CN112799911A - Node health state detection method, device, equipment and storage medium - Google Patents
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Abstract
The application discloses a node health state detection method, which comprises the following steps: acquiring current operation state data of a target node; inputting the current running state data of the target node into a Bayes detection model obtained by pre-training to obtain an output result of the Bayes detection model; determining the health state of the target node according to the output result; the Bayesian detection model is obtained by training based on historical running state data of a plurality of nodes and corresponding health state data. By applying the technical scheme provided by the application, the health state of the node can be accurately detected, corresponding measures can be taken in time according to the health state of the node for processing, and the stable operation of the cluster service is guaranteed. The application also discloses a node health state detection device, equipment and a storage medium, and the device and the equipment have corresponding technical effects.
Description
Technical Field
The present application relates to the field of computer application technologies, and in particular, to a method, an apparatus, a device, and a storage medium for detecting a node health status.
Background
With the rapid development of computer technology, various clusters, such as storage clusters, computing clusters, etc., are increasingly widely used. The cluster can be composed of a plurality of nodes, and some problems occur inevitably in the running process of the nodes, so that the nodes are in a sub-health state. The node in the sub-health state is more prone to failure, and if measures such as repair are taken on the node under the condition that the node has failed, normal operation of the cluster service is affected.
Therefore, how to accurately detect the health state of the node is a technical problem which needs to be solved urgently by those skilled in the art at present.
Disclosure of Invention
The application aims to provide a node health state detection method, a node health state detection device and a node health state detection storage medium, so that the health state of a node can be accurately detected, and stable operation of a cluster service can be guaranteed.
In order to solve the technical problem, the application provides the following technical scheme:
a node health state detection method comprises the following steps:
acquiring current operation state data of a target node;
inputting the current running state data of the target node into a Bayes detection model obtained by pre-training to obtain an output result of the Bayes detection model;
determining the health state of the target node according to the output result;
the Bayesian detection model is obtained by training based on historical operating state data of a plurality of nodes and corresponding health state data.
In one embodiment of the present application, the bayesian detection model is obtained by training:
acquiring historical operating state data and corresponding health state data of a plurality of nodes, wherein each group of historical operating state data comprises a plurality of mutually independent dimensional features, and each dimensional feature comprises a plurality of feature values corresponding to time points;
calculating related probabilities according to the historical operating state data of the nodes and the corresponding health state data and the Bayesian principle, wherein the related probabilities comprise the probability of each feature appearing in the health state or the sub-health state, the probability of the sub-health state and the probability of each feature;
and training a pre-constructed Bayes initial model based on the correlation probability to obtain the Bayes detection model.
In one embodiment of the present application, the current operating state data and the historical operating state data have features of the same dimension, and the features of each dimension have feature values of the same length.
In one embodiment of the present application, the characteristics of the multiple dimensions in the current operating state data and the historical operating state data include one or more of the following characteristics: memory consumption, disk wear, CPU utilization, disk utilization, and CPU temperature.
In a specific embodiment of the present application, the output result is: determining the health state of the target node according to the output result under the sub-health probability of the current operation state data, including:
and if the output sub-health probability is larger than a preset probability threshold, determining that the target node is in a sub-health state.
In a specific embodiment of the present application, the current operating state data includes a plurality of groups, and the output result is: determining the health state of the target node according to the output result at the sub-health probability of each group of current operating state data, including:
and if the sub-health probability output correspondingly to at least one group of current running state data is larger than a preset probability threshold, determining that the target node is in a sub-health state.
In one embodiment of the present application, after determining that the target node is in a sub-health state, the method further includes:
and eliminating the target node in the cluster.
A node health status detection apparatus comprising:
the operation state data acquisition module is used for acquiring the current operation state data of the target node;
the output result obtaining module is used for inputting the current operation state data of the target node into a Bayes detection model obtained by pre-training to obtain the output result of the Bayes detection model;
the health state determining module is used for determining the health state of the target node according to the output result;
the Bayesian detection model is obtained by training based on historical operating state data of a plurality of nodes and corresponding health state data.
