CN109240827B - Method and device for determining resource occupation condition of application, storage medium and equipment - Google Patents
Method and device for determining resource occupation condition of application, storage medium and equipment Download PDFInfo
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Abstract
The disclosure relates to a method, a device, a storage medium and equipment for determining resource occupation condition of application, wherein the method comprises the following steps: acquiring system resource data, wherein the system resource data comprises the total occupancy rate of system resources in a preset time period and the occupancy rate of each application to the system resources in the preset time period; secondly, generating a Bayesian network according to the system resource data, wherein the Bayesian network comprises a topological relation between each system resource and the application, and the topological relation is used for representing the application using each system resource; generating a conditional probability table by using a Bayesian network structure and discretization data obtained after discretization processing is carried out on the system resource data; and determining the resource occupation score of each system resource occupied by each application according to the conditional probability table, and determining the resource occupation type of each application according to the resource occupation score of each application. The system resource occupation of each application in the system can be determined.
Description
Technical Field
The present disclosure relates to the field of operation and maintenance technologies, and in particular, to a method, an apparatus, a storage medium, and a device for determining resource occupation status of an application.
Background
Resources such as a Central Processing Unit (CPU) resource, a memory resource, a disk Input/Output (IO) resource, a network IO resource, and the like are important computer system resources, and are a basis for ensuring normal operation of each application in the system. In an actual production environment, some problems (for example, memory leakage caused by a code quality problem or a program algorithm problem, CPU intensive computation, etc.) are often encountered, so that the running problem of some applications may sharply increase the consumption of system resources, so that one or more of the computer resources are exhausted, and other running applications in the system cannot obtain the required system resources, thereby affecting the normal running of other applications, and even possibly causing downtime.
However, the conventional system resource monitoring tool (such as top, a Linux tool, a system administrator can use a top operation command to monitor the process and the overall performance of Linux) can only provide monitoring when a problem occurs, and help operation and maintenance personnel find problem applications. However, it is uncertain which applications are caused, and therefore how to determine whether each application is a resource-consuming application is a problem that needs to be solved urgently at present.
Disclosure of Invention
The purpose of the present disclosure is to provide a method, an apparatus, a storage medium, and a device for determining resource occupation status of an application, so as to identify resource occupation status of each application in a system.
In order to achieve the above object, a first aspect of the present disclosure provides a method for determining resource occupation status of an application, where the method includes:
acquiring system resource data, wherein the system resource data comprises the total occupancy rate of system resources in a preset time period and the occupancy rate of each application to the system resources in the preset time period, and the system resources comprise one or more types;
generating a Bayesian network according to the system resource data, wherein the Bayesian network comprises a topological relation between each system resource and an application, and the topological relation is used for representing the application using each system resource;
generating a conditional probability table by using the Bayesian network structure and discretization data obtained after discretization processing is carried out on the system resource data;
determining a resource occupation score of each system resource occupied by each application pair according to the conditional probability table;
and determining the resource occupation type of each application according to the resource occupation score of each application.
Optionally, the generating a conditional probability table of discretization data obtained by performing discretization processing on the system resource data by using the bayesian network structure includes:
discretizing the total occupancy rate of the system resources in the preset time period according to preset m value intervals to obtain first discretization data; wherein the interval ranges of the m value intervals are sequentially increased;
discretizing the occupancy rate of each application to the system resources in the preset time period according to preset n value intervals to obtain second discretization data; the interval ranges of the n value intervals are sequentially increased;
performing statistics by using the bayesian network according to the first discretization data and the second discretization data to generate the conditional probability table, where the conditional probability table includes: when the occupancy rate of each application to the first system resource is in the ith value interval of the n value intervals, the total occupancy rate of the first system resource is in the conditional probability of the jth value interval of the m value intervals; wherein the first system resource is any system resource.
Optionally, the determining, according to the conditional probability table, a resource occupation score of each system resource occupied by each application pair includes:
according to the conditional probability table, obtaining expected values of the first system resources when the occupancy rates of the first application to the first system resources are in the n value intervals respectively; wherein the first application is any application;
and acquiring the resource occupation score of the first application to the first system resource according to the expected value of the first system resource when the occupancy rate of the first application to the first system resource is in the n value intervals respectively.
Optionally, the obtaining, according to the expected values of the first system resources when the occupancy rates of the first application to the first system resources are in the n value intervals, the resource occupancy scores of the first application to the first system resources includes:
acquiring the probability distribution of the occupancy rate of the first application to the first system resource in the n value intervals according to the second discretization data;
determining the value interval with the maximum probability distribution of the first application to the first system resource according to the probability distribution of the n value intervals;
acquiring a resource occupation score of each application for each system resource occupied by the first application according to the expected value of the first system resource when the occupancy rate of the first application for the first system resource is in the n value intervals and the value interval with the maximum probability distribution of the first application for the first system resource;
the resource occupation score calculation formula comprises:
wherein resource isxRepresenting system resources x, AiRepresenting the state when the occupancy rate of the application A to the system resource x is positioned in the ith value interval of the n value intervals, wherein A iscThe state that the occupancy rate of the application A to the system resource x is located in the c-th value section of the n value sections is represented, the c-th value section is the value section with the maximum probability distribution of the application A to the system resource x,
represents the expected value, E (SYS), when the occupancy rate of the application A on the system resource x is in the ith value intervalresource|Ac) And y represents the resource occupancy score of the application A to the system resource x.
