CN117098152A

CN117098152A - Data processing method and device

Info

Publication number: CN117098152A
Application number: CN202210517423.5A
Authority: CN
Inventors: 牛煜霞; 赵嵩
Original assignee: China Telecom Corp Ltd
Current assignee: China Telecom Corp Ltd
Priority date: 2022-05-12
Filing date: 2022-05-12
Publication date: 2023-11-21

Abstract

The embodiment of the application discloses a data processing method and device. According to the data processing method, performance parameters of the data analysis functional entity are measured according to the service processing parameters of the data analysis functional entity, performance analysis can be flexibly performed on the data analysis functional entity with different logic functions, service performance evaluation is performed on the data analysis functional entity according to the performance analysis result, the data analysis functional entity is further processed, service performance of the data analysis functional entity is improved, and unified management of the data analysis functional entity supporting logic function decomposition is achieved.

Description

Data processing method and device

Technical Field

The disclosure relates to the field of communication, and in particular relates to a data processing method and device.

Background

Network data analysis function (network data analytics function, NWDAF) devices are defined in the framework of the fifth generation mobile communication technology (5thgeneration mobile communication technology,5G) network, and the NWDAF devices can perform data acquisition, data analysis and analysis result feedback on service data in the 5G network through standardized communication network flows based on artificial intelligence (artificial intelligence, AI) technology.

In the third generation partnership project (3rd Generation Partnership Project,3GPP) Release 17 (rel 17, rel-17) study, NWDAF can be broken down into analysis logic functions (Analytics Logical Function, anLF) and model training logic functions (Model Training Logical Function, MTLF), allowing operators flexibility in deploying three NWDAF instances: NWDAF devices containing AnLF, NWDAF devices containing MTLF, and NWDAF devices containing both AnLF and MTLF, different NWDAF instances may provide different functions.

Although the deployment of NWDAFs supporting logic function decomposition is more flexible, the corresponding management method is correspondingly more complex, so how to better manage NWDAFs supporting logic function decomposition is a problem to be solved at present.

Disclosure of Invention

In order to solve the technical problems, embodiments of the present application provide a data processing method and apparatus, so as to better manage a data analysis functional entity supporting logic function decomposition.

According to an aspect of the embodiment of the present application, there is provided a data processing method applied to a performance analysis service production end, including: measuring performance parameters of the plurality of data analysis functional entities according to service processing parameters of the plurality of data analysis functional entities; performing performance analysis on the plurality of data analysis functional entities according to the measured values of the performance parameters; and processing the plurality of data analysis functional entities according to the performance analysis results obtained by the performance analysis.

In some embodiments, the traffic handling parameters include: at least one of service request information and service response information; measuring performance parameters of the plurality of data analysis functional entities according to service processing parameters of the plurality of data analysis functional entities, including: collecting service request information received by a data analysis functional entity in a specified period of time and/or collecting service response information generated by the data analysis functional entity in the specified period of time to obtain service processing parameters; generating at least one first performance measurement data for the data analysis functional entity in accordance with the traffic processing parameters; the first performance measurement data is sampled and counted over a specified period of time, generating at least one second performance measurement data for the data analysis functional entity.

In some embodiments, the data analysis functional entity includes at least one of a data analysis functional entity having an analysis logic function, a data analysis functional entity having a model training logic function, and a data analysis functional entity having both an analysis logic function and a model training logic function.

In some implementations, the first performance measurement data and the second performance measurement data each include measurement data associated with a logical function of the data analysis functional entity; generating at least one first performance measurement data for the data analysis functional entity according to the service processing parameters, sampling and counting the first performance measurement data within a specified time period, and generating at least one second performance measurement data for the data analysis functional entity, wherein the method comprises the following steps: if the data analysis functional entity is a data analysis functional entity with an analysis logic function, generating first performance measurement data and second performance measurement data corresponding to the data analysis functional entity with the analysis logic function; if the data analysis functional entity is a data analysis functional entity with a model training logic function, generating first performance measurement data and second performance measurement data corresponding to the data analysis functional entity with the model training logic function; and if the data analysis functional entity is a data analysis functional entity with both the analysis logic function and the model training logic function, generating first performance measurement data and second performance measurement data corresponding to the data analysis functional entity with both the analysis logic function and the model training logic function.