A node health status detection apparatus comprising:
a memory for storing a computer program;
a processor for implementing the steps of any of the above node health status detection methods when executing the computer program.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the node health status detection method of any one of the above.
By applying the technical scheme provided by the embodiment of the application, the Bayesian detection model is obtained by training based on historical operating state data of a plurality of nodes and corresponding health state data, the current operating state data of the target node is obtained and then input into the Bayesian detection model obtained by pre-training, corresponding output results are obtained, and the health state of the target node is determined according to the output results. The health state of the node is accurately detected based on the Bayesian detection model and the current running state data of the node, corresponding measures can be taken in time aiming at the health state of the node for processing, and the stable running of the cluster service is guaranteed.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart illustrating an implementation of a node health status detection method in an embodiment of the present application;
fig. 2 is a schematic structural diagram of a node health status detection apparatus according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a node health status detection device in an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the disclosure, the following detailed description will be given with reference to the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, an implementation flowchart of a node health status detection method provided in the embodiment of the present application is shown, where the method may include the following steps:
s110: and acquiring current operation state data of the target node.
The target node may be any one of the nodes in the cluster. That is to say, the scheme of the embodiment of the present application may be applied to any node in the cluster to perform health status detection on the node.
The target node is monitored, and the current operation state data of the target node can be obtained. The current operating state data may include a plurality of mutually independent dimensional features, which may include one or more of the following features: memory consumption, disk wear, CPU utilization, disk utilization, and CPU temperature, although other health related characteristics may also be included. The feature for each dimension may include a feature value corresponding to a respective time point. For example, data is collected every minute in the last hour, and then in this hour, there are 60 feature values corresponding to each feature, and the current operation state data of the target node is formed.
S120: and inputting the current running state data of the target node into a Bayes detection model obtained by pre-training to obtain an output result of the Bayes detection model.
The Bayesian detection model is obtained by training based on historical operating state data of a plurality of nodes and corresponding health state data.
In one embodiment of the present application, the bayesian detection model can be obtained by training through the following steps:
the method comprises the following steps: acquiring historical operating state data and corresponding health state data of a plurality of nodes, wherein each group of historical operating state data comprises a plurality of mutually independent dimensional features, and each dimensional feature comprises a plurality of feature values corresponding to time points;
step two: calculating related probabilities according to historical operating state data of the nodes and corresponding health state data and a Bayesian principle, wherein the related probabilities comprise the probability of each feature appearing in a health state or a sub-health state, the probability of the sub-health state and the probability of each feature;
step three: and training the pre-constructed Bayes initial model based on the correlation probability to obtain a Bayes detection model.
For convenience of description, the above three steps are combined for illustration.
Historical operating state data and corresponding health state data of a plurality of nodes can be obtained in advance and used as training sample data. The historical operating state data of the nodes can be in multiple groups, each group of the historical operating state data can comprise characteristics of multiple mutually independent dimensions, and the characteristics of each dimension comprise characteristic values corresponding to multiple time points.
For example, two sets of historical operating state data of the node a may be acquired by monitoring or the like, where one set of historical operating state data is acquired every other minute within one hour, and the other set of historical operating state data is acquired every other minute within another hour. Each group of historical operating state data comprises characteristics of five mutually independent dimensions of memory consumption, disk wear, CPU utilization rate, disk utilization rate and CPU temperature, the characteristics of each dimension comprise 60 characteristic values, each group of historical operating state data corresponds to health state data, and the health state data can be labels representing node health or sub-health states and the like.
The correlation probability can be calculated according to historical operating state data of a plurality of nodes and corresponding health state data and according to a Bayesian principle. The associated probabilities may include a probability of each feature occurring in a healthy state or a sub-healthy state, a probability of a sub-healthy state, a probability of each feature.