Optionally, the conditional probability table further includes: when the occupancy rate of each application to the first system resource is in the ith value interval of the n value intervals, the occupancy rates of other applications to the first system resource are in the conditional probability of the kth value interval of the n value intervals; wherein the first system resource is any system resource; the method further comprises the following steps:
according to the conditional probability table, obtaining expected values of a second application about the first system resource when the occupancy rates of the first application to the first system resource are in the n value intervals respectively; wherein the second application is any application other than the first application;
and acquiring the influence degree score of the first application on the second application according to the expected value of the second application about the first system resource when the occupancy rate of the first application on the first system resource is in the n value intervals respectively.
Optionally, the obtaining, according to an expected value of a second application with respect to the first system resource when the occupancy rates of the first application to the first system resource are in the n value intervals, a score of an influence degree of the first application on the second application includes:
when the occupancy rates of the first application to the first system resources are respectively in the n value intervals, the expected value of the second application to the first system resources and the value interval of the first application to the first system resources with the maximum probability distribution are used for obtaining the resource occupancy score of each application to each occupied system resource by using an influence degree score calculation formula;
the influence degree score calculation formula comprises:
wherein resource isxRepresenting system resources x, AiThe state of the application A when the occupancy rate of the system resource x is positioned in the ith value interval of the n value intervals is represented, and the state of the application B when the occupancy rate of the system resource x is positioned in the ith value interval of the n value intervals is representedcThe state of the application B when the occupancy rate of the system resource x is in the c-th value interval of the n value intervals is represented, wherein the c-th value interval is the time that the occupancy rate of the system resource x is required to be reducedUsing the value interval with the maximum probability distribution of B to the system resource x,
when the occupancy rate of the application A to the system resource x is in the ith value interval, the expected value of the application B to the system resource x is shown, and y represents the influence degree score of the application A to the application B.
Optionally, the system resources include a CPU, a memory, a disk IO, and/or a network IO, the generating a bayesian network according to the system resource data, the bayesian network including a topological relation between each system resource and an application, the topological relation being used to represent the application using each system resource, includes:
and respectively determining the application using the CPU, the application using the memory, the application using the disk IO and/or the application using the network IO according to the occupancy rate of each application on the CPU, the memory, the disk IO and/or the network IO.
In a second aspect, an apparatus for determining resource occupation of an application is provided, the apparatus comprising:
the data acquisition module is used for acquiring system resource data, wherein the system resource data comprises the total occupancy rate of system resources in a preset time period and the occupancy rate of each application to the system resources in the preset time period, and the system resources comprise one or more types;
a network construction module, configured to generate a bayesian network according to the system resource data, where the bayesian network includes a topological relation between each system resource and an application, and the topological relation is used to represent the application using each system resource;
the conditional probability calculation module is used for generating a conditional probability table by using the Bayesian network structure and discretization data obtained after discretization processing is carried out on the system resource data;
the first scoring module is used for determining the resource occupation score of each system resource occupied by each application pair according to the conditional probability table;
and the type determining module is used for determining the resource occupation type of each application according to the resource occupation score of each application.
Optionally, the conditional probability calculating module includes:
the discretization processing submodule is used for discretizing the total occupancy rate of the system resources in the preset time period according to preset m value intervals to obtain first discretization data; wherein the interval ranges of the m value intervals are sequentially increased;
the discretization processing submodule is further configured to perform discretization processing on the occupancy rate of each application to the system resources in the preset time period according to preset n value intervals to obtain second discretization data; the interval ranges of the n value intervals are sequentially increased;
a probability calculation sub-module, configured to perform statistics by using the bayesian network according to the first discretization data and the second discretization data to generate the conditional probability table, where the conditional probability table includes: when the occupancy rate of each application to the first system resource is in the ith value interval of the n value intervals, the total occupancy rate of the first system resource is in the conditional probability of the jth value interval of the m value intervals; wherein the first system resource is any system resource.
Optionally, the first scoring module includes:
the expectation obtaining submodule is used for obtaining the expectation value of the first system resource when the occupancy rate of the first application to the first system resource is respectively in the n value intervals according to the conditional probability table; wherein the first application is any application;
and the score obtaining submodule is used for obtaining the resource occupation score of the first application on the first system resource according to the expected value of the first system resource when the occupancy rate of the first application on the first system resource is respectively in the n value intervals.
Optionally, the score obtaining sub-module is configured to:
acquiring the probability distribution of the occupancy rate of the first application to the first system resource in the n value intervals according to the second discretization data;
determining the value interval with the maximum probability distribution of the first application to the first system resource according to the probability distribution of the n value intervals;
acquiring a resource occupation score of each application for each system resource occupied by the first application according to the expected value of the first system resource when the occupancy rate of the first application for the first system resource is in the n value intervals and the value interval with the maximum probability distribution of the first application for the first system resource;
the resource occupation score calculation formula comprises:
wherein resource isxRepresenting system resources x, AiRepresenting the state when the occupancy rate of the application A to the system resource x is positioned in the ith value interval of the n value intervals, wherein A iscThe state that the occupancy rate of the application A to the system resource x is located in the c-th value section of the n value sections is represented, the c-th value section is the value section with the maximum probability distribution of the application A to the system resource x,
represents the expected value, E (SYS), when the occupancy rate of the application A on the system resource x is in the ith value intervalresource|Ac) And y represents the resource occupancy score of the application A to the system resource x.