In some implementations, the service request information includes at least one of a service request, a service subscription; the service response information comprises at least one of a service response and a service notification; the parameter type of the business processing parameter comprises at least one of a service request or subscription, a service response or notification, a service success response or notification, a service error response or notification; generating at least one first performance measurement data for the data analysis functional entity based on the traffic processing parameters, comprising: determining an accumulated counter corresponding to the service processing parameter according to the parameter type of the service processing parameter; counting the business processing parameters by using an accumulation counter in a designated period of time to obtain first performance measurement data; if the parameter type of the service processing parameter comprises a service request or subscription, updating the value of an accumulation counter corresponding to the service request or subscription parameter to obtain first performance measurement data; if the parameter type of the service processing parameter comprises a service response or notification, updating the value of an accumulated counter corresponding to the service response or notification parameter to obtain first performance measurement data; if the parameter type of the service processing parameter comprises a service success response or notification parameter, updating the value of an accumulated counter corresponding to the service success response or notification parameter; if the parameter type of the service processing parameter comprises a service error response or notification parameter, updating the value of the accumulated counter corresponding to the service error response or notification parameter.

In some implementations, the first performance measurement data includes at least one of a corresponding number of service requests or subscriptions, a corresponding number of service responses or notifications, a corresponding number of service success responses or notifications, a corresponding number of service error responses or notifications; counting the business processing parameters by using an accumulation counter in a designated period to obtain first performance measurement data, wherein the first performance measurement data comprises: if the data analysis functional entity receives a service request or subscription, the value of a first accumulation counter related to the service request or subscription is increased by 1; if the data analysis functional entity generates a service response or notification, the value of a second accumulation counter related to the service response or notification is increased by 1; if the data analysis functional entity generates a service success response or notification, the value of a third accumulation counter related to the service success response or notification is increased by 1; if the data analysis functional entity generates a service error response or notification, the value of a fourth accumulation counter related to the service error response or notification is increased by 1; the first performance measurement data includes at least one of a value of a first accumulation counter, a value of a second accumulation counter, a value of a third accumulation counter, and a value of a fourth accumulation counter.

In some embodiments, sampling and counting the first performance measurement data over a specified period of time to generate at least one second performance measurement data for the data analysis functional entity, comprising: if the parameter type of the service processing parameter is a service request or subscription, sampling the value of a first accumulation counter corresponding to the service request or subscription in a specified time period to obtain a plurality of sampling performance parameters; if the parameter type of the service processing parameter is a service response or notification, sampling the value of a second accumulation counter corresponding to the service response or notification in a specified time period to obtain a plurality of sampling performance parameters; if the parameter type of the service processing parameter is a service success response or notification, sampling the value of a third accumulation counter corresponding to the service success response or notification in a specified time period to obtain a plurality of sampling performance parameters; and if the parameter type of the service processing parameter is the service error response or notification, sampling the value of the fourth accumulation counter corresponding to the service error response or notification in a specified time period to obtain a plurality of sampling performance parameters.

In some embodiments, sampling and counting the first performance measurement data over a specified period of time to generate at least one second performance measurement data for the data analysis functional entity, comprising: acquiring a statistical strategy matched with the first performance measurement data; if the statistical strategy of the first performance measurement data is an average statistical strategy, calculating an average value of a plurality of sampling performance parameters corresponding to the first performance measurement data, and taking the average value as second performance measurement data corresponding to the first performance measurement data; if the statistical strategy of the first performance measurement data is the maximum statistical strategy, calculating the maximum value of a plurality of sampling performance parameters corresponding to the first performance measurement data, and taking the maximum value as second performance measurement data corresponding to the first performance measurement data; if the statistical strategy of the first performance measurement data is the minimum value statistical strategy, calculating the minimum value of a plurality of sampling performance parameters corresponding to the first performance measurement data, and taking the minimum value as second performance measurement data corresponding to the first performance measurement data.

In some embodiments, the second performance measurement data includes at least one of an average or maximum number or minimum number of service requests or subscriptions, an average or maximum number or minimum number of service responses or notifications, an average or maximum number or minimum number of service success responses or notifications, an average or maximum number or minimum number of service error responses or notifications.

