Monitoring system and method

Abstract

The invention provides a monitoring system and method, and the method comprises the steps: collecting a resource monitoring data set, determining an initial data selection parameter according to the resource monitoring data set, and selecting a first resource monitoring data subset through the initial data selection parameter; determining a correlation distance square corresponding to each resource monitoring data point in the resource monitoring data set according to the first resource monitoring data subset; a correlation distance deviation value set is determined according to the correlation distance square, the correlation distance square corresponding to the minimum correlation distance deviation value is obtained, and then resource monitoring data selection parameters are determined; selecting a second resource monitoring data subset according to the resource monitoring data selection parameter, and determining a resource monitoring data correlation distance corresponding to each resource monitoring data point in the resource monitoring data set through the second resource monitoring data subset; and when the related distance of the resource monitoring data is higher than a preset critical value, giving an alarm, so as to solve the technical problem of low alarm accuracy of abnormal use of resources.

Description

Technical Field

[0001] The present application relates to the technical field of resource usage monitoring, and more specifically, to a monitoring system and method. Background Art

[0002] Prometheus is an open source system and service monitoring tool. It is widely used to collect, store and query metric data of various applications and systems. The Prometheus monitoring system can monitor resource usage, performance indicators, application status, etc., and can provide real-time alerts for abnormal situations.

[0003] The resource usage monitoring method based on Prometheus includes the steps of installing and configuring the Prometheus monitoring system, importing Exporter, setting monitoring indicators, configuring alarm rules, setting dashboards and data storage, etc. Through this monitoring method, the resource usage of the system and application can be mastered in real time, anomalies can be discovered in time, and troubleshooting and performance optimization can be performed. In the Prometheus monitoring system, administrators can set monitoring indicators, alarm rules and data storage strategies according to actual needs to achieve high customization. By setting alarm rules, the system can automatically monitor whether the indicator value exceeds the set threshold, and trigger alarm notifications in time when abnormal situations occur, ensuring that key problems can be solved in time and ensuring the stability and reliability of the Prometheus monitoring system. The Prometheus monitoring system also provides powerful data visualization functions. By displaying the real-time change trend of monitoring indicators on the dashboard, administrators can quickly identify performance bottlenecks and abnormal behaviors. However, in the existing technology, when the abnormal usage analysis of resource monitoring data is performed through the Prometheus monitoring system and abnormal alarms are issued based on the results of resource abnormal analysis, there is a technical problem of low accuracy of abnormal resource usage alarms. Summary of the invention

[0004] The present application provides a monitoring system and method to solve the technical problem of low accuracy of abnormal resource usage alarms.

[0005] In order to solve the above technical problems, this application adopts the following technical solutions: In a first aspect, the present application provides a monitoring method, comprising the following steps: Collect resource monitoring data through the Prometheus monitoring system to obtain a resource monitoring data set, determine initial data selection parameters according to the resource monitoring data set, and select a first resource monitoring data subset according to the initial data selection parameters; Determine, according to the first resource monitoring data subset, a square of a correlation distance corresponding to each resource monitoring data point in the resource monitoring data set; Determine a relevant distance deviation value set according to the relevant distance square corresponding to each resource monitoring data point, further determine the relevant distance square corresponding to the minimum relevant distance deviation value, and determine the resource monitoring data selection parameter according to the relevant distance square corresponding to the minimum relevant distance deviation value; Selecting a second resource monitoring data subset according to the resource monitoring data selection parameter, and determining the resource monitoring data related distance corresponding to each resource monitoring data point in the resource monitoring data set through the second resource monitoring data subset; Resource monitoring data points whose correlation distances are higher than preset critical values ​​are used as resource usage abnormality information for alarm.

[0006] In some embodiments, the resource monitoring data is collected through the Prometheus monitoring system, and the resource monitoring data set obtained specifically includes: Determine the median value of resource monitoring data collected through the Prometheus monitoring system; The missing values ​​of the collected resource monitoring data are filled according to the data median of the resource monitoring data to obtain a resource monitoring data set.