Optionally, the conditional probability table further includes: when the occupancy rate of each application to the first system resource is in the ith value interval of the n value intervals, the occupancy rates of other applications to the first system resource are in the conditional probability of the kth value interval of the n value intervals; wherein the first system resource is any system resource; the device further comprises:
an expected value obtaining module, configured to obtain, according to the conditional probability table, an expected value of a second application about the first system resource when the occupancy rates of the first application to the first system resource are in the n value intervals, respectively; wherein the second application is any application other than the first application;
and the second scoring module is used for acquiring the influence degree score of the first application on the second application according to the expected value of the second application on the first system resource when the occupancy rate of the first application on the first system resource is in the n value intervals respectively.
Optionally, the second scoring module is configured to:
when the occupancy rates of the first application to the first system resources are respectively in the n value intervals, the expected value of the second application to the first system resources and the value interval of the first application to the first system resources with the maximum probability distribution are used for obtaining the resource occupancy score of each application to each occupied system resource by using an influence degree score calculation formula;
the influence degree score calculation formula comprises:
wherein resource isxRepresenting system resources x, AiThe state of the application A when the occupancy rate of the system resource x is positioned in the ith value interval of the n value intervals is represented, and the state of the application B when the occupancy rate of the system resource x is positioned in the ith value interval of the n value intervals is representedcThe state that the occupancy rate of the application B to the system resource x is positioned in the c-th value section of the n value sections is represented, the c-th value section is the value section with the maximum probability distribution of the application B to the system resource x,
when the occupancy rate of the application A to the system resource x is in the ith value interval, the expected value of the application B to the system resource x is shown, and y represents the influence degree score of the application A to the application B.
Optionally, the system resource includes a CPU, a memory, a disk IO, and/or a network IO, and the network construction module is configured to:
and respectively determining the application using the CPU, the application using the memory, the application using the disk IO and/or the application using the network IO according to the occupancy rate of each application on the CPU, the memory, the disk IO and/or the network IO.
In a third aspect, a computer-readable storage medium is provided, on which a computer program is stored, the computer program being executed by a processor for performing the steps of the method of the first aspect.
In a fourth aspect, an electronic device is provided, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of the first aspect.
In the technical solution provided by the present disclosure, system resource data is obtained first, where the system resource data includes the total occupancy rate of system resources in a preset time period and the occupancy rate of each application to the system resources in the preset time period, and the system resources include one or more types; secondly, generating a Bayesian network according to the system resource data, wherein the Bayesian network comprises a topological relation between each system resource and the application, and the topological relation is used for representing the application using each system resource; then, a Bayesian network structure is utilized, and a conditional probability table is generated for discretization data obtained after discretization processing is carried out on the system resource data; and determining the resource occupation score of each application to each occupied system resource according to the conditional probability table, and determining the resource occupation type of each application according to the resource occupation score of each application. Therefore, the system resource occupation condition of each application in the system can be determined, and operation and maintenance personnel can find problems in advance according to the system resource occupation condition of each application.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
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 chart illustrating a method for determining resource occupancy of an application according to an exemplary embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating the topological relationships of applications using CPU resources in a system according to an exemplary embodiment of the present disclosure;
FIG. 3 is a flow chart illustrating a conditional probability table generation method according to the embodiment shown in FIG. 1;
FIG. 4 is a flow diagram illustrating a method for determining a resource occupancy score according to the embodiment shown in FIG. 1;
FIG. 5 is a flow chart illustrating a method for determining resource occupancy for another application according to an exemplary embodiment of the present disclosure;
FIG. 6 is a block diagram illustrating an apparatus for determining resource occupancy for another application according to an exemplary embodiment of the present disclosure;
FIG. 7 is a block diagram illustrating a conditional probability computation module according to an exemplary embodiment of the present disclosure;
FIG. 8 is a block diagram illustrating a first scoring module according to an exemplary embodiment of the present disclosure;
FIG. 9 is a block diagram illustrating an apparatus for determining resource occupancy for another application according to an exemplary embodiment of the present disclosure;
FIG. 10 is a block diagram illustrating an electronic device in accordance with an exemplary embodiment of the present disclosure;
fig. 11 is a block diagram illustrating another electronic device according to an exemplary embodiment of the present disclosure.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a flowchart illustrating a method for determining resource occupation of an application according to an exemplary embodiment of the present disclosure, where as shown in fig. 1, the method may include:
The system resources may also include: resources such as a CPU, a memory, a disk IO (Input/Output), a network IO, and the like. Accordingly, the occupancy rate of each application for system resources may include: one or more of CPU occupancy rate, memory occupancy rate, disk IO occupancy rate and network IO occupancy rate, wherein the total occupancy rate of system resources comprises: one or more of total occupancy rate of CPU, total occupancy rate of memory, total occupancy rate of disk IO and total occupancy rate of network IO.
For example, in the embodiment of the present disclosure, the total CPU occupancy, the total memory occupancy, the total disk IO occupancy, the total network IO occupancy, and the CPU occupancy, the memory occupancy, the total disk IO occupancy, and the total network IO occupancy of each application in the system are obtained in a preset time period, which may be, for example, one hour, one day, one week, and the like.
For example, it is determined which applications are used by each of the resources such as CPU, memory, disk IO, network IO, etc. For example, as shown in fig. 2, a schematic diagram of the topology of an application using CPU resources in a system is shown. As shown in FIG. 2, the CPU applications used are tomcat, mysqld, python, sshd, solr, and main. For resources such as memory, disk IO, network IO, and the like, the same principle as that of the CPU is not described again.