Each set of historical operating state data includes features of each dimension which can be individually represented as Ai＝[a1a2a3···an]Wherein A isiFeatures representing the ith dimension, [ a ]1a2a3···an]Representing feature values taken at different times of a time period, the 5-dimensional features being [ A ]1A2A3···An]. The health state of a node may be denoted as Cj(j equals 1 indicates a healthy state, and j equals 2 indicates a sub-healthy state).
Because the features of multiple dimensions are mutually independent, according to the Bayes principle, the probability of each feature appearing in the healthy state or the sub-healthy state, the probability of the sub-healthy state, the probability of each feature and the like can be obtained, and then P (A) can be obtained1|Cj)、P(A2|Cj)、P(A3|Cj)、P(A4|Cj)、P(A5|Cj)、P(Cj)、P(A1)、P(A2)、P(A3)、P(A4)、P(A5)、P(Cj)。
The bayesian formula is shown below:
based on the correlation probability, training a pre-constructed Bayes initial model to obtain a Bayes detection model, wherein the output result of the Bayes detection model is the probability of the health or sub-health state under the operation state data.
After the current operation state data of the target node is acquired, the current operation state data of the target node is input into a Bayes detection model obtained through pre-training, and the output result of the Bayes detection model can be obtained through the operation of the Bayes detection model.
S130: and determining the health state of the target node according to the output result.
After the current operation state data of the target node is input into the Bayes detection model, the output result of the Bayes detection model can be obtained, and the health state of the target node can be determined according to the output result.
In a specific embodiment of the present application, the output result of the bayesian detection model is: and if the output sub-health probability is greater than a preset probability threshold value under the sub-health probability of the current running state data, determining that the target node is in the sub-health state.
A probability threshold may be preset, if the output result of the bayesian detection model is a sub-health state in the current operating state data, it may be determined that the target node is in the sub-health state when the output sub-health probability is greater than the probability threshold, and conversely, it may be determined that the target node is in the health state when the output sub-health probability is less than or equal to the probability threshold. The probability threshold can be set and adjusted according to actual conditions.
In another specific embodiment of the present application, the current operating state data is a plurality of groups, and the output result is: and if the sub-health probability output by the at least one group of current operation state data is larger than the probability threshold, determining that the target node is in the sub-health state.
In the embodiment of the present application, multiple sets of current operating state data of the target node may be obtained, for example, a set of current operating state data is obtained in different one hour, and a characteristic value is collected every other minute in each hour. After obtaining multiple sets of current operating state data of the target node, each set of current operating state data can be respectively input into the bayesian detection model, and an output result of the bayesian detection model corresponding to each set of current operating state data is obtained. The sub-health probabilities output by different groups of current operation state data correspondingly may be different, and if the sub-health probability output by at least one group of current operation state data correspondingly is larger than a preset probability threshold, it may be determined that the target node is in the sub-health state. That is, as long as there is a group of current operation state data corresponding to the output sub-health probability greater than the probability threshold, the target node is considered to be in the sub-health state, and if the sub-health probability corresponding to the output of each group of current operation state data is less than or equal to the probability threshold, the target node can be determined to be in the health state.
It should be noted that the output result of the bayesian detection model can also be set as the health probability under the current operating state data according to the actual situation, if the health probability is greater than the preset probability threshold, the target node can be determined to be in the healthy state, otherwise, the target node is determined to be in the sub-healthy state.
In addition, it should be noted that, in the embodiment of the present application, the current operating state data of the target node and the historical operating state data used for training the bayesian detection model have features with the same dimension, and the features of each dimension have feature values with the same length, so that the bayesian detection model is better utilized to perform health state detection, and errors of the model are avoided. The characteristics of the multiple dimensions in the current operating state data and the historical operating state data may include one or more of memory consumption, disk wear, CPU utilization, disk utilization, and CPU temperature.
By applying the method provided by the embodiment of the application, the Bayesian detection model is obtained by training based on historical operating state data of a plurality of nodes and corresponding health state data, the current operating state data of the target node is obtained and then input into the Bayesian detection model obtained by pre-training, corresponding output results are obtained, and the health state of the target node is determined according to the output results. The health state of the node is accurately detected based on the Bayesian detection model and the current running state data of the node, corresponding measures can be taken in time aiming at the health state of the node for processing, and the stable running of the cluster service is guaranteed.
