Container health monitoring method based on embedded real-time environment

Abstract

The invention belongs to the technical field of computer system software, and particularly relates to a container health monitoring method based on an embedded real-time environment, which comprises the following steps of: acquiring running state data of a container in real time, analyzing and processing the acquired running state data of the container, and determining the health state of the container; when it is determined that the health state of the container is abnormal, alarm information is generated, and corresponding processing measures are taken. According to the invention, the health of the container can be monitored more efficiently and accurately.

Description

Technical Field

[0001] The present invention belongs to the technical field of computer system software, and in particular relates to a container health monitoring method based on an embedded real-time environment. Background Art

[0002] With the widespread application of container technology in the embedded field, the number and complexity of containers are increasing, and container health monitoring is becoming more and more important. Traditional container health monitoring methods mainly rely on manual inspections and empirical judgments, which have problems such as untimely and inaccurate monitoring. Therefore, a smarter and more accurate container health monitoring method is needed. Summary of the invention

[0003] In view of this, the present invention provides a container health monitoring method based on an embedded real-time environment, which can monitor the health of the container more efficiently and accurately.

[0004] The technical solution of the present invention:

[0005] A container health monitoring method based on an embedded real-time environment comprises the following steps:

[0006] S101: Collecting the running status data of the container in real time; the running status data includes resource usage data, network connection data and task execution data;

[0007] S102: Analyze and process the collected container operation status data to determine the health status of the container;

[0008] When it is determined in S102 that the health status of the container is abnormal, an alarm message is generated and corresponding processing measures are taken.

[0009] Furthermore, the resource usage data includes CPU usage, memory usage, disk usage and network bandwidth usage; the network connection data includes network connection status, network delay and network packet loss rate; the task execution data includes task execution time, task execution success rate and task execution error rate.

[0010] Furthermore, the method for determining the health status of a container includes the following steps:

[0011] (1) Analyze the collected resource usage data to determine whether the CPU usage, memory usage, disk usage, and network bandwidth usage exceed preset thresholds;

[0012] (2) Analyze the collected network connection data to determine whether the network connection status is normal, and whether the network delay and network packet loss rate exceed preset thresholds;

[0013] (3) analyzing the collected task execution data to determine whether the task execution time exceeds a preset threshold, whether the task execution success rate is lower than a preset threshold, and whether the task execution error rate exceeds a preset threshold;

[0014] (4) Determine the health status of the container based on the analysis results of resource usage data, network connection data, and task execution data.

[0015] Furthermore, the threshold is set by setting a self-learning model, wherein the self-learning model determines a historical peak value based on historical operating status data within a period of time, and uses the peak value as the threshold value.

[0016] Furthermore, when the health status of the container is abnormal, an alarm message is generated and corresponding processing measures are taken, including:

[0017] When the health status of the container is abnormal, an alarm message is generated and sent to relevant personnel;

[0018] According to the abnormal type of the health status of the container, the abnormality of the container is handled by restarting the container, adjusting the resource configuration of the container, or repairing the tasks in the container.

[0019] Furthermore, restarting the container is used to solve deadlock and resource leakage of tasks in the container.

[0020] Further, methods of adjusting container resource configuration include increasing the number of CPU cores, memory capacity, and disk space of the container.

[0021] Furthermore, repairing the task in the container includes restarting the task and fixing errors in the task code.

[0022] Furthermore, in S102, the method for determining the health of the container is:

[0023] The deep neural network model is trained by using the collected operation data; the neural network model includes multiple hidden layers, and the number of neurons in each hidden layer decreases successively to achieve layer-by-layer abstraction of features;

[0024] The operating data collected in real time is input into a trained neural network model, and the neural network model outputs a health status assessment result of the container.

[0025] The beneficial effects of the present invention are:

[0026] The present invention collects more extensive operating status data and determines the health status of the container based on threshold settings, and can monitor the health of the container efficiently and accurately; at the same time, the present invention provides processing methods for coping with different health states, which can quickly and effectively repair errors and ensure that the container can operate normally in the short term. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 This is an example diagram of a container health monitoring method based on an embedded real-time environment in a specific implementation manner of the present invention. DETAILED DESCRIPTION

[0029] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0030] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0031] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0032] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The illustrations only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0033] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0034] In one embodiment of the present invention, a container health monitoring method based on an embedded real-time environment is proposed, comprising the following steps:

[0035] S101: Collecting the running status data of the container in real time; the running status data includes resource usage data, network connection data and task execution data;

[0036] S102: Analyze and process the collected container operation status data to determine the health status of the container;

[0037] When it is determined in S102 that the health status of the container is abnormal, an alarm message is generated and corresponding processing measures are taken.