The discretization processing refers to dividing the system resource data obtained in the step 101 according to different value intervals. For example, according to preset occupancy rate value intervals in different ranges, intervals in which the CPU occupancy rate, the memory occupancy rate, the disk IO occupancy rate, and/or the network IO occupancy rate of each application are located may be respectively determined, and in the same way, the same discretization process may be performed on the CPU total occupancy rate, the memory total occupancy rate, the disk IO total occupancy rate, and the network IO total occupancy rate. Correspondingly, the conditional probability table determined according to the discretized data includes a plurality of conditional probabilities, for example, the conditional probability that the total occupancy of a certain system resource is in a certain value interval when the occupancy of each application to a certain resource is in a certain value interval. For example, when the CPU occupancy rate of the application a is in the value interval 1, the CPU total occupancy rate is in the conditional probability of the value interval 2.
And 104, determining the resource occupation score of each system resource occupied by each application pair according to the conditional probability table.
And step 105, determining the resource occupation type of each application according to the resource occupation score of each application.
For example, according to the conditional probability in the conditional probability table, the influence degree of each application on the occupation situation of each occupied system resource can be reflected, so the resource occupation score can be calculated based on the conditional probability in the conditional probability table, and the resource occupation type of each application can be determined according to the height of the resource occupation score. The resource occupation type may include: resource-consuming applications, non-resource-consuming applications. Or finer type division such as high resource consuming applications, medium resource consuming applications and non-resource consuming applications may also be set as required.
In the technical scheme, system resource data are obtained firstly, wherein the system resource data comprise the total occupancy rate of system resources in a preset time period and the occupancy rate of each application to the system resources in the preset time period, and the system resources comprise one or more types; secondly, generating a Bayesian network according to the system resource data, wherein the Bayesian network comprises a topological relation between each system resource and the application, and the topological relation is used for representing the application using each system resource; then, a Bayesian network structure is utilized, and a conditional probability table is generated for discretization data obtained after discretization processing is carried out on the system resource data; and determining the resource occupation score of each application to each occupied system resource according to the conditional probability table, and determining the resource occupation type of each application according to the resource occupation score of each application. Therefore, the system resource occupation condition of each application in the system can be determined, and operation and maintenance personnel can find problems in advance according to the system resource occupation condition of each application.
Fig. 3 is a flowchart illustrating a conditional probability table generating method according to the embodiment shown in fig. 1, and as shown in fig. 3, the step of generating the conditional probability table by using the bayesian network structure and discretizing the discretized data obtained by discretizing the system resource data in step 103 may include the following steps:
1031, discretizing the total occupancy rate of the system resources in the preset time period according to preset m value intervals to obtain first discretization data; wherein, the interval range of the m value intervals is sequentially increased.
For example, 5 value intervals may be set for the total CPU occupancy, the total memory occupancy, the total disk IO occupancy, and the total network IO occupancy, such as: in the preset time period, the total occupancy rate of the CPU, the total occupancy rate of the memory, the total occupancy rate of the disk IO and the total occupancy rate of the network IO at each moment can be acquired, so that the intervals to which the total occupancy rate of the CPU, the total occupancy rate of the memory, the total occupancy rate of the disk IO and the total occupancy rate of the network IO acquired at each moment belong can be determined according to the value intervals.
Step 1032, discretizing the occupancy rate of each application to the system resources in the preset time period according to preset n value intervals to obtain second discretization data; wherein, the interval range of the n value intervals is sequentially increased.
Similar to the value section of the total occupancy rate of the system resources, a plurality of value sections may be divided between 0 and 1, and may be the same as or different from the value section divided in step 1031. For example, 3 value intervals may be set, such as: [0,0.1 ], (0.1,0.2], (0.2,01 ]. where the values of the above-mentioned value intervals are exemplary, including but not limited thereto, larger or smaller partition particle sizes may be set according to actual needs.
Step 1033, performing statistics by using the bayesian network according to the first discretization data and the second discretization data to generate the conditional probability table.
Wherein the conditional probability table includes: when the occupancy rate of each application to the first system resource is in the ith value interval of the n value intervals, the total occupancy rate of the first system resource is in the conditional probability of the jth value interval of the m value intervals; wherein the first system resource is any system resource.
For example, taking the sshd as an example, if the CPU occupancy of sshd is in the intervals of [0,0.1), (0.1,0.2], (0.2,01], the total CPU occupancy of the system is in the intervals of [0,0.2), (0.2,0.4], (0.4,0.6], (0.6,0.8], (0.8,1 ]) according to the first discretization data and the second discretization data, the conditional probability in the intervals of [0,0.2), (0.2, 0.4), (0.4, 0.6), (0.6,0.8, 1] can be calculated, similarly, the conditional probability in the intervals of [0,0.1), (0.1,0.2], (0.2,01] for the CPU occupancy of each application in the system (or memory/disk IO/network IO) is in the intervals of [0,0.2), (0.2,0.4], (0.6 ], (0.8), (0.8,1] for the CPU (or disk IO/network IO).
Fig. 4 is a flowchart illustrating a method for determining a resource occupation score according to the embodiment shown in fig. 1, where, as shown in fig. 4, the step of determining a resource occupation score of each system resource occupied by each application pair according to the conditional probability table in step 104 may include the following steps:
Wherein, the step 1041 may include:
firstly, according to the second discretization data, obtaining the probability distribution of the occupancy rate of the first application to the first system resource in the n value intervals.
Secondly, according to the probability distribution in the n value intervals, the value interval with the maximum probability distribution of the first application to the first system resource is determined.