[0007] In some embodiments, determining the initial data selection parameters according to the resource monitoring data set specifically includes: Determine the total amount of data of resource monitoring data points in the resource monitoring data set; Determine the data dimensions of the resource monitoring data points in the resource monitoring data set; The initial data selection parameter is determined according to the total amount of data of the resource monitoring data points in the resource monitoring data set and the data dimension of the resource monitoring data points, wherein the initial data selection parameter is determined according to the following formula: in, Indicates the initial data selection parameters, Indicates resource monitoring dataset The total amount of data of resource monitoring data points in the Indicates resource monitoring dataset The data dimensions of the resource monitoring data points, Represents a resource monitoring dataset.

[0008] In some embodiments, determining the square of the relevant distance corresponding to each resource monitoring data point in the resource monitoring data set according to the first resource monitoring data subset specifically includes: Determining a data mean vector and a data covariance matrix of a first resource monitoring data subset; Using the data mean vector as a data position parameter of a first resource monitoring data subset; Using the data covariance matrix as a data scale parameter of a first resource monitoring data subset; Determine the square of the relevant distance corresponding to each resource monitoring data point in the resource monitoring data set according to the data position parameter and the data scale parameter of the first resource monitoring data subset, wherein the square of the relevant distance is determined according to the following formula; in, Indicates resource monitoring data points The corresponding correlation distance squared, Indicates the resource monitoring data set Resource monitoring data points, Indicates the first resource monitoring data subset The data position parameter, Indicates the first resource monitoring data subset The data scale parameter, Represents a transpose operation.

[0009] In some embodiments, determining the square of the correlation distance corresponding to the minimum correlation distance deviation value, and determining the resource monitoring data selection parameter according to the square of the correlation distance corresponding to the minimum correlation distance deviation value specifically includes: Sort the relevant distance squares of all resource monitoring data to obtain a relevant distance square sequence; Determine the resource monitoring data point corresponding to the minimum correlation distance deviation value, and then determine the sequence position of the correlation distance square corresponding to the resource monitoring data point in the correlation distance square sequence; The resource monitoring data selection parameters are determined according to the sequence position and the initial data selection parameters.

[0010] In some embodiments, selecting the second resource monitoring data subset according to the resource monitoring data selection parameter specifically includes: Selecting resource monitoring data points in the resource monitoring data set whose number is equal to the selected parameter value of the resource monitoring data; The resource monitoring data points are combined into a second resource monitoring data subset in chronological order.

[0011] In some embodiments, the preset critical value is determined based on selected parameters of resource monitoring data.

[0012] In a second aspect, the present application provides a monitoring system, including a resource abnormality alarm unit, wherein the resource abnormality alarm unit includes: A first resource monitoring data subset selection module, used to collect resource monitoring data through the Prometheus monitoring system to obtain a resource monitoring data set, determine initial data selection parameters according to the resource monitoring data set, and select a first resource monitoring data subset according to the initial data selection parameters; A correlation distance square determination module, configured to determine, according to the first resource monitoring data subset, a correlation distance square corresponding to each resource monitoring data point in the resource monitoring data set; A resource monitoring data selected parameter determination module, used to determine a relevant distance deviation value set according to the relevant distance square corresponding to each resource monitoring data point, and then determine the relevant distance square corresponding to the minimum relevant distance deviation value, and determine the resource monitoring data selected parameter according to the corresponding relevant distance square; A resource monitoring data related distance determination module, configured to select a second resource monitoring data subset according to the resource monitoring data selection parameter, and determine the resource monitoring data related distance corresponding to each resource monitoring data point in the resource monitoring data set through the second resource monitoring data subset; The resource abnormality alarm module is used to use the resource monitoring data points whose relevant distance of the resource monitoring data is higher than a preset critical value as resource usage abnormality information to issue an alarm.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned monitoring method.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions or codes, and when the instructions or codes are run on a computer, the computer implements the above-mentioned monitoring method when executing.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In a monitoring system and method provided by the present application, resource monitoring data is collected through a Prometheus monitoring system to obtain a resource monitoring data set, an initial data selection parameter is determined according to the resource monitoring data set, and a first resource monitoring data subset is selected according to the initial data selection parameter; the relevant distance square corresponding to each resource monitoring data point in the resource monitoring data set is determined according to the first resource monitoring data subset; a relevant distance deviation value set is determined according to the relevant distance square corresponding to each resource monitoring data point, and then the relevant distance square corresponding to the minimum relevant distance deviation value is determined, and the resource monitoring data selection parameter is determined according to the corresponding relevant distance square; a second resource monitoring data subset is selected according to the resource monitoring data selection parameter, and the resource monitoring data relevant distance corresponding to each resource monitoring data point in the resource monitoring data set is determined through the second resource monitoring data subset; and the resource monitoring data point whose resource monitoring data relevant distance is higher than a preset critical value is used as resource usage abnormality information for alarm.