In some embodiments, the first performance measurement data and the second performance measurement data each include measurement data associated with specific information, the specific information including: the single network slice selects at least one of auxiliary information, analysis type information, request type information, subscription type information, service type information, and analysis identification information.

In some embodiments, the method further comprises: generating a performance analysis report from at least one of the first performance measurement data and the second performance measurement data; and sending the performance analysis report to a performance analysis service consumer.

In some embodiments, processing a plurality of data analysis functional entities according to performance analysis results from performance analysis includes: performing fault prediction on a plurality of data analysis functional entities according to the performance analysis result to obtain a fault prediction result; determining a fault solution according to the fault prediction result; and executing the fault solution on the data analysis functional entity.

In some embodiments, processing a plurality of data analysis functional entities according to performance analysis results from performance analysis includes: receiving a data analysis functional entity allocation request, wherein the data analysis functional entity allocation request carries parameter information of data analysis service to be processed; selecting a target data analysis functional entity matched with the parameter information of the data analysis service to be processed according to the performance analysis results of the plurality of data analysis functional entities; and distributing the data analysis service to be processed to the target data analysis functional entity.

In some embodiments, processing a plurality of data analysis functional entities according to performance analysis results from performance analysis includes: respectively acquiring the computing resource use conditions of a plurality of data analysis functional entities according to the performance analysis results; and distributing the computing resources to the plurality of data analysis functional entities according to the use condition of the computing resources.

According to an aspect of an embodiment of the present application, there is provided a data processing method applied to a performance analysis service consumer, including: receiving a measured value of a performance parameter sent by a performance analysis service production end; the measured value of the performance parameter is obtained by measuring the performance parameters of a plurality of data analysis functional entities according to the service processing parameters of the plurality of data analysis functional entities by a performance analysis service production end; performing performance analysis on the plurality of data analysis functional entities according to the measured values of the performance parameters; and processing the plurality of data analysis functional entities according to the performance analysis results obtained by the performance analysis.

According to an aspect of an embodiment of the present application, there is provided a data processing apparatus applied to a performance analysis service production end, including: the performance measurement module is configured to measure the performance parameters of the plurality of data analysis functional entities according to the service processing parameters of the plurality of data analysis functional entities; a first performance analysis module configured to perform performance analysis on the plurality of data analysis functional entities according to the measured values of the performance parameters; the first processing module is configured to process the plurality of data analysis functional entities according to performance analysis results obtained by the performance analysis.

According to an aspect of an embodiment of the present application, there is provided a data processing apparatus applied to a performance analysis service consumption end, including: the performance parameter receiving module is configured to receive the measured value of the performance parameter sent by the performance analysis service production end; the measured value of the performance parameter is obtained by measuring the performance parameters of a plurality of data analysis functional entities according to the service processing parameters of the plurality of data analysis functional entities by a performance analysis service production end; the second performance analysis module is configured to perform performance analysis on the plurality of data analysis functional entities according to the measured value of the performance parameter; and the second processing module is configured to process the plurality of data analysis functional entities according to the performance analysis result obtained by the performance analysis.

According to an aspect of an embodiment of the present application, there is provided a computer-readable storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements the data processing method as above.

According to an aspect of an embodiment of the present application, there is provided an electronic apparatus including: a processor; and a memory for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the data processing method as described above.

According to the technical scheme provided by the embodiment of the application, the performance parameters of the data analysis functional entity are calculated according to the service processing parameters of the data analysis functional entity, so that the data analysis functional entity with different logic functions can be flexibly subjected to performance analysis, the service performance of the data analysis functional entity is evaluated according to the performance analysis result, the data analysis functional entity is further processed, the service performance of the data analysis functional entity is improved, and unified management of the data analysis functional entity supporting logic function decomposition is realized.