And finally, according to the expected value of the first system resource when the occupancy rate of the first application to the first system resource is respectively in the n value intervals and the value interval with the maximum probability distribution of the first application to the first system resource, acquiring the resource occupancy score of each application to each occupied system resource by using a resource occupancy score calculation formula.
Wherein, the resource occupation score calculation formula comprises:
wherein resource isxRepresenting system resources x, AiRepresenting the state when the occupancy rate of the application A to the system resource x is positioned in the ith value interval of the n value intervals, wherein A iscIndicating that the occupancy of the system resource x by the application a is located atA state in a c-th value interval of the n value intervals, wherein the c-th value interval is a value interval with the maximum probability distribution of the application A to the system resource x,
represents the expected value, E (SYS), when the occupancy rate of the application A on the system resource x is in the ith value intervalresource|Ac) And y represents the resource occupancy score of the application A to the system resource x.
The higher the resource occupation score of any application on a certain system resource is, the higher the occupation degree of the application on the system resource is. Therefore, one or more resource occupation score thresholds can be set for each application for each system resource, so as to determine whether each application is a resource consumption type application according to the resource occupation score of each application for the system resource and the set resource occupation score threshold. For example, a resource occupancy score threshold 1 and a resource occupancy score threshold 2 may be set, where the resource occupancy score threshold 1 is greater than the resource occupancy score threshold 2, when the resource occupancy score of an application for a system resource is less than the resource occupancy score threshold 2, the application is determined to be a non-resource consuming application, when the resource occupancy score of the application for the system resource is greater than or equal to the resource occupancy score threshold 2 and less than the resource occupancy score threshold 1, the application is determined to be a medium-resource consuming application, and when the resource occupancy score of the application for the system resource is greater than the resource occupancy score threshold 1, the application is determined to be a high-resource consuming application.
Further, the conditional probability table further includes: when the occupancy rate of each application to the first system resource is in the ith value interval of the n value intervals, the occupancy rates of other applications to the first system resource are in the conditional probability of the kth value interval of the n value intervals; wherein the first system resource is any system resource.
For example, taking sshd and main as examples, the conditional probability table further includes: when the CPU occupancy of sshd is in the intervals of [0,0.1), (0.1,0.2], (0.2,01], respectively, the CPU occupancy of main is in the conditional probabilities in the intervals of [0,0.1), (0.1,0.2], (0.2,01], similarly, the conditional probability table also comprises the conditional probabilities that when the occupancy of each application to CPU (or memory/disk IO/network IO) is in the intervals of [0,0.1), (0.1,0.2], (0.2,01], the occupancy of other applications to CPU (or memory/disk IO/network IO) is in the intervals of [0,0.1), (0.1,0.2], (0.2,01], respectively.
Fig. 5 is a flowchart illustrating a method for determining resource occupation of another application according to an exemplary embodiment of the disclosure, where as shown in fig. 5, the method may include:
And 107, obtaining the influence degree score of the first application on the second application according to the expected value of the second application on the first system resource when the occupancy rate of the first application on the first system resource is in the n value intervals respectively.
Wherein the step 107 may include:
when the occupancy rates of the first application to the first system resources are respectively in the n value intervals, the expected value of the second application relative to the first system resources and the value interval with the maximum probability distribution of the first application to the first system resources are used for acquiring the resource occupancy score of each application to each occupied system resource by using an influence degree score calculation formula;
wherein the influence degree score may include:
wherein resource isxRepresenting system resources x, AiThe state of the application A when the occupancy rate of the system resource x is positioned in the ith value interval of the n value intervals is represented, and the state of the application B when the occupancy rate of the system resource x is positioned in the ith value interval of the n value intervals is representedcThe state that the occupancy rate of the application B to the system resource x is positioned in the c-th value section of the n value sections is represented, the c-th value section is the value section with the maximum probability distribution of the application B to the system resource x,
when the occupancy rate of the application A to the system resource x is in the ith value interval, the expected value of the application B to the system resource x is shown, and y represents the influence degree score of the application A to the application B.
Wherein, the higher the influence degree score of any application on other applications, the higher the influence degree of the application on other applications on the system resource is. Therefore, one or more threshold values of the degree of influence score can be set for each application for each system resource, so that each application is determined to influence other applications according to the degree of influence score of each application on other applications. For example, an influence degree score 1 and an influence degree score 2 may be set, where the influence degree score 1 is greater than the influence degree score 2, when the influence degree score of a certain application a on another application B with respect to the system resource C is less than the influence degree score threshold 2, it is determined that the application B is not influenced by the application a, when the influence degree score is greater than or equal to the influence degree score threshold 2 and less than the influence degree score threshold 1, it is determined that the application B is influenced by the application a, the influence degree is medium, and when the influence degree score is greater than the influence degree score threshold 1, it is determined that the application B is influenced by the application a, the influence degree is serious.
In the technical solution provided by the present disclosure, system resource data is obtained first, where the system resource data includes the total occupancy rate of system resources in a preset time period and the occupancy rate of each application to the system resources in the preset time period, and the system resources include one or more types; secondly, generating a Bayesian network according to the system resource data, wherein the Bayesian network comprises a topological relation between each system resource and the application, and the topological relation is used for representing the application using each system resource; then, a Bayesian network structure is utilized, and a conditional probability table is generated for discretization data obtained after discretization processing is carried out on the system resource data; and determining the resource occupation score of each application to each occupied system resource according to the conditional probability table, and determining the resource occupation type of each application according to the resource occupation score of each application. Therefore, the system resource occupation condition of each application in the system can be determined, and operation and maintenance personnel can find problems in advance according to the system resource occupation condition of each application.