[0016] In the present application, firstly, by filling in missing values, the impact of missing values ​​in the resource monitoring data set on the resource monitoring data analysis is reduced; secondly, the square of the relevant distance corresponding to each resource monitoring data point in the resource monitoring data set is determined, which helps to identify abnormal resource usage; then, by determining the relevant distance deviation value, the impact of abnormal data on the overall data analysis can be reduced, and the accuracy of resource monitoring data analysis can be improved; finally, by determining and comparing the relevant distances of resource monitoring data, abnormal resource monitoring data points can be detected and alarms can be issued to solve the technical problem of low accuracy of abnormal resource usage alarms. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1 is an exemplary flow chart of a monitoring method according to some embodiments of the present application; Figure 2 is a schematic diagram of exemplary hardware and / or software of a resource abnormality alarm unit according to some embodiments of the present application; Figure 3 It is a structural diagram of a computer device for implementing a monitoring method according to some embodiments of the present application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0020] The embodiment of the present application provides a monitoring system and method, the core of which is to collect resource monitoring data through a Prometheus monitoring system to obtain a resource monitoring data set, determine initial data selection parameters according to the resource monitoring data set, and select a first resource monitoring data subset according to the initial data selection parameters; determine the relevant distance square corresponding to each resource monitoring data point in the resource monitoring data set according to the first resource monitoring data subset; determine a relevant distance deviation value set according to the relevant distance square corresponding to each resource monitoring data point, and then determine the relevant distance square corresponding to the minimum relevant distance deviation value, and determine the resource monitoring data selection parameters according to the corresponding relevant distance squares; select a second resource monitoring data subset according to the resource monitoring data selection parameters, and determine the resource monitoring data relevant distance corresponding to each resource monitoring data point in the resource monitoring data set through the second resource monitoring data subset; and use the resource monitoring data points whose resource monitoring data relevant distance is higher than a preset critical value as resource usage abnormality information for alarm, so as to solve the technical problem of low accuracy of resource abnormal usage alarm.

[0021] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is an exemplary flow chart of a monitoring method according to some embodiments of the present application, and the monitoring method 100 mainly includes the following steps: In step 101, resource monitoring data is collected through the Prometheus monitoring system to obtain a resource monitoring data set, initial data selection parameters are determined according to the resource monitoring data set, and a first resource monitoring data subset is selected according to the initial data selection parameters.

[0022] It should be noted that the Prometheus monitoring system itself is an open source monitoring system that can collect and store resource monitoring data to obtain a resource monitoring data set. When collecting resource monitoring data, you first need to import Exporter or Agent. Exporter is a process or program used to collect resource monitoring data, and Agent is an agent responsible for collecting and sending resource monitoring data to the Prometheus monitoring system. In this application, the resource monitoring data set is a multidimensional data set containing a series of resource monitoring data points sorted by time, where each resource monitoring data point contains monitoring indicator characteristics such as CPU usage, memory usage, or disk read and write.

[0023] In some embodiments, the resource monitoring data is collected through the Prometheus monitoring system to obtain the resource monitoring data set, which can be specifically obtained in the following manner, namely: Determine the median value of resource monitoring data collected through the Prometheus monitoring system; The missing values ​​of the collected resource monitoring data are filled according to the data median of the resource monitoring data to obtain a resource monitoring data set.

[0024] In the specific implementation, the median of all resource monitoring data is determined. In this application, the data median is the middle value after the resource monitoring data is sorted according to the value size. In the resource monitoring data set, there may be missing values, that is, there is no resource monitoring data at certain time points. Use the determined data median to fill in the missing resource monitoring data, that is, set the resource monitoring data value of the missing point to the data median. After completing the data median calculation and missing value filling, the complete resource monitoring data set can be obtained. Through the above method, the impact of missing data on subsequent resource usage analysis can be reduced.