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 as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:

FIG. 1 is a flow chart of a data processing method shown in an exemplary embodiment of the application;

FIG. 2 is a schematic diagram of an application scenario of a data processing method according to an exemplary embodiment of the present application;

FIG. 3 is a flow chart of a data processing method shown in another exemplary embodiment of the application;

FIG. 4 is a schematic diagram illustrating sampling of performance parameters according to an exemplary embodiment of the present application;

FIG. 5 is a flow chart of a data processing method shown in another exemplary embodiment of the application;

fig. 6 is an interaction diagram illustrating load balancing of multiple NWDAF devices in accordance with an exemplary embodiment of the present application;

FIG. 7 is a flow chart of a data processing method shown in another exemplary embodiment of the application;

FIG. 8 is an interaction diagram of a data processing method shown in an exemplary embodiment of the application;

FIG. 9 is a schematic diagram of a data processing apparatus shown in accordance with an exemplary embodiment of the present application;

FIG. 10 is a schematic diagram of a data processing apparatus shown in accordance with another exemplary embodiment of the present application;

fig. 11 is a schematic diagram of a computer system of an electronic device according to an exemplary embodiment of the present application.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations identical to the present application. Rather, they are merely examples of apparatus and methods that are identical to some aspects of the present application as detailed in the appended claims.

The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.

In the present application, the term "plurality" means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., a and/or B may represent: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.

In the eNA (Study of Enablers for Network Automation for G) subject of 3GPP Release 16 (Release 16, rel-16), the concept of NWDAF is proposed, where NWDAF is used to perform a Network data analysis service, and specific data can be collected from a specific Network Function (NF), a Network management system (Operation Administration and Maintenance, OAM) or an application Function (Application Function, AF), and a certain analysis result is obtained through a certain big data analysis Network element, and the result is sent to a specific NF, OAM or AF as required, where NWDAF is a very critical Network Function for implementing Network intelligence. Further, in Rel-17, NWDAF is decomposed into AnLF and MTLF, so that three NWDAF instances, namely NWDAF devices including AnLF, NWDAF devices including MTLF and NWDAF devices including both AnLF and MTLF, can be deployed in an actual application scene, thereby improving flexibility of NWDAF deployment.

However, although the above solution can improve flexibility of NWDAF deployment, the difficulty of managing NWDAF is further improved, so that evaluation and optimization of service performance of NWDAF are affected.

Therefore, the application provides a data processing method, and an execution subject of the method is a performance analysis service production end. As shown in fig. 1, the method at least includes steps S110 to S130, and is described in detail as follows:

step S110, measuring the performance parameters of the data analysis functional entities according to the service processing parameters of the data analysis functional entities.

It should be noted that, the service processing parameter is used for characterizing the relevant parameter of the data analysis functional entity for processing the network data analysis service, and the performance parameter is used for characterizing the performance of the data analysis functional entity.

It can be understood that the data analysis functional entity in the embodiment of the present application may be NWDAF equipment, or other data analysis equipment, which is not particularly limited in the present application. The following embodiments illustrate the present application by taking the NWDAF device as an example of a data analysis functional entity.

The performance analysis service production end sends a service processing parameter acquisition request to the NWDAF device, so that the NWDAF device feeds back the service processing parameter to the performance analysis service production end after receiving the service processing parameter acquisition request; the method and the device can also report the own service processing parameters to the active performance analysis service production end of the NWDAF device, for example, the NWDAF device reports the service processing parameters once every preset time interval, or the NWDAF device reports the service processing parameters once every time the NWDAF device performs the processing of the network data analysis service.

For example, as shown in fig. 2, the performance analysis service consumer is communicatively connected to a performance analysis service production end, where the performance analysis service consumer is used for characterizing a device that has a performance analysis requirement on NWDAF devices, the performance analysis service production end is communicatively connected to n NWDAF devices, where n is an integer greater than or equal to 1, and the NWDAF devices may be one or more NWDAF devices including antf, NWDAF devices including MTLF, and NWDAF devices including both antf and MTLF.

After receiving the performance analysis request sent by the performance analysis service consumer, the performance analysis service production end sends a service processing parameter acquisition request to each NWDAF device so as to obtain service processing parameters fed back by each NWDAF device according to the service processing parameter acquisition request, and further, performance parameter measurement is performed according to the service processing parameters.

Step S120, performing performance analysis on the plurality of data analysis functional entities according to the measured values of the performance parameters.

The performance analysis is used for analyzing the performance of the NWDAF device in the preset time period when the NWDAF device performs data analysis on the network data analysis service.