[0038] In this embodiment, the resource usage data includes CPU usage, memory usage, disk usage and network bandwidth usage; the network connection data includes network connection status, network delay and network packet loss rate; the task execution data includes task execution time, task execution success rate and task execution error rate.

[0039] In this embodiment, the method for determining the health status of a container includes the following steps:

[0040] (1) Analyze the collected resource usage data to determine whether the CPU usage, memory usage, disk usage, and network bandwidth usage exceed preset thresholds;

[0041] (2) Analyze the collected network connection data to determine whether the network connection status is normal, and whether the network delay and network packet loss rate exceed preset thresholds;

[0042] (3) analyzing the collected task execution data to determine whether the task execution time exceeds a preset threshold, whether the task execution success rate is lower than a preset threshold, and whether the task execution error rate exceeds a preset threshold;

[0043] (4) Determine the health status of the container based on the analysis results of resource usage data, network connection data, and task execution data.

[0044] In this embodiment, the threshold is set by setting a self-learning model, which determines a historical peak value based on historical operating status data within a period of time, and uses the peak value as the threshold.

[0045] In this embodiment, when the health status of the container is abnormal, an alarm message is generated and corresponding processing measures are taken, including:

[0046] When the health status of the container is abnormal, an alarm message is generated and sent to relevant personnel;

[0047] According to the abnormal type of the health status of the container, the container is restarted, the container resource configuration is adjusted, or the tasks in the container are repaired to handle the abnormality of the container.

[0048] In this embodiment, restarting the container is used to solve deadlock and resource leakage of tasks in the container.

[0049] In this embodiment, the manner of adjusting the container resource configuration includes: increasing the number of CPU cores, memory container, and disk space of the container.

[0050] In this embodiment, repairing the task in the container includes restarting the task and repairing errors in the task code.

[0051] See also Figure 1 The framework of the container health monitoring method based on the embedded real-time environment of this embodiment includes a data acquisition module, a data analysis module, an alarm module and a processing module, wherein:

[0052] The data collection module is used to collect the running status data of the container in real time, and the running status data includes resource usage data, network connection data and task execution data.

[0053] The data analysis module is used to analyze and process the collected operating status data to determine the health status of the container.

[0054] The alarm module is used to generate alarm information and send it to relevant personnel when the health status of the container is abnormal.

[0055] The processing module is used to take corresponding processing measures according to the abnormal type of the health status of the container, such as restarting the container, adjusting the container resource configuration, and repairing the tasks in the container.

[0056] Implementation of the data acquisition module in this embodiment:

[0057] The data collection module can collect resource usage data such as CPU usage, memory usage, disk usage, and network bandwidth usage of the container in real time by installing a monitoring agent in the container, as well as network connection data such as network connection status, network delay, and network packet loss rate, task execution time, task execution success rate, and task execution error rate. The monitoring agent can then send the collected data to the data analysis module.

[0058] In one embodiment, in S102, the method for determining the health of the container is:

[0059] The deep neural network model is trained by using the collected operation data; the neural network model includes multiple hidden layers, and the number of neurons in each hidden layer decreases successively to achieve layer-by-layer abstraction of features;

[0060] The operating data collected in real time is input into a trained neural network model, and the neural network model outputs a health status assessment result of the container.

[0061] Implementation of the data analysis module in this embodiment:

[0062] The data analysis module is mainly implemented by two parts. First, a deep neural network model needs to be trained with the collected operating data. The model includes multiple hidden layers, and the number of neurons in each hidden layer decreases successively to achieve layer-by-layer abstraction of features.

[0063] The data signals collected in real time are input into the trained neural network model, and the model outputs the health status assessment results of the container.