[0025] In some embodiments, the initial data selection parameters may be determined according to the resource monitoring data set in the following manner, namely: Determine the total amount of data of resource monitoring data points in the resource monitoring data set; Determine the data dimensions of the resource monitoring data points in the resource monitoring data set; The initial data selection parameter is determined according to the total amount of data of the resource monitoring data points in the resource monitoring data set and the data dimension of the resource monitoring data points. In specific implementation, the initial data selection parameter can be determined according to the following formula: in, Indicates the initial data selection parameters, Indicates resource monitoring dataset The total amount of data of resource monitoring data points in the Indicates resource monitoring dataset The data dimensions of the resource monitoring data points, Represents a resource monitoring data set. It should be noted that the value of the initial data selection parameter determines the number of resource monitoring data points selected from the resource monitoring data set, that is, the value of the initial data selection parameter is the number of selected resource monitoring data points. The data dimension of the resource monitoring data point represents the number of monitoring indicator features contained in each resource monitoring data point.

[0026] In some embodiments, the first resource monitoring data subset is selected by the initial data selection parameter. After the initial data selection parameter is determined according to the above method, a number of resource monitoring data points equal to the value of the initial data selection parameter are selected in the resource monitoring data set, and these resource monitoring data points are combined into the first resource monitoring data subset in chronological order.

[0027] It should be noted that by filling missing values, the impact of missing values ​​in the resource monitoring data set on resource monitoring data analysis is reduced, the continuity of resource monitoring data is ensured, and after the first resource monitoring data subset is selected, more efficient resource monitoring data analysis can be performed.

[0028] In step 102, the square of the relevant distance corresponding to each resource monitoring data point in the resource monitoring data set is determined according to the first resource monitoring data subset.

[0029] In some embodiments, the following method may be used to determine the square of the relevant distance corresponding to each resource monitoring data point in the resource monitoring data set according to the first resource monitoring data subset, namely: Determining a data mean vector and a data covariance matrix of a first resource monitoring data subset; Using the data mean vector as a data position parameter of a first resource monitoring data subset; Using the data covariance matrix as a data scale parameter of a first resource monitoring data subset; Determine the square of the relevant distance corresponding to each resource monitoring data point in the resource monitoring data set according to the data position parameter and the data scale parameter of the first resource monitoring data subset. In a specific implementation, the square of the relevant distance can be determined according to the following formula; in, Indicates resource monitoring data points The square of the correlation distance, Indicates the resource monitoring data set Resource monitoring data points, Indicates the first resource monitoring data subset The data position parameter, Indicates the first resource monitoring data subset The data scale parameter, Represents a transposition operation. It should be noted that the square of the correlation distance represents the square of the correlation distance. The correlation distance is an indicator for measuring the difference between the resource monitoring data point in the resource monitoring data set and the first resource monitoring data subset. The smaller the correlation distance, the smaller the difference between the resource monitoring data point in the resource monitoring data set and the first resource monitoring data subset. The larger the correlation distance, the greater the difference between the resource monitoring data point in the resource monitoring data set and the first resource monitoring data subset.

[0030] In the above embodiment, for the first resource monitoring data subset, the data mean of each monitoring indicator feature is calculated to form a data mean vector, the data mean vector contains the average value of each monitoring indicator feature in the first resource monitoring data subset, and the data covariance matrix of the first resource monitoring data subset is calculated based on the data mean vector. The calculated data mean vector is regarded as the data position parameter of the first resource monitoring data subset. The data position parameter describes the position of the first resource monitoring data subset as a whole in the dimension, that is, the average level of the monitoring indicator features of the first resource monitoring data subset. The calculated data covariance matrix is ​​regarded as the data scale parameter of the first resource monitoring data subset. The data scale parameter reflects the degree of diffusion of the first resource monitoring data subset in different dimensions, that is, the correlation between different monitoring indicator features in the first resource monitoring data subset.

[0031] It should be noted that, by using the data location parameters and data scale parameters of the first resource monitoring data subset, the square of the relevant distance of each resource monitoring data point in the resource monitoring data set can be determined, which helps to identify abnormal resource usage and take timely measures to avoid system failures.

[0032] In step 103, a set of relevant distance deviation values ​​is determined according to the relevant distance squares corresponding to each resource monitoring data point, and then the relevant distance square corresponding to the minimum relevant distance deviation value is determined, and resource monitoring data selection parameters are determined according to the corresponding relevant distance squares.