For example, performance analysis may be performed on measurement values of performance parameters of the plurality of data analysis functional entities according to performance indexes, where the performance indexes may be pre-configured in a performance analysis service production end, or may be carried in a performance analysis request sent by a performance analysis service consumption end, which is not particularly limited in the present application. The performance index specifically comprises the value (including the value range) of the measured value of the performance parameter, and the performance analysis result can be obtained according to the relation between the measured value of the performance parameter and the corresponding performance index.

For example, the measured value of the performance parameter is an average analysis duration of data analysis of the NWDAF device on the network data analysis service in a specified period of time, and when the measured value of the performance parameter is greater than the average analysis duration in the performance index, it indicates that the data analysis time of the NWDAF device is too slow, that is, the performance analysis result of the measured value of the performance parameter is that the data analysis time of the NWDAF device is too slow.

Optionally, the measured value of the performance parameter can be subjected to data preprocessing according to the performance evaluation condition to obtain the preprocessed performance parameter, and further, the performance analysis is performed on the preprocessed performance parameter to obtain a performance analysis result. Wherein the performance evaluation condition comprises one or more of the following: performance data statistical granularity, performance data category, performance data screening condition.

The performance data statistical granularity is used for indicating granularity of performance parameters to be analyzed, and the performance data statistical granularity comprises one or more of the following: regional level, cell level, grid level, network element level, network slice level, sub-network level, network slice traffic level, tenant level, user level. For example, if the statistical granularity of the performance data is at the area level, it indicates that the performance analysis service production end needs performance measurement data of each NWDAF device at the area level granularity.

The performance data class is used for indicating the class of the performance parameters to be analyzed, if the performance data class selects auxiliary information (Single Network Slice Selection Assistance Information, S-NSSAI) for the network slice, the performance data class indicates that the performance analysis service production end needs performance measurement data of each NWDAF device associated with the S-NSSAI; if the performance data type is analysis identification information (Analytics Identity Document, analysis ID), it indicates that the performance measurement data of each NWDAF device associated with the analysis ID is needed by the performance analysis service production end.

The performance data screening conditions are used for indicating screening conditions which need to be met by the performance parameters to be analyzed, and the performance data screening conditions can comprise network problem conditions, time range conditions and the like. For example, if the performance data filtering condition includes a network problem condition, where the network problem condition is that the data processing resources are insufficient, the performance data filtering condition indicates that the performance analysis service production end needs corresponding performance parameters when the data processing resources of each NWDAF device are insufficient. For another example, if the performance data screening condition includes a time range condition, the time range condition is a weekly sunday, it indicates that the performance analysis service production end needs performance parameters from sunday 0 to 24.

And step S130, processing the plurality of data analysis functional entities according to the performance analysis result obtained by the performance analysis.

The purpose of processing the multiple NWDAF devices is to improve the service performance of the NWDAF devices, and may be load balancing the NWDAF devices, or performing fault prediction and fault resolution on the NWDAF devices, which is not particularly limited in the present application.

The load of the NWDAF device is used for measuring the current working state of the NWDAF device, and is an index reflecting the busyness of the NWDAF device. It can be appreciated that the lower the busyness of the NWDAF device, the lower the load of the NWDAF device is indicated; the higher the busyness of the NWDAF device, the higher the load of the NWDAF device. When the load of the NWDAF device is greater than the load preset index, it indicates that the NWDAF device load is higher than the standard value, so that load balancing is required for the NWDAF device.

Load balancing the NWDAF device means that the NWDAF device with the busyness greater than the preset index is subjected to load reduction so as to realize the load balancing of the NWDAF device.

In some embodiments, processing a plurality of data analysis functional entities according to performance analysis results from performance analysis includes: respectively acquiring the computing resource use conditions of the data analysis functional entities according to the performance analysis results; and distributing the computing resources to the plurality of data analysis functional entities according to the computing resource use condition.

For example, the method of performing load reduction on the NWDAF device with the load higher than the load preset index may be to allocate computing resources to the NWDAF device, for example, allocate idle computing resources of the NWDAF device with the load lower than the load preset index to the NWDAF device with the load higher than the load preset index, so as to improve the resource utilization of the NWDAF device.

In some embodiments, NWDAF services in NWDAF devices with loads higher than the load preset index may be reassigned to NWDAF devices with loads lower than the load preset index, so as to ensure processing efficiency of the NWDAF services; and the method can uniformly allocate all the NWDAF services so as to uniformly control the quantity of the NWDAF services processed by a plurality of the NWDAF devices and avoid the unbalanced load of the NWDAF devices caused by unreasonable allocation of the NWDAF services.