[0064] This embodiment trains the model based on the collected historical operation status data, inputs the real-time collected data into the trained model, and the model evaluates the current health status of the container; when the health status of the container is abnormal, an alarm message is generated and corresponding processing measures are taken. This embodiment combines the characteristics of container big data with the advantages of deep neural network algorithms, and can simultaneously complete the self-extraction of container big data fault features and the identification of container health status, and can monitor the health status of the container in real time, accurately, and intelligently, improving the reliability and stability of the container.

[0065] Implementation of the alarm module in this embodiment:

[0066] The alarm module can record the alarm information locally or output and send the alarm information to the container orchestration scheduling. The relevant personnel of the container orchestration management will take corresponding processing measures in a timely manner based on the information to ensure the healthy operation of the container.

[0067] The alarm information mainly includes the name of the container, the type of health status abnormality, the time when the abnormality occurred, and other information.

[0068] The implementation of the processing module in this embodiment:

[0069] This module is used to take corresponding processing measures according to the abnormal type of container health status, such as restarting the container, adjusting container resource configuration, and repairing tasks in the container.

[0070] Automatically restarting the container can solve some temporary problems, such as deadlock of tasks in the container and resource leakage.

[0071] Automatically adjusting container resource configuration can include increasing the container's CPU core count, memory capacity, disk space and other resources to meet the container's operating requirements.

[0072] Automatically repairing tasks within a container can include restarting the task, fixing errors in the task code, etc.

[0073] The above is only a specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A container health monitoring method based on an embedded real-time environment, characterized in that: The following steps are involved: S101: Collecting the running status data of the container in real time; the running status data includes resource usage data, network connection data and task execution data; S102: Analyze and process the collected container operation status data to determine the health status of the container; When it is determined in S102 that the health status of the container is abnormal, an alarm message is generated and corresponding processing measures are taken.

2. The container health monitoring method based on an embedded real-time environment according to claim 1 is characterized in that: The resource usage data includes CPU usage, memory usage, disk usage and network bandwidth usage; the network connection data includes network connection status, network delay and network packet loss rate; the task execution data includes task execution time, task execution success rate and task execution error rate.

3. The container health monitoring method based on an embedded real-time environment according to claim 2 is characterized in that: The method for determining the health status of a container includes the following steps: (1) Analyze the collected resource usage data to determine whether the CPU usage, memory usage, disk usage, and network bandwidth usage exceed preset thresholds; (2) Analyze the collected network connection data to determine whether the network connection status is normal, and whether the network delay and network packet loss rate exceed preset thresholds; (3) analyzing the collected task execution data to determine whether the task execution time exceeds a preset threshold, whether the task execution success rate is lower than a preset threshold, and whether the task execution error rate exceeds a preset threshold; (4) Determine the health status of the container based on the analysis results of resource usage data, network connection data, and task execution data.

4. The container health monitoring method based on an embedded real-time environment according to claim 3 is characterized in that: The threshold is set in the following manner: a self-learning model is set, the self-learning model determines a historical peak value based on historical operating status data within a period of time, and the peak value is used as the threshold value.

5. The container health monitoring method based on an embedded real-time environment according to claim 4 is characterized in that: When the health status of a container is abnormal, an alarm message is generated and corresponding processing measures are taken, including: When the health status of the container is abnormal, an alarm message is generated and sent to relevant personnel; According to the abnormal type of the health status of the container, the abnormality of the container is handled by restarting the container, adjusting the resource configuration of the container, or repairing the tasks in the container.

6. The container health monitoring method based on an embedded real-time environment according to claim 5 is characterized in that: Restarting a container is used to resolve deadlocks and resource leaks in tasks within the container.

7. The container health monitoring method based on an embedded real-time environment according to claim 6 is characterized in that: Ways to adjust container resource configuration include increasing the number of CPU cores, memory capacity, and disk space of the container.

8. The container health monitoring method based on an embedded real-time environment according to claim 7 is characterized in that: Repairing a task within a container involves restarting the task and fixing errors in the task code.

9. The container health monitoring method based on an embedded real-time environment according to claim 1 is characterized in that: In S102, the method for determining the health of the container is: The deep neural network model is trained by using the collected operation data; the neural network model includes multiple hidden layers, and the number of neurons in each hidden layer decreases successively to achieve layer-by-layer abstraction of features; The operating data collected in real time is input into a trained neural network model, and the neural network model outputs a health status assessment result of the container.

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