is presented in the form of a general-purpose computing device. The processor can be one or multiple and work collaboratively. The present invention does not exclude distributed processing, that is, the processors can be dispersed in different physical devices. The electronic device of the present invention is not limited to a single entity and can also be the sum of multiple physical devices.

[0149] The memory stores computer-executable programs, usually machine-readable code. The computer-readable program can be executed by the processor so that the electronic device can execute the method of the present invention or at least some of the steps in the method.

[0150] The memory includes volatile memory, such as a random access storage unit (RAM) and / or a cache storage unit, and can also be non-volatile memory, such as a read-only storage unit (ROM).

[0151] Optionally, in this embodiment, the electronic device further includes an I / O interface, which is used for the electronic device to exchange data with external devices. The I / O interface can represent one or more of several bus structures, including a memory unit bus or a memory unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any bus structure in multiple bus structures.

[0152] It should be understood that Figure 2 the shown electronic device is only an example of the present invention. The electronic device of the present invention may further include elements or components not shown in the above example. For example, some electronic devices further include a display unit such as a display screen, and some electronic devices further include human-computer interaction elements, such as buttons, keyboards, etc. As long as the electronic device can execute the computer-readable program in the memory to implement the method of the present invention or at least some of the steps of the method, it can be considered as the electronic device covered by the present invention.

[0153] Figure 3 It is a schematic diagram of a computer-readable recording medium according to an embodiment of the present invention. As Figure 3As shown, a computer-executable program is stored in a computer-readable recording medium. When the computer-executable program is executed, it implements an intelligent microservice monitoring and alarming method according to Embodiment 1 of the present invention. The computer-readable recording medium may include a data signal propagated in a baseband or as part of a carrier wave, in which the readable program code is carried. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable recording medium may also be any readable medium other than the readable recording medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable recording medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination of the above.

[0154] The program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., connected through the Internet using an Internet service provider).

[0155] From the above description of the embodiments, those skilled in the art can easily understand that the present invention can be implemented by hardware capable of executing a specific computer program, such as the system of the present invention, and the electronic processing unit, server, client, mobile phone, control unit, processor, etc. included in the system. The present invention can also be implemented by computer software for executing the method of the present invention, such as control software executed by a microprocessor, an electronic control unit, a client, a server, etc. However, it should be noted that the computer software for executing the method of the present invention is not limited to being executed in one or a specific number of hardware entities, and it can also be implemented in a distributed manner by unspecified specific hardware. For computer software, the software product can be stored in a computer-readable recording medium (which can be a CD-ROM, USB flash drive, mobile disk, etc.), or can be distributed and stored on a network, as long as it can enable an electronic device to execute the method according to the present invention.

[0156] The above embodiments are only used to illustrate the present invention rather than limit the technical solutions described in the present invention. Although this specification has described the present invention in detail with reference to the above respective embodiments, the present invention is not limited to the above specific embodiments. Therefore, any modification or equivalent replacement of the present invention; and all technical solutions and their improvements that do not depart from the spirit and scope of the invention are covered by the scope of the claims of the present invention.

Claims

1. An intelligent microservice monitoring and alarm method, characterized in that: include: Automatically select microservice monitoring indicators based on the type and function of microservices; Collect historical monitoring data and the alarm thresholds set by developers based on the selected microservice monitoring indicators to build a training data set; The training data set is input into the machine learning training model for model training to obtain a microservice monitoring indicator threshold prediction model, which is used to predict the alarm threshold of the microservice monitoring indicator; The real-time monitoring data of the microservice operation is input into the microservice monitoring indicator threshold prediction model, and the microservice monitoring indicator threshold prediction model dynamically calculates the alarm threshold of the microservice monitoring indicator; Automatically configure alarm rules based on dynamically calculated alarm thresholds of microservice monitoring indicators.

2. According to the intelligent microservice monitoring and alarm method of claim 1, it is characterized in that: The automatic selection of microservice monitoring indicators based on the type and function of the microservice includes: Add default microservice monitoring indicators based on latency, traffic, errors, and resource saturation for microservices. The default microservice monitoring indicators include system CPU load, memory, disk, traffic success rate, failure, and latency. Add default language technology stack monitoring indicators for microservice programming languages.

3. The intelligent microservice monitoring and alarm method according to claim 1 is characterized in that: The real-time monitoring data of the microservice operation is input into the microservice monitoring indicator threshold prediction model. The microservice monitoring indicator threshold prediction model dynamically calculates the alarm threshold of the microservice monitoring indicator, and adjusts the alarm threshold according to the microservice changes and business needs.

4. The intelligent microservice monitoring and alarm method according to claim 3 is characterized in that: The adjusted alarm threshold and the corresponding microservice monitoring indicator data are used as the feedback data training set and fed back to the microservice monitoring indicator threshold prediction model for training; The microservice monitoring indicator threshold prediction model optimizes and adjusts the machine learning model parameters according to the feedback data training set.

5. The intelligent microservice monitoring and alarm method according to claim 1 is characterized in that: include: By analyzing the operators of the compilation process and the online release process events, the developer information of the microservice module is automatically identified, and the developer information is stored in the developer information database.

6. The intelligent microservice monitoring and alarm method according to claim 5 is characterized in that: The automatic configuration of alarm rules based on the dynamically calculated alarm thresholds of microservice monitoring indicators includes: Automatically generate alarm conditions based on dynamically calculated alarm thresholds of microservice monitoring indicators; Determine the alarm object according to the alarm conditions and the developer information database; Configure the alarm method according to the alarm conditions and alarm objects.

7. The intelligent microservice monitoring and alarm method according to claim 6 is characterized in that: include: Apply the automatically configured alarm rules to the DevOps service center to monitor the running status of microservices in real time. When the microservice monitoring indicators exceed the alarm threshold, the alarm rules are automatically triggered to notify the corresponding alarm objects.

8. An intelligent microservice monitoring and alarm system, characterized in that: include: The monitoring indicator automatic setting module automatically selects microservice monitoring indicators according to the type and function of microservices; The machine learning model training module collects historical monitoring data and the alarm thresholds set by the developer based on the selected microservice monitoring indicators, builds a training data set, inputs the training data set into the machine learning training model for model training, and obtains a microservice monitoring indicator threshold prediction model, which is used to predict the alarm thresholds of the microservice monitoring indicators; The alarm threshold dynamic prediction module inputs the real-time monitoring data of the microservice operation into the microservice monitoring indicator threshold prediction model, and the microservice monitoring indicator threshold prediction model dynamically calculates the alarm threshold of the microservice monitoring indicator; The automated alarm configuration module automatically configures alarm rules based on the alarm thresholds of dynamically calculated microservice monitoring indicators.

9. An electronic device comprising a processor and a memory, characterized in that The memory is used to store a computer executable program. When the computer program is executed by the processor, the processor executes an intelligent microservice monitoring and alarm method as described in any one of claims 1 to 7.

10. A computer-readable recording medium storing a computer-executable program, characterized in that: When the computer executable program is executed, an intelligent microservice monitoring and alarm method as described in any one of claims 1 to 7 is implemented.

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