In one embodiment of the present application, after determining that the target node is in a sub-healthy state, the target node may also be culled in the cluster. Because the probability of the node in the sub-health state being in the fault state is high, once the node is in the fault state, the service is interrupted, and the normal operation of the cluster service is influenced, so that the target node is removed from the cluster when the target node is detected to be in the sub-health state, and the stability and the reliability of the cluster during the service can be ensured.
Corresponding to the above method embodiments, the present application further provides a node health status detection apparatus, and the node health status detection apparatus described below and the node health status detection method described above may be referred to in correspondence.
Referring to fig. 2, the apparatus may include the following modules:
an operation status data obtaining module 210, configured to obtain current operation status data of a target node;
an output result obtaining module 220, configured to input the current operating state data of the target node into a bayesian detection model obtained through pre-training, and obtain an output result of the bayesian detection model;
a health status determination module 230, configured to determine a health status of the target node according to the output result;
the Bayesian detection model is obtained by training based on historical running state data of a plurality of nodes and corresponding health state data.
By applying the device provided by the embodiment of the application, the Bayesian detection model is obtained by training based on historical operating state data of a plurality of nodes and corresponding health state data, the current operating state data of the target node is obtained and then input into the Bayesian detection model obtained by pre-training, corresponding output results are obtained, and the health state of the target node is determined according to the output results. The health state of the node is accurately detected based on the Bayesian detection model and the current running state data of the node, corresponding measures can be taken in time aiming at the health state of the node for processing, and the stable running of the cluster service is guaranteed.
In a specific embodiment of the present application, the system further includes a bayesian detection model training module, configured to obtain the bayesian detection model through the following training steps:
acquiring historical operating state data and corresponding health state data of a plurality of nodes, wherein each group of historical operating state data comprises a plurality of mutually independent dimensional features, and each dimensional feature comprises a plurality of feature values corresponding to time points;
calculating related probabilities according to historical operating state data of the nodes and corresponding health state data and a Bayesian principle, wherein the related probabilities comprise the probability of each feature appearing in a health state or a sub-health state, the probability of the sub-health state and the probability of each feature;
and training the pre-constructed Bayes initial model based on the correlation probability to obtain a Bayes detection model.
In one embodiment of the present application, the current operating state data and the historical operating state data have features of the same dimension, and the features of each dimension have feature values of the same length.
In one embodiment of the present application, the characteristics of the multiple dimensions in the current operating state data and the historical operating state data include one or more of the following characteristics: memory consumption, disk wear, CPU utilization, disk utilization, and CPU temperature.
In one embodiment of the present application, the output result is: a sub-health probability under the current operating state data, a health state determination module 230 to:
and if the output sub-health probability is larger than a preset probability threshold, determining that the target node is in a sub-health state.
In a specific embodiment of the present application, the current operating status data is a plurality of sets, and the output result is: a health status determination module 230, configured to, for each set of current operating state data, determine a sub-health probability, wherein:
and if the sub-health probability output corresponding to at least one group of current operation state data is larger than a preset probability threshold, determining that the target node is in the sub-health state.
In a specific embodiment of the present application, the system further includes a node culling module, configured to:
after determining that the target node is in a sub-health state, removing the target node from the cluster.
Corresponding to the above method embodiment, an embodiment of the present application further provides a node health status detection device, including:
a memory for storing a computer program;
and the processor is used for realizing the steps of the node health state detection method when executing the computer program.
As shown in fig. 3, which is a schematic view of a structure of a node health status detection device, the node health status detection device may include: a processor 10, a memory 11, a communication interface 12 and a communication bus 13. The processor 10, the memory 11 and the communication interface 12 all communicate with each other through a communication bus 13.
In the embodiment of the present application, the processor 10 may be a Central Processing Unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic device, etc.
The processor 10 may call a program stored in the memory 11, and in particular, the processor 10 may perform operations in an embodiment of the node health status detection method.
