CN112559291A - Resource monitoring method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application discloses a resource monitoring method, a resource monitoring device, an electronic device and a computer readable storage medium, wherein the method comprises the following steps: acquiring state data of a target resource, and calculating a performance health value of the target resource based on the state data; determining a target category to which the target resource belongs, and calculating the health degree of the target category according to the state data of all resources under the target category; and calculating the health degree of the target resource based on the performance health value of the target resource and the health degree of the target category so as to monitor the target resource. Therefore, the resource monitoring method provided by the application not only focuses on the running state of the resource, but also focuses on the running state of a certain type of resource, and accuracy of calculating the health degree of the resource is improved.
Description
Technical Field
The present application relates to the technical field of cloud platforms, and more particularly, to a resource monitoring method and apparatus, an electronic device, and a computer-readable storage medium.
Background
The cloud management platform manages a large number of cloud resources such as cloud hosts and cloud storage, the operation condition of each resource and the operation condition of the whole cloud management platform are data which need to be mastered by an administrator, conventional resource management usually focuses on the operation condition related to specific resources, but ignores the group-based property of the resources, and the accuracy of the calculated resource health degree is poor.
Therefore, how to improve the accuracy of the health of the computing resources is a technical problem to be solved by the computing personnel in the field.
Disclosure of Invention
The application aims to provide a resource monitoring method and device, an electronic device and a computer readable storage medium, and accuracy of calculating resource health degree is improved.
In order to achieve the above object, the present application provides a resource monitoring method, including:
acquiring state data of a target resource, and calculating a performance health value of the target resource based on the state data;
determining a target category to which the target resource belongs, and calculating the health degree of the target category according to the state data of all resources under the target category;
and calculating the health degree of the target resource based on the performance health value of the target resource and the health degree of the target category so as to monitor the target resource.
Wherein, calculating the health degree of the target category according to the state data of all the resources under the target category comprises:
determining the total number of resources of all resources in the target category;
determining the number of normal resources in the target category according to the state data of all the resources in the target category;
and taking the ratio of the number of the normal resources to the total number of the resources as the health degree of the target category.
Wherein calculating the health of the target resource based on the performance health value of the target resource and the health of the target category comprises:
taking the product of the performance health value of the target resource and the health degree of the target category as the health degree of the target resource.
Wherein determining the target class to which the target resource belongs comprises:
initializing the class labels of all resources, and calculating the similarity between the class labels of every two resources to construct an NxN weight matrix; wherein N is the total number of resources of all the resources;
and updating the class label of the target resource by utilizing a label propagation algorithm based on the weight matrix, and determining the target class to which the target resource belongs based on the updated class label.
Updating the category label of the target resource by using a label propagation algorithm based on the weight matrix, wherein the updating comprises the following steps:
determining a neighboring resource of the target resource based on the weight matrix;
updating the class label of the target resource based on the class label of the adjacent resource by using a label propagation algorithm.
Wherein determining the neighboring resource of the target resource based on the weight matrix comprises:
and determining the resource with the similarity between the class label and the class label of the target resource larger than a preset value as the adjacent resource of the target resource.
Wherein the category label comprises RGB values, the method further comprising:
initializing preset color labels, and determining a preset RGB value corresponding to each preset color label;
correspondingly, determining the target category to which the target resource belongs based on the updated category label includes:
determining a target preset RGB value closest to the RGB value after the target resource is updated in all the preset RGB values;
and determining a target preset color label corresponding to the target preset RGB value, and taking a category corresponding to the target preset color label as a target category to which the target resource belongs.
In order to achieve the above object, the present application provides a resource monitoring apparatus, including:
the acquisition module is used for acquiring state data of the target resource and calculating a performance health value of the target resource based on the state data;
the determining module is used for determining a target category to which the target resource belongs and calculating the health degree of the target category according to the state data of all resources under the target category;
and the calculating module is used for calculating the health degree of the target resource based on the performance health value of the target resource and the health degree of the target category so as to monitor the target resource.
To achieve the above object, the present application provides an electronic device including:
a memory for storing a computer program;
a processor for implementing the steps of the resource monitoring method when executing the computer program.
To achieve the above object, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when being executed by a processor, implements the steps of the above-mentioned resource monitoring method.