Fig. 6 is a block diagram illustrating another apparatus for determining resource occupation according to an exemplary embodiment of the disclosure, where as shown in fig. 6, the apparatus 600 may include:
a data obtaining module 601, configured to obtain system resource data, where the system resource data includes a total occupancy rate of system resources in a preset time period and an occupancy rate of each application to the system resources in the preset time period, and the system resources include one or more types;
a network building module 602, configured to generate a bayesian network according to the system resource data, where the bayesian network includes a topological relation between each system resource and an application, and the topological relation is used to represent the application using each system resource;
a conditional probability calculation module 603, configured to generate a conditional probability table using the bayesian network structure and discretized data obtained after discretizing the system resource data;
a first scoring module 604, configured to determine a resource occupation score of each system resource occupied by each application pair according to the conditional probability table;
a type determining module 605, configured to determine the resource occupation type of each application according to the resource occupation score of each application.
Fig. 7 is a block diagram illustrating a conditional probability calculation module according to an exemplary embodiment of the present disclosure, and as shown in fig. 7, the conditional probability calculation module 603 may include:
the discretization processing submodule 6031 is configured to perform discretization processing on the total occupancy rate of the system resource in the preset time period according to m preset value intervals to obtain first discretization data; wherein the interval ranges of the m value intervals are sequentially increased;
the discretization processing submodule 6031 is further configured to perform discretization processing on the occupancy rate of each application to the system resource in the preset time period according to preset n value intervals, so as to obtain second discretization data; wherein, the interval ranges of the n value intervals are sequentially increased;
a probability calculation sub-module 6032, configured to perform statistics on the bayesian network according to the first discretization data and the second discretization data to generate the conditional probability table, where the conditional probability table includes: when the occupancy rate of each application to the first system resource is in the ith value interval of the n value intervals, the total occupancy rate of the first system resource is in the conditional probability of the jth value interval of the m value intervals; wherein the first system resource is any system resource.
Optionally, fig. 8 is a block diagram illustrating a first scoring module according to an exemplary embodiment of the disclosure, and as shown in fig. 8, the first scoring module 604 may include:
an expected obtaining submodule 6041, configured to obtain, according to the conditional probability table, expected values of the first system resource when the occupancy rates of the first application to the first system resource are in the n value intervals, respectively; wherein the first application is any application;
the score obtaining sub-module 6042 is configured to obtain the resource occupancy score of the first application for the first system resource according to the expected value of the first system resource when the occupancy rate of the first application for the first system resource is in the n value intervals, respectively.
Optionally, the score obtaining sub-module 6042 may be configured to:
acquiring the probability distribution of the occupancy rate of the first application to the first system resource in the n value intervals according to the second discretization data;
determining the value interval with the maximum probability distribution of the first application to the first system resource according to the probability distribution of the n value intervals;
acquiring a resource occupation score of each application for each system resource occupied by the first application according to the expected value of the first system resource when the occupancy rate of the first application for the first system resource is in the n value intervals and the value interval with the maximum probability distribution of the first application for the first system resource;
the resource occupation score calculation formula comprises:
wherein resource isxRepresenting system resources x, AiThe state of the application A when the occupancy rate of the system resource x is in the ith value interval of the n value intervals is shown, wherein A iscThe state that the occupancy rate of the application A to the system resource x is positioned in the c-th value section of the n value sections is shown, the c-th value section is the value section with the maximum probability distribution of the application A to the system resource x,
represents the expected value, E (SYS), when the occupancy rate of the application a for the system resource x is in the ith value intervalresource|Ac) And y represents the resource occupancy score of the application A for the system resource x.
Optionally, the conditional probability table further includes: when the occupancy rate of each application to the first system resource is in the ith value interval of the n value intervals, the occupancy rates of other applications to the first system resource are in the conditional probability of the kth value interval of the n value intervals; wherein the first system resource is any system resource; fig. 9 is a block diagram illustrating an apparatus for determining resource occupation of another application according to an exemplary embodiment of the disclosure, and as shown in fig. 9, the apparatus 600 may further include:
an expected obtaining module 606, configured to obtain, according to the conditional probability table, an expected value of a second application about the first system resource when the occupancy rates of the first application to the first system resource are in the n value intervals, respectively; wherein the second application is any application other than the first application;
the second scoring module 607 is configured to obtain, according to the expected values of the first system resource of the second application when the occupancy rates of the first application on the first system resource are in the n value intervals, the influence degree score of the first application on the second application.
Optionally, the second scoring module 607 may be configured to:
when the occupancy rates of the first application to the first system resources are respectively in the n value intervals, the expected value of the second application relative to the first system resources and the value interval with the maximum probability distribution of the first application to the first system resources are used for acquiring the resource occupancy score of each application to each occupied system resource by utilizing an influence degree score calculation formula;
the influence degree score calculation formula comprises:
wherein resource isxRepresenting system resources x, AiThe state of the application A when the occupancy rate of the system resource x is positioned in the ith value interval of the n value intervals is represented, and the state of the application B when the occupancy rate of the system resource x is positioned in the ith value interval of the n value intervals is representedcThe state that the occupancy rate of the application B to the system resource x is positioned in the c-th value section of the n value sections is shown, the c-th value section is the value section with the maximum probability distribution of the application B to the system resource x,
when the occupancy rate of the application A to the system resource x is in the ith value interval, the expected value of the application B to the system resource x is shown, and y shows that the influence degree of the application A to the application B is highAnd (4) dividing.