According to the data processing method, the performance analysis service production end is connected with the plurality of NWDAF devices, performance analysis can be flexibly conducted on the NWDAF devices with different logic functions, further service performance of the NWDAF devices is evaluated, performance problems of the NWDAF devices are detected according to evaluation results, the NWDAF devices are processed according to the performance problems, the service performance of the NWDAF devices is improved, and unified management of the NWDA devices supporting logic function decomposition is achieved.

In some implementations, referring to fig. 3, fig. 3 is a flowchart illustrating a data processing method according to another exemplary embodiment, where the service processing parameters include: at least one of service request information and service response information; in step S110 of fig. 1, performance parameters of a plurality of data analysis functional entities are measured according to service processing parameters of the plurality of data analysis functional entities, including steps S111 to S113.

Step S111, service request information received by the data analysis functional entity is collected in a specified period, and/or service response information generated by the data analysis functional entity is collected in the specified period, so as to obtain service processing parameters.

Optionally, the service processing parameter is a service processing condition of each NWDAF device, and the type of the service processing parameter includes: the number of service requests received by each NWDAF device, the number of service responses sent, etc. The NWDAF device sends a service request to the NWDAF device, wherein the service request is a data analysis request sent by the NWDAF service request end to the NWDAF device, and the NWDAF device feeds back a service response to the NWDAF service request end according to the service request.

And collecting service request information received by the data analysis functional entity in a specified period of time and/or collecting service response information generated by the data analysis functional entity in the specified period of time to obtain service processing parameters. The specified time period may be carried in a performance analysis request sent by the performance analysis service consumer end to the performance analysis service production end.

Step S112, generating at least one first performance measurement data for the data analysis functional entity according to the service processing parameters.

The performance analysis service production end obtains first performance measurement data corresponding to each NWDAF device according to the service processing parameters, for example, records the service processing parameters of each NWDAF device, and obtains the first performance measurement data corresponding to each NWDAF device.

Step S113, sampling and counting the first performance measurement data in a specified period of time, and generating at least one second performance measurement data for the data analysis functional entity.

The performance analysis service production end can obtain second performance measurement data of each NWDAF device in a specified time period according to the first performance measurement data corresponding to each NWDAF device, where the second performance measurement data is used for characterizing a processing condition when the NWDAF device performs data analysis on the network data analysis service in the specified time period.

Optionally, the performance analysis request sent by the performance analysis service consumer side carries a designated time period and a sampling time interval for performing performance analysis on the NWDAF device, and the performance analysis service production side samples the NWDAF device according to the performance analysis request in the designated time period at a preset time interval to obtain a sampling result. And then, counting the sampling results to obtain second performance measurement data of each NWDAF device in a specified time period.

For example, the performance analysis request sent by the performance analysis service consumer side carries that the designated time period for performing performance analysis on the NWDAF device is 9 to 10 am today, and the sampling time interval is 5 minutes, so that the performance analysis service production side samples at a preset time interval in the designated time period to obtain 12 sampling results. Then, the 12 sampling results are counted to obtain second performance measurement data of the NWDAF device in 9 to 10 am.

Alternatively, a formula or an algorithm for counting the first performance measurement data may be preconfigured, and if the performance analysis server production side receives the performance analysis request sent by the performance analysis server consumption side, the first performance measurement data may be counted according to the preconfigured statistical formula. For example, an average formula of the service request is preconfigured in the performance analysis service production end, and then the average value of the service request received by the NWDAF device in a preset period of time can be calculated by using the preconfigured formula.

Optionally, a formula or algorithm for counting the first performance measurement data may be carried in a performance analysis request sent by the performance analysis service consumer, and the performance analysis service production end obtains a statistical formula according to the received performance analysis request, and may count the first performance measurement data according to the statistical formula. For example, the performance analysis request carries a formula for obtaining the maximum value of the service request, and the NWDAF device may calculate the maximum value of the service request received in the preset time period by using the formula carried in the request.