The memory 11 is used for storing one or more programs, the program may include program codes, the program codes include computer operation instructions, in this embodiment, the memory 11 stores at least the program for implementing the following functions:
acquiring current operation state data of a target node;
inputting the current running state data of the target node into a Bayes detection model obtained by pre-training to obtain an output result of the Bayes detection model;
determining the health state of the target node according to the output result;
the Bayesian detection model is obtained by training based on historical running state data of a plurality of nodes and corresponding health state data.
In one possible implementation, the memory 11 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a node monitoring function, a model training function), and the like; the storage data area may store data created during use, such as operating state data, test model data, and the like.
Further, the memory 11 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device or other volatile solid state storage device.
The communication interface 12 may be an interface of a communication module for connecting with other devices or systems.
Of course, it should be noted that the structure shown in fig. 3 does not constitute a limitation to the node health status detection device in the embodiment of the present application, and in practical applications, the node health status detection device may include more or less components than those shown in fig. 3, or some components in combination.
Corresponding to the above method embodiments, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the node health status detection method are implemented.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The principle and the implementation of the present application are explained in the present application by using specific examples, and the above description of the embodiments is only used to help understanding the technical solution and the core idea of the present application. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
Claims (10)
1. A node health state detection method is characterized by comprising the following steps:
acquiring current operation state data of a target node;
inputting the current running state data of the target node into a Bayes detection model obtained by pre-training to obtain an output result of the Bayes detection model;
determining the health state of the target node according to the output result;
the Bayesian detection model is obtained by training based on historical operating state data of a plurality of nodes and corresponding health state data.
2. The method of claim 1, wherein the bayesian detection model is obtained by training:
acquiring historical operating state data and corresponding health state data of a plurality of nodes, wherein each group of historical operating state data comprises a plurality of mutually independent dimensional features, and each dimensional feature comprises a plurality of feature values corresponding to time points;
calculating related probabilities according to the historical operating state data of the nodes and the corresponding health state data and the Bayesian principle, wherein the related probabilities comprise the probability of each feature appearing in the health state or the sub-health state, the probability of the sub-health state and the probability of each feature;
and training a pre-constructed Bayes initial model based on the correlation probability to obtain the Bayes detection model.
3. The method of claim 1, wherein the current operating state data and the historical operating state data have features of the same dimension, the features of each dimension having feature values of the same length.
4. The method of claim 3, wherein the characteristics of the multiple dimensions in the current operating state data and the historical operating state data include one or more of the following characteristics: memory consumption, disk wear, CPU utilization, disk utilization, and CPU temperature.
5. The method of claim 1, wherein the output result is: determining the health state of the target node according to the output result under the sub-health probability of the current operation state data, including:
and if the output sub-health probability is larger than a preset probability threshold, determining that the target node is in a sub-health state.
6. The method of claim 1, wherein the current operating state data is a plurality of sets, and the output result is: determining the health state of the target node according to the output result at the sub-health probability of each group of current operating state data, including:
and if the sub-health probability output correspondingly to at least one group of current running state data is larger than a preset probability threshold, determining that the target node is in a sub-health state.
7. The method of any one of claims 1 to 6, further comprising, after determining that the target node is in a sub-healthy state:
and eliminating the target node in the cluster.
8. A node health status detection apparatus, comprising:
the operation state data acquisition module is used for acquiring the current operation state data of the target node;
the output result obtaining module is used for inputting the current operation state data of the target node into a Bayes detection model obtained by pre-training to obtain the output result of the Bayes detection model;
the health state determining module is used for determining the health state of the target node according to the output result;
the Bayesian detection model is obtained by training based on historical operating state data of a plurality of nodes and corresponding health state data.
9. A node health status detection apparatus, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the node health status detection method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the node health status detection method according to any one of claims 1 to 7.
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| CN116501027B (en) * | 2023-06-29 | 2023-10-03 | 中南大学 | Distributed braking system health assessment method, system, equipment and storage medium |
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