The system resource includes a CPU, a memory, a disk IO and/or a network IO, and the network construction module 602 may be configured to:
and respectively determining the application using the CPU, the application using the memory, the application using the disk IO and/or the application using the network IO according to the occupancy rate of each application to the CPU, the memory, the disk IO and/or the network IO.
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.
In the technical solution provided by the present disclosure, system resource data is obtained first, where the system resource data includes the total occupancy rate of system resources in a preset time period and the occupancy rate of each application to the system resources in the preset time period, and the system resources include one or more types; secondly, generating a Bayesian network according to the system resource data, wherein the Bayesian network comprises a topological relation between each system resource and the application, and the topological relation is used for representing the application using each system resource; then, a Bayesian network structure is utilized, and a conditional probability table is generated for discretization data obtained after discretization processing is carried out on the system resource data; and determining the resource occupation score of each application to each occupied system resource according to the conditional probability table, and determining the resource occupation type of each application according to the resource occupation score of each application. Therefore, the system resource occupation condition of each application in the system can be determined, and operation and maintenance personnel can find problems in advance according to the system resource occupation condition of each application.
FIG. 10 is a block diagram illustrating an electronic device in accordance with an example embodiment. As shown in fig. 10, the electronic device 1000 may include: a processor 1001 and a memory 1002. The electronic device 1000 may also include one or more of a multimedia component 1003, an input/output (I/O) interface 1004, and a communications component 1005.
The processor 1001 is configured to control the overall operation of the electronic device 1000, so as to complete all or part of the steps in the method for determining the resource occupation status of the application. The memory 1002 is used to store various types of data to support operation of the electronic device 1000, such as instructions for any application or method operating on the electronic device 1000 and application-related data, such as contact data, messaging, pictures, audio, video, and so forth. The Memory 1002 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as a Static Random Access Memory (SRAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), an Erasable Programmable Read-Only Memory (EPROM), a Programmable Read-Only Memory (PROM), a Read-Only Memory (ROM), a magnetic Memory, a flash Memory, a magnetic disk, or an optical disk. The multimedia components 703 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may further be stored in memory 1002 or transmitted through communication component 1005. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 1004 provides an interface between the processor 1001 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 1005 is used for wired or wireless communication between the electronic device 1000 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding Communication component 1005 may include: Wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the electronic Device 1000 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, for executing the method for determining the resource occupation status of the Application.
In another exemplary embodiment, a computer readable storage medium is also provided, which comprises program instructions, which when executed by a processor, implement the steps of the above-mentioned method for determining resource occupation of an application. For example, the computer readable storage medium may be the memory 1002 comprising program instructions executable by the processor 1001 of the electronic device 1000 to perform the method for determining resource occupation of an application as described above.
FIG. 11 is a block diagram illustrating another electronic device in accordance with an example embodiment. For example, the electronic device 1100 may be provided as a server. Referring to fig. 11, electronic device 1100 includes a processor 1122, which can be one or more in number, and a memory 1132 for storing computer programs executable by processor 1122. The computer programs stored in memory 1132 may include one or more modules that each correspond to a set of instructions. Further, the processor 1122 may be configured to execute the computer program to perform the above-described method for determining resource occupancy of an application.
Additionally, the electronic device 1100 may also include a power component 1126 and a communication component 1150, the power component 1126 may be configured to perform power management of the electronic device 1100, and the communication component 1150 may be configured to enable communication, e.g., wired or wireless communication, of the electronic device 1100. In addition, the electronic device 1100 may also include an input/output (I/O) interface 1158. The electronic device 1100 may operate based on an operating system stored in memory 1132, such as Windows Server, Mac OS XTM, UnixTM, Linux, and the like.
In another exemplary embodiment, a computer readable storage medium is also provided, which comprises program instructions, which when executed by a processor, implement the steps of the above-mentioned method for determining resource occupation of an application. For example, the computer readable storage medium may be the memory 1132 described above comprising program instructions executable by the processor 1122 of the electronic device 1100 to perform the method for determining resource occupancy of an application described above.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various combinations that are possible in the present disclosure are not described again.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Claims (9)
1. A method for determining resource occupation of an application, the method comprising:
acquiring system resource data, wherein the system resource data comprises the total occupancy rate of system resources in a preset time period and the occupancy rate of each application to the system resources in the preset time period, and the system resources comprise one or more types;
generating a Bayesian network according to the system resource data, wherein the Bayesian network comprises a topological relation between each system resource and an application, and the topological relation is used for representing the application using each system resource;
generating a conditional probability table by using the Bayesian network structure and discretization data obtained after discretization processing is carried out on the system resource data;
determining a resource occupation score of each system resource occupied by each application pair according to the conditional probability table;
determining the resource occupation type of each application according to the resource occupation score of each application;
the generating a conditional probability table of discretization data obtained by discretizing the system resource data by using the bayesian network structure includes:
discretizing the total occupancy rate of the system resources in the preset time period according to preset m value intervals to obtain first discretization data; wherein the interval ranges of the m value intervals are sequentially increased;
discretizing the occupancy rate of each application to the system resources in the preset time period according to preset n value intervals to obtain second discretization data; the interval ranges of the n value intervals are sequentially increased;
performing statistics by using the bayesian network according to the first discretization data and the second discretization data to generate the conditional probability table, where the conditional probability table includes: when the occupancy rate of each application to the first system resource is in the ith value interval of the n value intervals, the total occupancy rate of the first system resource is in the conditional probability of the jth value interval of the m value intervals; wherein the first system resource is any system resource.