[0033] It should be noted that after obtaining the square of the correlation distance of each resource monitoring data point, it is necessary to arrange the square of the correlation distances of all resource monitoring data points in ascending order according to their sizes to obtain a square of correlation distance sequence.

[0034] In some embodiments, the relevant distance deviation value set is determined according to the relevant distance square corresponding to each resource monitoring data point in the following manner, namely: For each resource monitoring data point, the mean of the squared correlation distance is determined based on the squared correlation distance and the parameters selected from the initial data; Get the initial data and select the parameters; The squared variability of the correlation distance is determined according to the squared mean of the correlation distance and the parameters selected by the initial data. In specific implementation, the squared variability of the correlation distance can be determined according to the following formula: in, represents the first The squared correlation distance variability corresponding to the squared correlation distance is Indicates resource monitoring data points The square of the correlation distance, Indicates the resource monitoring data set Resource monitoring data points, represents the first The mean of the squared correlation distances corresponding to the squared correlation distances, Indicates the initial data selection parameters; The squared variation of the relevant distance is compared with a preset squared variation of the relevant distance to obtain the relevant distance deviation value of the resource monitoring data point, and then determine a relevant distance deviation value set.

[0035] It should be noted that, after determining the squared variability of the relevant distance of a resource monitoring data point, the squared variability of the relevant distance is subtracted from the preset squared variability of the relevant distance to obtain a difference, and the absolute value of the difference is taken as the relevant distance deviation value of the resource monitoring data point. In specific implementation, the preset squared variability of the relevant distance is generally determined according to the dimension of the resource monitoring data. For example, assuming that the dimension of the resource monitoring data is , then the preset correlation distance square variation value can be After obtaining the relevant distance deviation value corresponding to each resource monitoring data in the resource monitoring data set in the above manner, all relevant distance deviation values ​​are combined together in chronological order to form a relevant distance deviation value set.

[0036] In addition, it should be noted that by calculating the square of the relevant distance and determining the relevant distance deviation value, it can help optimize the selection of selected parameters of resource monitoring data. The relevant distance deviation value can reflect the relative deviation between each resource monitoring data point in the resource monitoring data set and the first resource monitoring data subset.

[0037] In some embodiments, the square of the relevant distance corresponding to the minimum relevant distance deviation value is further determined, and the resource monitoring data selection parameter is determined according to the corresponding square of the relevant distance. Specifically, the following method can be used, namely: Sort the relevant distance squares of all resource monitoring data to obtain a relevant distance square sequence; Determine the resource monitoring data point corresponding to the minimum correlation distance deviation value, and then determine the sequence position of the correlation distance square of the resource monitoring data point in the correlation distance square sequence; The resource monitoring data selection parameter is determined according to the sequence position and the initial data selection parameter. In specific implementation, the resource monitoring data selection parameter can be determined according to the following formula: in, Indicates the selected parameters of resource monitoring data. Indicates the initial data selection parameters, Represents the sequence position of the squared correlation distance in the squared correlation distance sequence.

[0038] It should be noted that by determining the relevant distance deviation value, the impact of abnormal data on the overall data analysis can be reduced. By considering the relevant distance deviation value, abnormal data can be better filtered, and the stability and reliability of resource monitoring data analysis can be improved. By determining the selected parameters of resource monitoring data through the relevant distance deviation value, unnecessary computing and storage overhead can be reduced, and the accuracy of resource monitoring data analysis can be improved.

[0039] In step 104, a second resource monitoring data subset is selected according to the resource monitoring data selection parameter, and a resource monitoring data related distance corresponding to each resource monitoring data point in the resource monitoring data set is determined through the second resource monitoring data subset.

[0040] In some embodiments, the second resource monitoring data subset may be selected according to the resource monitoring data selection parameter in the following manner, namely: Selecting resource monitoring data points in the resource monitoring data set whose number is equal to the selected parameter value of the resource monitoring data; The resource monitoring data points are combined into a second resource monitoring data subset in chronological order.

[0041] It should be noted that, by determining the second resource monitoring data subset through the above steps, the size of the data set can be reduced, thereby reducing the complexity of resource monitoring data processing and calculation.