According to the scheme, the resource monitoring method provided by the application comprises the following steps: acquiring state data of a target resource, and calculating a performance health value of the target resource based on the state data; determining a target category to which the target resource belongs, and calculating the health degree of the target category according to the state data of all resources under the target category; and calculating the health degree of the target resource based on the performance health value of the target resource and the health degree of the target category so as to monitor the target resource.
In the present application, when calculating the resource health degree, not only the health condition of a single resource but also the resource health condition of the category to which the resource belongs are considered, that is, the overall resource health condition of the category to which the resource belongs is subjected to additive calculation. Therefore, the resource monitoring method provided by the application not only focuses on the running state of the resource, but also focuses on the running state of a certain type of resource, and accuracy of calculating the health degree of the resource is improved. The application also discloses a resource monitoring device, an electronic device and a computer readable storage medium, which can also realize the technical effects.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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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. The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
FIG. 1 is a flow diagram illustrating a method of resource monitoring in accordance with an exemplary embodiment;
FIG. 2 is a flow diagram illustrating another method of resource monitoring in accordance with an exemplary embodiment;
FIG. 3 is a block diagram illustrating a resource monitoring device in accordance with an exemplary embodiment;
FIG. 4 is a block diagram illustrating an electronic device in accordance with an exemplary embodiment.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. 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. In addition, in the embodiments of the present application, "first", "second", and the like are used for distinguishing similar objects, and are not necessarily used for describing a specific order or a sequential order.
The embodiment of the application discloses a resource monitoring method, which improves the accuracy of calculating the health degree of resources.
Referring to fig. 1, a flowchart of a resource monitoring method according to an exemplary embodiment is shown, as shown in fig. 1, including:
s101: acquiring state data of a target resource, and calculating a performance health value of the target resource based on the state data;
the embodiment aims to calculate the health degree of target resources in the cloud management platform, and can be applied to cloud management platforms with architectures such as arm and mpis. In this step, the state of the resource of the cloud management platform may be monitored based on prochimus, the state data of the resource may be recorded, and the state data of the resource may be queried in real time. After the state data of the target resource is acquired, the performance health value of the target resource can be calculated accordingly. The specific calculation method is not limited here, and all schemes for calculating the resource health degree in the related art are within the protection scope of the present embodiment.
S102: determining a target category to which the target resource belongs, and calculating the health degree of the target category according to the state data of all resources under the target category;
in this embodiment, when calculating the resource health degree, not only the health condition of a single resource but also the resource health condition of the category to which the resource belongs are considered, that is, the overall resource health condition of the category to which the resource belongs is subjected to the addition calculation.
Therefore, in this step, a target class to which the target resource belongs needs to be determined, the classes in the cloud management platform may include a cloud host, a cloud storage, a cloud hard disk, and the like, and the cloud hosts of different current operating systems may also be divided into different resource classes, which is not specifically limited herein.
Secondly, calculating the health degree of the target category according to the state data of all the resources under the target category, which may specifically include: determining the total number of resources of all resources in the target category; determining the number of normal resources in the target category according to the state data of all the resources in the target category; and taking the ratio of the number of the normal resources to the total number of the resources as the health degree of the target category.
S103: and calculating the health degree of the target resource based on the performance health value of the target resource and the health degree of the target category so as to monitor the target resource.
The purpose of this step is to calculate the health degree of the target resource based on the performance health value of the target resource and the health degree of the target class, and in a specific implementation, the product of the performance health value of the target resource and the health degree of the target class may be used as the health degree of the target resource. Therefore, if the health status of the target category is good, the resource health degree mainly depends on the performance index of the resource, and if the community resource health degree is poor, the health condition of the resource is worse, and the health condition is reflected to the overall health condition of the cloud management platform, namely when the resource of a certain community has group abnormality, the health degree of the whole cloud management platform is obviously reduced.
In the embodiment of the present application, when calculating the health degree of a resource, not only the health condition of a single resource but also the health condition of the resource of the category to which the resource belongs are considered, that is, the health condition of the whole resource of the category to which the resource belongs is subjected to addition calculation. Therefore, the resource monitoring method provided by the embodiment of the application not only focuses on the running state of the resource, but also focuses on the running state of a certain type of resource, and the accuracy of calculating the health degree of the resource is improved.