In some implementations, the first performance measurement data and the second performance measurement data each include measurement data associated with a logical function of the data analysis functional entity; generating at least one first performance measurement data for the data analysis functional entity according to the service processing parameters, sampling and counting the first performance measurement data within a specified time period, and generating at least one second performance measurement data for the data analysis functional entity, wherein the method comprises the following steps:

if the data analysis functional entity is a data analysis functional entity with an analysis logic function, generating first performance measurement data and second performance measurement data corresponding to the data analysis functional entity with the analysis logic function;

if the data analysis functional entity is a data analysis functional entity with a model training logic function, generating first performance measurement data and second performance measurement data corresponding to the data analysis functional entity with the model training logic function;

And if the data analysis functional entity is a data analysis functional entity with both the analysis logic function and the model training logic function, generating first performance measurement data and second performance measurement data corresponding to the data analysis functional entity with both the analysis logic function and the model training logic function.

It is understood that NWDAF devices include a plurality of types, such as NWDAF devices including AnLF, NWDAF devices including MTLF, and NWDAF devices including both AnLF and MTLF, and that the traffic data handled by each NWDAF device includes a plurality of types, such as traffic data for S-NSSAI and traffic data for Analytics ID. Therefore, the performance analysis service production end is instructed to flexibly acquire the service processing parameters of the plurality of NWDAF devices through the data analysis functional entity identifier and the service identifier, so that the data processing efficiency is improved.

For example, according to the data analysis functional entity identifier, the NWDAF device to be analyzed, which needs to perform performance parameter analysis, is identified, and then the NWDAF device to be analyzed is removed to obtain the service processing parameter to be analyzed, which corresponds to the service identifier.

For example, the performance analysis request sent by the performance analysis service production end to the performance analysis service production end carries a data analysis function entity identifier, and if the performance analysis service production end determines that the data analysis function entity to be analyzed is a data analysis function entity with an analysis logic function according to the data analysis function entity identifier in the performance analysis request, first performance measurement data and second performance measurement data corresponding to the data analysis function entity with the analysis logic function are generated. And if the performance analysis service production end determines that the data analysis functional entity to be analyzed is the data analysis functional entity with the model training logic function according to the data analysis functional entity identification in the performance analysis request, generating first performance measurement data and second performance measurement data corresponding to the data analysis functional entity with the model training logic function. If the performance analysis service production end determines that the data analysis functional entity to be analyzed is the data analysis functional entity with the analysis logic function and the model training logic function according to the data analysis functional entity identification in the performance analysis request, generating first performance measurement data and second performance measurement data corresponding to the data analysis functional entity with the analysis logic function and the model training logic function.

In some implementations, the service request information includes at least one of a service request, a service subscription; the service response information comprises at least one of a service response and a service notification; the parameter type of the business processing parameter comprises at least one of a service request or subscription, a service response or notification, a service success response or notification, a service error response or notification; generating at least one first performance measurement data for the data analysis functional entity based on the traffic processing parameters, comprising:

determining an accumulated counter corresponding to the service processing parameter according to the parameter type of the service processing parameter;

counting the business processing parameters by using an accumulation counter in a designated period of time to obtain first performance measurement data;

if the parameter type of the service processing parameter comprises a service request or subscription, updating the value of an accumulation counter corresponding to the service request or subscription parameter to obtain first performance measurement data;

if the parameter type of the service processing parameter comprises a service response or notification, updating the value of an accumulated counter corresponding to the service response or notification parameter to obtain first performance measurement data;

if the parameter type of the service processing parameter comprises a service success response or notification parameter, updating the value of an accumulated counter corresponding to the service success response or notification parameter;

If the parameter type of the service processing parameter comprises a service error response or notification parameter, updating the value of the accumulated counter corresponding to the service error response or notification parameter.

The NWDAF device sends a service request to the NWDAF device, wherein the service request is a data analysis acquisition request sent by the NWDAF service request end to the NWDAF device, and the NWDAF device feeds back a service response to the NWDAF service request end according to the service request; the service subscription means that the NWDAF device sends a data acquisition request to the NWDAF service request terminal, the NWDAF service request terminal collects and processes corresponding service data according to the NWDAF service subscription request, and further uploads the obtained service data to the NWDAF device for data analysis, and the NWDAF device sends a service notification to the NWDAF service request terminal according to a data analysis result; the service success response refers to a service response which points to the NWDAF service request end and feeds back success; the service error response is a service response that points to the NWDAF service request end to feed back failure.