[0042] In some embodiments, the resource monitoring data related distance corresponding to each resource monitoring data point in the resource monitoring data set may be determined by using the second resource monitoring data subset in the following manner, namely: determining a data mean vector and a data covariance matrix of a second resource monitoring data subset; Determine the resource monitoring data related distance corresponding to each resource monitoring data point in the resource monitoring data set according to the data mean vector and the data covariance matrix of the second resource monitoring data subset. In a specific implementation, the resource monitoring data related distance can be determined according to the following formula; in, Indicates resource monitoring data points The corresponding resource monitoring data related distance, Indicates the resource monitoring data set Resource monitoring data points, Indicates the second resource monitoring data subset The data mean vector, Indicates the second resource monitoring data subset The data covariance matrix is Represents a transposition operation. It should be noted that the resource monitoring data correlation distance is an indicator for measuring the difference between the resource monitoring data point in the resource monitoring data set and the second resource monitoring data subset. The smaller the resource monitoring data correlation distance, the smaller the difference between the resource monitoring data point in the resource monitoring data set and the second resource monitoring data subset. The larger the resource monitoring data correlation distance, the greater the difference between the resource monitoring data point in the resource monitoring data set and the second resource monitoring data subset. In addition, the larger the resource monitoring data correlation distance, the greater the possibility that the resource monitoring data point is abnormal.

[0043] In addition, it should be noted that by determining the relevant distance of the resource monitoring data, the similarities and differences between the resource monitoring data points in the resource monitoring data set and the second resource monitoring data subset can be found, which is helpful for detecting abnormal resource monitoring data points.

[0044] In step 105, the resource monitoring data points whose resource monitoring data correlation distance is higher than a preset critical value are used as resource usage abnormality information to issue an alarm.

[0045] In some embodiments, the resource monitoring data points whose resource monitoring data correlation distance is higher than a preset critical value are used as resource usage abnormality information for alarm. In specific implementation, the preset critical value is used to determine which resource monitoring data points have resource monitoring data correlation distances higher than the preset critical value. For resource monitoring data points whose resource monitoring data correlation distances are higher than the preset critical value, they are regarded as resource usage abnormalities and an alarm notification is triggered. The alarm notification should include specific information about the abnormality, such as the timestamp of the abnormal resource monitoring data point, the corresponding monitoring indicator notification and the specific value, so that operation and maintenance personnel can quickly locate the problem.

[0046] In some embodiments, the preset critical value is determined according to the selected parameter of the resource monitoring data. It should be noted that the resource monitoring data related distance is subject to the degree of freedom of the selected parameter of the resource monitoring data. The chi-square distribution of the value is , usually the confidence interval is set to 97.5%, then the preset critical value can be obtained , when the resource monitoring data point The resource monitoring data correlation distance of is higher than the preset critical value, that is, , then determine the resource monitoring data point Monitor data points for abnormal resources and use them as resource usage abnormality information for alarm, thereby improving the accuracy of abnormal resource usage alarms.

[0047] In the present application, firstly, by filling in missing values, the impact of missing values ​​in the resource monitoring data set on the resource monitoring data analysis is reduced; secondly, the square of the relevant distance corresponding to each resource monitoring data point in the resource monitoring data set is determined, which helps to identify abnormal resource usage; then, by determining the relevant distance deviation value, the impact of abnormal data on the overall data analysis can be reduced, and the accuracy of resource monitoring data analysis can be improved; finally, by determining and comparing the relevant distances of resource monitoring data, abnormal resource monitoring data points can be detected and alarms can be issued to solve the technical problem of low accuracy of abnormal resource usage alarms.