The embodiment of the application discloses a resource monitoring method, and compared with the previous embodiment, the embodiment further explains and optimizes the technical scheme. Specifically, the method comprises the following steps:
referring to fig. 2, a flowchart of another resource monitoring method according to an exemplary embodiment is shown, as shown in fig. 2, including:
s201: acquiring state data of a target resource, and calculating a performance health value of the target resource based on the state data;
s202: initializing the class labels of all resources, and calculating the similarity between the class labels of every two resources to construct an NxN weight matrix; wherein N is the total number of resources of all the resources;
s203: updating the class label of the target resource by using a label propagation algorithm based on the weight matrix, and determining the target class to which the target resource belongs based on the updated class label;
in this embodiment, the resource categories are initialized through a tag propagation algorithm, all resources in the cloud management platform are automatically classified, the resource health conditions under all categories are counted to calculate the category health degree, and the category health degree and the performance health value of the resources are added to finally obtain the overall operation condition of the cloud management platform.
When creating resources, assigning a class label to the resources, and constructing a weight matrix according to the similarity between the class labels of every two resources, wherein the weight matrix is an N multiplied by N matrix, and N is the total number of the resources of all the resources, namely constructing a fully-connected resource network.
The label propagation algorithm is a graph-based semi-supervised learning algorithm, and the basic idea is to predict unlabelled node label information from the labeled node label information, establish a complete graph model by using the relation among samples, and be suitable for an undirected graph. And each node label is propagated to the adjacent nodes according to the similarity, each node updates the label of the node according to the label of the adjacent node in each step of node propagation, the greater the similarity with the node is, the greater the influence weight value of the adjacent node on the label is, the more the labels of the similar nodes tend to be consistent, and the easier the label is to be propagated. During the label propagation process, the label of the marked data is kept unchanged, so that the label is transmitted to the unmarked data. Finally, when the iteration is finished, the probability distributions of the similar nodes tend to be similar and can be divided into a class.
As can be seen, the class to which each resource belongs may be determined by using a label propagation algorithm, that is, the class label of the target resource is updated by using a label propagation algorithm based on the weight matrix, including: determining a neighboring resource of the target resource based on the weight matrix; updating the class label of the target resource based on the class label of the adjacent resource by using a label propagation algorithm. Determining neighboring resources of the target resource based on the weight matrix, including: and determining the resource with the similarity between the class label and the class label of the target resource larger than a preset value as the adjacent resource of the target resource.
As a preferred embodiment, the category label includes RGB values, and the method further includes: initializing preset color labels, and determining a preset RGB value corresponding to each preset color label; correspondingly, determining the target category to which the target resource belongs based on the updated category label includes: determining a target preset RGB value closest to the RGB value after the target resource is updated in all the preset RGB values; and determining a target preset color label corresponding to the target preset RGB value, and taking a category corresponding to the target preset color label as a target category to which the target resource belongs.
In a specific implementation, each label has a color attribute, i.e., each label has an RGB value, provided that labels with more similar RGB values are more likely to belong to the same category. Meanwhile, red, orange, yellow, green, blue, indigo and purple are set as 7 initialization color labels, the initialization color labels can be flexibly set by a person skilled in the art according to actual conditions, each initialization color label corresponds to a preset RGB value, the initialization color labels are manually marked to specific resources, and the RGB values of the other resources can be set at will. And constructing a resource matrix based on all resources, and setting the weights of two nodes according to the incidence relation of the RGB attributes of the two resources to construct an NxN weight matrix. And calculating by means of a label propagation algorithm, and classifying the resources according to the color labels to determine the target class to which the target resource belongs.
S204: calculating the health degree of the target category according to the state data of all the resources under the target category;
s205: and calculating the health degree of the target resource based on the performance health value of the target resource and the health degree of the target category so as to monitor the target resource.
Therefore, the community discovery idea in machine learning is used for reference, and the resources are classified by using the label propagation algorithm. And each resource does not need to be accurately labeled manually, and the resource can be automatically attributed to the basic category according to the label of the resource. If the resources have community attributes, the health condition of the resources is brought into the calculation of the health condition of the resources, and the overall health degree of the system is reflected that the resources of a certain community have group abnormal events, so that the resource monitoring accuracy is improved.