2. The method according to claim 1, wherein said determining a resource occupation score of each system resource occupied by each application pair according to the conditional probability table comprises:
according to the conditional probability table, obtaining expected values of the first system resources when the occupancy rates of the first application to the first system resources are in the n value intervals respectively; wherein the first application is any application;
and acquiring the resource occupation score of the first application to the first system resource according to the expected value of the first system resource when the occupancy rate of the first application to the first system resource is in the n value intervals respectively.
3. The method according to claim 2, wherein the obtaining the resource occupancy score of the first application for the first system resource according to the expected value of the first system resource when the occupancy rate of the first application for the first system resource is in the n value intervals respectively comprises:
acquiring the probability distribution of the occupancy rate of the first application to the first system resource in the n value intervals according to the second discretization data;
determining the value interval with the maximum probability distribution of the first application to the first system resource according to the probability distribution of the n value intervals;
acquiring a resource occupation score of each application for each system resource occupied by the first application according to the expected value of the first system resource when the occupancy rate of the first application for the first system resource is in the n value intervals and the value interval with the maximum probability distribution of the first application for the first system resource;
the resource occupation score calculation formula comprises:
wherein resource isxRepresenting system resources x, AiRepresenting the state when the occupancy rate of the application A to the system resource x is positioned in the ith value interval of the n value intervals, wherein A iscThe state that the occupancy rate of the application A to the system resource x is located in the c-th value section of the n value sections is represented, the c-th value section is the value section with the maximum probability distribution of the application A to the system resource x,
represents the expected value, E (SYS), when the occupancy rate of the application A on the system resource x is in the ith value intervalresource|Ac) And y represents the resource occupancy score of the application A to the system resource x.
4. The method of claim 3, wherein the conditional probability table further comprises: when the occupancy rate of each application to the first system resource is in the ith value interval of the n value intervals, the occupancy rates of other applications to the first system resource are in the conditional probability of the kth value interval of the n value intervals; wherein the first system resource is any system resource; the method further comprises the following steps:
according to the conditional probability table, obtaining expected values of a second application about the first system resource when the occupancy rates of the first application to the first system resource are in the n value intervals respectively; wherein the second application is any application other than the first application;
and acquiring the influence degree score of the first application on the second application according to the expected value of the second application about the first system resource when the occupancy rate of the first application on the first system resource is in the n value intervals respectively.
5. The method according to claim 4, wherein the obtaining, according to expected values of a second application with respect to the first system resource when the occupancy rates of the first application on the first system resource are in the n value intervals, a score of the degree of influence of the first application on the second application includes:
when the occupancy rates of the first application to the first system resources are respectively in the n value intervals, the expected value of the second application to the first system resources and the value interval of the first application to the first system resources with the maximum probability distribution are used for obtaining the resource occupancy score of each application to each occupied system resource by using an influence degree score calculation formula;
the influence degree score calculation formula comprises:
wherein resource isxRepresenting system resources x, AiThe state of the application A when the occupancy rate of the system resource x is positioned in the ith value interval of the n value intervals is represented, and the state of the application B when the occupancy rate of the system resource x is positioned in the ith value interval of the n value intervals is representedcThe state that the occupancy rate of the application B to the system resource x is positioned in the c-th value section of the n value sections is represented, the c-th value section is the value section with the maximum probability distribution of the application B to the system resource x,
when the occupancy rate of the application A to the system resource x is in the ith value interval, the expected value of the application B to the system resource x is shown, and y represents the influence degree score of the application A to the application B.
6. The method according to claim 1, wherein the system resources include CPUs, memories, disk IOs and/or network IOs, and the generating a bayesian network from the system resource data includes a topological relation between each system resource and an application, and the topological relation is used for representing the application using each system resource, and includes:
and respectively determining the application using the CPU, the application using the memory, the application using the disk IO and/or the application using the network IO according to the occupancy rate of each application on the CPU, the memory, the disk IO and/or the network IO.
7. An apparatus for determining resource occupancy of an application, the apparatus comprising:
the data acquisition module is used for acquiring system resource data, wherein the system resource data comprises the total occupancy rate of system resources in a preset time period and the occupancy rate of each application to the system resources in the preset time period, and the system resources comprise one or more types;
a network construction module, configured to generate a bayesian network according to the system resource data, where the bayesian network includes a topological relation between each system resource and an application, and the topological relation is used to represent the application using each system resource;
the conditional probability calculation module is used for generating a conditional probability table by using the Bayesian network structure and discretization data obtained after discretization processing is carried out on the system resource data;
the first scoring module is used for determining the resource occupation score of each system resource occupied by each application pair according to the conditional probability table;
the type determining module is used for determining the resource occupation type of each application according to the resource occupation score of each application;
the conditional probability calculation module includes:
the discretization processing submodule is used for discretizing the total occupancy rate of the system resources in the preset time period according to preset m value intervals to obtain first discretization data; wherein the interval ranges of the m value intervals are sequentially increased;
the discretization processing submodule is further configured to perform discretization processing on the occupancy rate of each application to the system resources in the preset time period according to preset n value intervals to obtain second discretization data; the interval ranges of the n value intervals are sequentially increased;
a probability calculation sub-module, configured to perform statistics by using the bayesian network according to the first discretization data and the second discretization data to generate the conditional probability table, where the conditional probability table includes: when the occupancy rate of each application to the first system resource is in the ith value interval of the n value intervals, the total occupancy rate of the first system resource is in the conditional probability of the jth value interval of the m value intervals; wherein the first system resource is any system resource.
8. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
9. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 6.
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