[0048] In addition, in another aspect of the present application, in some embodiments, the present application provides a monitoring system, the system also includes a resource abnormality alarm unit, referring to Figure 2 , which is a schematic diagram of exemplary hardware and / or software of a resource abnormality alarm unit according to some embodiments of the present application, the resource abnormality alarm unit 200 includes: a first resource monitoring data subset selection module 201, a correlation distance square determination module 202, a resource monitoring data selected parameter determination module 203, a resource monitoring data correlation distance determination module 204 and a resource abnormality alarm module 205, which are respectively described as follows: A first resource monitoring data subset selection module 201, in the present application, the first resource monitoring data subset selection module 201 is mainly used to collect resource monitoring data through the Prometheus monitoring system to obtain a resource monitoring data set, determine initial data selection parameters according to the resource monitoring data set, and select the first resource monitoring data subset according to the initial data selection parameters; A correlation square distance determination module 202, in the present application, the correlation square distance determination module 202 is mainly used to determine the correlation square distance corresponding to each resource monitoring data point in the resource monitoring data set according to the first resource monitoring data subset; Resource monitoring data selected parameter determination module 203, in the present application, resource monitoring data selected parameter determination module 203 is mainly used to determine the relevant distance deviation value according to the relevant distance square corresponding to each resource monitoring data point, and then determine the relevant distance square corresponding to the minimum relevant distance deviation value, and determine the resource monitoring data selected parameter according to the corresponding relevant distance square; The resource monitoring data related distance determination module 204 in the present application is mainly used to select a second resource monitoring data subset according to the resource monitoring data selection parameter, and determine the resource monitoring data related distance corresponding to each resource monitoring data point in the resource monitoring data set through the second resource monitoring data subset; The resource anomaly alarm module 205 in the present application is mainly used to use the resource monitoring data points whose related distance of the resource monitoring data is higher than the preset critical value as resource usage anomaly information for alarm.

[0049] The examples of the monitoring system and method provided by the embodiments of the present application are described in detail above. It can be understood that the corresponding device includes a hardware structure and / or software module corresponding to each function in order to realize the above functions. It should be easily appreciated by those skilled in the art that the present application can be implemented in the form of hardware or a combination of hardware and computer software in combination with the units and algorithm steps of each example described in the embodiments disclosed herein. Whether a function is executed in the form of hardware or computer software driving hardware 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 exceed the scope of the present application.

[0050] In some embodiments, the present application also provides a computer device, which includes a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned monitoring method.

[0051] In some embodiments, reference Figure 3 , the dotted line in the figure indicates that the unit or the module is optional, and the figure is a structural schematic diagram of a computer device according to a monitoring method provided by an embodiment of the present application. The above monitoring method in the above embodiment can be Figure 3 The computer device 300 shown in the figure is implemented, and the computer device 300 includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device 300 can be a terminal device, a server or a chip.

[0052] The processor 301 may be a general-purpose processor or a special-purpose processor. For example, the processor 301 may be a central processing unit (CPU), which may be used to control the computer device 300, execute software programs, and process data of the software programs. The computer device 300 may also include a communication unit 305 to implement signal input (reception) and output (transmission).

[0053] For example, the computer device 300 may be a chip, the communication unit 305 may be an input and / or output circuit of the chip, or the communication unit 305 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other devices.

[0054] For another example, the computer device 300 may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0055] The computer device 300 may include one or more memories 302, on which a program 304 is stored. The program 304 can be executed by the processor 301 to generate instructions 303, so that the processor 301 performs the method described in the above method embodiment according to the instructions 303. Optionally, data (such as a target audit model) may also be stored in the memory 302. Optionally, the processor 301 may also read the data stored in the memory 302, which may be stored at the same storage address as the program 304, or may be stored at a different storage address from the program 304.

[0056] The processor 301 and the memory 302 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of the terminal device.

[0057] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or a software-based instruction in the processor 301. The processor 301 can be a central processing unit, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, such as discrete gates, transistor logic devices or discrete hardware components.

[0058] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0059] For example, in some embodiments, the present application also provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned monitoring method when executing.

[0060] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0061] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A monitoring method, characterized in that: The steps include: Collect resource monitoring data through the Prometheus monitoring system to obtain a resource monitoring data set, determine initial data selection parameters according to the resource monitoring data set, and select a first resource monitoring data subset according to the initial data selection parameters; Determine, according to the first resource monitoring data subset, a square of a correlation distance corresponding to each resource monitoring data point in the resource monitoring data set; Determine a relevant distance deviation value set according to the relevant distance square corresponding to each resource monitoring data point, further determine the relevant distance square corresponding to the minimum relevant distance deviation value, and determine the resource monitoring data selection parameter according to the relevant distance square corresponding to the minimum relevant distance deviation value; Selecting a second resource monitoring data subset according to the resource monitoring data selection parameter, and determining the resource monitoring data related distance corresponding to each resource monitoring data point in the resource monitoring data set through the second resource monitoring data subset; Resource monitoring data points whose correlation distances are higher than preset critical values ​​are used as resource usage abnormality information for alarm.