In the following, a resource monitoring apparatus provided in an embodiment of the present application is introduced, and a resource monitoring apparatus described below and a resource monitoring method described above may be referred to each other.
Referring to fig. 3, a block diagram of a resource monitoring device according to an exemplary embodiment is shown, as shown in fig. 3, including:
an obtaining module 301, configured to obtain state data of a target resource, and calculate a performance health value of the target resource based on the state data;
a determining module 302, configured to determine a target category to which the target resource belongs, and calculate a health degree of the target category according to state data of all resources in the target category;
a calculating module 303, configured to calculate a health degree of the target resource based on the performance health value of the target resource and the health degree of the target category, so as to monitor the target resource.
In the embodiment of the present application, when calculating the health degree of a resource, not only the health condition of a single resource but also the health condition of the resource of the category to which the resource belongs are considered, that is, the health condition of the whole resource of the category to which the resource belongs is subjected to addition calculation. Therefore, the resource monitoring device provided by the embodiment of the application pays attention to the running state of the resource and also pays attention to the running state of a certain type of resource, and the accuracy of calculating the health degree of the resource is improved.
On the basis of the foregoing embodiment, as a preferred implementation manner, the determining module 302 includes:
the first determining submodule is used for determining a target category to which the target resource belongs;
the second determining submodule is used for determining the total number of resources of all the resources under the target category;
a third determining submodule, configured to determine, according to the state data of all resources in the target category, the number of normal resources in the target category;
and the calculating submodule is used for calculating the ratio of the number of the normal resources to the total number of the resources as the health degree of the target category.
On the basis of the foregoing embodiment, as a preferred implementation manner, the calculating module 303 is specifically a module that takes a product of the performance health value of the target resource and the health degree of the target category as the health degree of the target resource, so as to monitor the target resource.
On the basis of the foregoing embodiment, as a preferred implementation, the first determining sub-module includes:
the computing unit is used for initializing the class labels of all the resources and computing the similarity between the class labels of every two resources so as to construct an NxN weight matrix; wherein N is the total number of resources of all the resources;
and the updating unit is used for updating the class label of the target resource by utilizing a label propagation algorithm based on the weight matrix and determining the target class to which the target resource belongs based on the updated class label.
On the basis of the foregoing embodiment, as a preferred implementation, the updating unit includes:
a first determining subunit, configured to determine, based on the weight matrix, a neighboring resource of the target resource;
an updating subunit, configured to update the category label of the target resource based on the category label of the adjacent resource by using a label propagation algorithm;
and the second determining subunit is used for determining the target category to which the target resource belongs based on the updated category label.
On the basis of the foregoing embodiment, as a preferred implementation manner, the first determining subunit specifically determines, as a unit of an adjacent resource of the target resource, a resource whose similarity between the category label and the category label of the target resource is greater than a preset value.
On the basis of the foregoing embodiment, as a preferred implementation manner, the category label includes RGB values, and the apparatus further includes:
the initialization module is used for initializing preset color tags and determining a preset RGB value corresponding to each preset color tag;
correspondingly, the second determining subunit is specifically a unit that determines a target preset RGB value that is closest to the RGB value updated by the target resource among all the preset RGB values, determines a target preset color label corresponding to the target preset RGB value, and takes a category corresponding to the target preset color label as a target category to which the target resource belongs.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Based on the hardware implementation of the program module, and in order to implement the method according to the embodiment of the present application, an embodiment of the present application further provides an electronic device, and fig. 4 is a structural diagram of an electronic device according to an exemplary embodiment, as shown in fig. 4, the electronic device includes:
a communication interface 1 capable of information interaction with other devices such as network devices and the like;
and the processor 2 is connected with the communication interface 1 to realize information interaction with other equipment, and is used for executing the resource monitoring method provided by one or more technical schemes when running a computer program. And the computer program is stored on the memory 3.
In practice, of course, the various components in the electronic device are coupled together by the bus system 4. It will be appreciated that the bus system 4 is used to enable connection communication between these components. The bus system 4 comprises, in addition to a data bus, a power bus, a control bus and a status signal bus. For the sake of clarity, however, the various buses are labeled as bus system 4 in fig. 4.
The memory 3 in the embodiment of the present application is used to store various types of data to support the operation of the electronic device. Examples of such data include: any computer program for operating on an electronic device.
It will be appreciated that the memory 3 may be either volatile memory or nonvolatile memory, and may include both volatile and nonvolatile memory. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic random access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The memory 2 described in the embodiments of the present application is intended to comprise, without being limited to, these and any other suitable types of memory.
The method disclosed in the above embodiment of the present application may be applied to the processor 2, or implemented by the processor 2. The processor 2 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 2. The processor 2 described above may be a general purpose processor, a DSP, or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The processor 2 may implement or perform the methods, steps and logic blocks disclosed in the embodiments of the present application. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium located in the memory 3, and the processor 2 reads the program in the memory 3 and in combination with its hardware performs the steps of the aforementioned method.
When the processor 2 executes the program, the corresponding processes in the methods according to the embodiments of the present application are realized, and for brevity, are not described herein again.
In an exemplary embodiment, the present application further provides a storage medium, i.e. a computer storage medium, specifically a computer readable storage medium, for example, including a memory 3 storing a computer program, which can be executed by a processor 2 to implement the steps of the foregoing method. The computer readable storage medium may be Memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface Memory, optical disk, or CD-ROM.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or portions thereof that contribute to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling an electronic device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for resource monitoring, comprising:
acquiring state data of a target resource, and calculating a performance health value of the target resource based on the state data;
determining a target category to which the target resource belongs, and calculating the health degree of the target category according to the state data of all resources under the target category;
and calculating the health degree of the target resource based on the performance health value of the target resource and the health degree of the target category so as to monitor the target resource.
2. The method for monitoring resources according to claim 1, wherein calculating the health of the target class according to the status data of all resources in the target class comprises:
determining the total number of resources of all resources in the target category;
determining the number of normal resources in the target category according to the state data of all the resources in the target category;
and taking the ratio of the number of the normal resources to the total number of the resources as the health degree of the target category.
3. The resource monitoring method of claim 1, wherein calculating the health of the target resource based on the performance health value of the target resource and the health of the target class comprises:
taking the product of the performance health value of the target resource and the health degree of the target category as the health degree of the target resource.
4. The method for monitoring resources according to claim 1, wherein determining the target class to which the target resource belongs comprises:
initializing the class labels of all resources, and calculating the similarity between the class labels of every two resources to construct an NxN weight matrix; wherein N is the total number of resources of all the resources;
and updating the class label of the target resource by utilizing a label propagation algorithm based on the weight matrix, and determining the target class to which the target resource belongs based on the updated class label.
5. The method of claim 4, wherein updating the class label of the target resource using a label propagation algorithm based on the weight matrix comprises:
determining a neighboring resource of the target resource based on the weight matrix;
updating the class label of the target resource based on the class label of the adjacent resource by using a label propagation algorithm.
6. The method of claim 5, wherein determining the neighboring resource of the target resource based on the weight matrix comprises:
and determining the resource with the similarity between the class label and the class label of the target resource larger than a preset value as the adjacent resource of the target resource.
7. The resource monitoring method of claim 5, wherein the category label comprises an RGB value, the method further comprising:
initializing preset color labels, and determining a preset RGB value corresponding to each preset color label;
correspondingly, determining the target category to which the target resource belongs based on the updated category label includes:
determining a target preset RGB value closest to the RGB value after the target resource is updated in all the preset RGB values;
and determining a target preset color label corresponding to the target preset RGB value, and taking a category corresponding to the target preset color label as a target category to which the target resource belongs.
8. A resource monitoring apparatus, comprising:
the acquisition module is used for acquiring state data of the target resource and calculating a performance health value of the target resource based on the state data;
the determining module is used for determining a target category to which the target resource belongs and calculating the health degree of the target category according to the state data of all resources under the target category;
and the calculating module is used for calculating the health degree of the target resource based on the performance health value of the target resource and the health degree of the target category so as to monitor the target resource.
9. An electronic device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the resource monitoring 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 resource monitoring method according to any one of claims 1 to 7.
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| CN202011501445.XA CN112559291A (en) | 2020-12-17 | 2020-12-17 | Resource monitoring method and device, electronic equipment and storage medium |
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