2. The method according to claim 1, characterized in that The resource monitoring data is collected through the Prometheus monitoring system, and the resource monitoring data set includes: Determine the median value of resource monitoring data collected through the Prometheus monitoring system; The missing values ​​of the collected resource monitoring data are filled according to the data median of the resource monitoring data to obtain a resource monitoring data set.

3. The method according to claim 1, characterized in that Determining the initial data selection parameters according to the resource monitoring data set specifically includes: Determine the total amount of data of resource monitoring data points in the resource monitoring data set; Determine the data dimensions of the resource monitoring data points in the resource monitoring data set; The initial data selection parameter is determined according to the total amount of data of the resource monitoring data point in the resource monitoring data set and the data dimension of the resource monitoring data point, wherein the initial data selection parameter is determined according to the following formula: in, Indicates the initial data selection parameters, Indicates resource monitoring dataset The total amount of data of resource monitoring data points, Indicates resource monitoring dataset The data dimensions of the resource monitoring data points, Represents a resource monitoring dataset.

4. The method according to claim 1, characterized in that Determining the square of the relevant distance corresponding to each resource monitoring data point in the resource monitoring data set according to the first resource monitoring data subset specifically includes: Determining a data mean vector and a data covariance matrix of a first resource monitoring data subset; Using the data mean vector as a data position parameter of a first resource monitoring data subset; Using the data covariance matrix as a data scale parameter of a first resource monitoring data subset; Determine the square of the relevant distance corresponding to each resource monitoring data point in the resource monitoring data set according to the data position parameter and the data scale parameter of the first resource monitoring data subset, wherein the square of the relevant distance is determined according to the following formula; in, Indicates resource monitoring data points The corresponding correlation distance squared, Indicates the resource monitoring data set Resource monitoring data points, Indicates the first resource monitoring data subset The data position parameter, Indicates the first resource monitoring data subset The data scale parameter, Represents a transpose operation.

5. The method according to claim 1, characterized in that Determining the square of the relevant distance corresponding to the minimum relevant distance deviation value, and determining the resource monitoring data selection parameter according to the square of the relevant distance corresponding to the minimum relevant distance deviation value specifically includes: Sort the relevant distance squares of all resource monitoring data to obtain a relevant distance square sequence; Determine the resource monitoring data point corresponding to the minimum correlation distance deviation value, and then determine the sequence position of the correlation distance square corresponding to the resource monitoring data point in the correlation distance square sequence; The resource monitoring data selection parameters are determined according to the sequence position and the initial data selection parameters.

6. The method according to claim 1, characterized in that Selecting the second resource monitoring data subset according to the resource monitoring data selection parameter specifically includes: Selecting resource monitoring data points in the resource monitoring data set whose number is equal to the selected parameter value of the resource monitoring data; The resource monitoring data points are combined into a second resource monitoring data subset in chronological order.

7. The method according to claim 1, characterized in that The preset critical value is determined according to the selected parameters of the resource monitoring data.

8. A monitoring system, characterized in that: A resource abnormality alarm unit is included, and the resource abnormality alarm unit includes: A first resource monitoring data subset selection module, used to collect resource monitoring data through the Prometheus monitoring system to obtain a resource monitoring data set, determine initial data selection parameters according to the resource monitoring data set, and select a first resource monitoring data subset according to the initial data selection parameters; A correlation distance square determination module, configured to determine a correlation distance square corresponding to each resource monitoring data point in the resource monitoring data set according to the first resource monitoring data subset; A resource monitoring data selected parameter determination module, used to determine a relevant distance deviation value set according to the relevant distance square corresponding to each resource monitoring data point, and then determine the relevant distance square corresponding to the minimum relevant distance deviation value, and determine the resource monitoring data selected parameter according to the corresponding relevant distance square; A resource monitoring data related distance determination module, configured to select a second resource monitoring data subset according to the resource monitoring data selection parameter, and determine the resource monitoring data related distance corresponding to each resource monitoring data point in the resource monitoring data set through the second resource monitoring data subset; The resource abnormality alarm module is used to use the resource monitoring data points whose relevant distance of the resource monitoring data is higher than a preset critical value as resource usage abnormality information to issue an alarm.

9. A computer device, characterized in that: The computer device comprises a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the monitoring method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions or codes, and when the instructions or codes are executed on a computer, the computer implements the monitoring method according to any one of claims 1 to 7.

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