And confirming the parameter type of the service processing parameters, and updating the value of the accumulation counter corresponding to each service processing parameter according to the parameter type to obtain first performance measurement data.

In some implementations, the first performance measurement data includes at least one of a corresponding number of service requests or subscriptions, a corresponding number of service responses or notifications, a corresponding number of service success responses or notifications, a corresponding number of service error responses or notifications; counting the business processing parameters by using an accumulation counter in a designated period to obtain first performance measurement data, wherein the first performance measurement data comprises:

If the data analysis functional entity receives a service request or subscription, the value of a first accumulation counter related to the service request or subscription is increased by 1;

if the data analysis functional entity generates a service response or notification, the value of a second accumulation counter related to the service response or notification is increased by 1;

if the data analysis functional entity generates a service success response or notification, the value of a third accumulation counter related to the service success response or notification is increased by 1;

if the data analysis functional entity generates a service error response or notification, the value of a fourth accumulation counter related to the service error response or notification is increased by 1;

the first performance measurement data includes at least one of a value of a first accumulation counter, a value of a second accumulation counter, a value of a third accumulation counter, and a value of a fourth accumulation counter.

For example, the performance analysis service production end is provided with first performance measurement data reqsubnbrnnwdaf, where the first performance measurement data includes a value of a first accumulation counter corresponding to an NWDAF service discovery request and an NWDAF service subscription request received by each NWDAF device. The method may be that, whenever the NWDAF device receives a service request and a service subscription, the NWDAF device sends a request receiving feedback to the performance analysis service production end, where the request receiving feedback carries an identifier of the NWDAF device and a type identifier of a received content (service request or service subscription), and the performance analysis service production end increments a value of a corresponding first accumulation counter in the first performance measurement data reqsubnbrnnwdaf by 1 according to the identifier of the NWDAF device and the type identifier of the received content in the request receiving feedback.

For example, the performance analysis service production end is provided with first performance measurement data responsennotifynbrnwdaf, where the first performance measurement data includes a second cumulative counter corresponding to a service response and a service notification sent by each NWDAF device. The method may be that, when the NWDAF device sends a service response and a service notification once, the response and notification feedback is sent to the performance analysis service production end, where the response and notification feedback carries an identifier of the NWDAF device and a type identifier of a sending content (service response or service notification), and the performance analysis service production end increments a value of a corresponding second accumulation counter in the first performance measurement data responsennotifynbrnwdaf by 1 according to the identifier of the NWDAF device and the type identifier of the sending content in the response and notification feedback.

For example, the performance analysis service production end is provided with first performance measurement data sucresponsennotifynbrnwdaf, where the first performance measurement data includes a service success response and a third accumulation counter corresponding to a service success notification sent by each NWDAF device. And each time the NWDAF device executes a service success response and a service success notification, sending success feedback to the performance analysis service production end, wherein the success feedback carries the identification of the NWDAF device and the type identification of the success feedback (the service success response and the service success notification), and the performance analysis service production end increases the value of a corresponding third accumulation counter in the first performance measurement data sucresponsennotifynbrnwdaf by 1 according to the identification of the NWDAF device in the success feedback and the type identification of the success feedback.

For example, the performance analysis service production end is provided with first performance measurement data errresponse notifynbrnwdaf, where the first performance measurement data includes a service error response and a sub-accumulation counter corresponding to a service error notification sent by each NWDAF device. The method may include that when the NWDAF device executes a service error response and a service error notification once, an error feedback is sent to the performance analysis service production end, where the error feedback carries an identifier of the NWDAF device and a type identifier of the error feedback (service error response and service error notification), and the performance analysis service production end increments a value of a corresponding fourth accumulation counter in the first performance measurement data errresponse notifynbrnwdaf by 1 according to the identifier of the NWDAF device in the error feedback and the type identifier of the error feedback.

In some embodiments, sampling and counting the first performance measurement data over a specified period of time to generate at least one second performance measurement data for the data analysis functional entity, comprising:

if the parameter type of the service processing parameter is a service request or subscription, sampling the value of a first accumulation counter corresponding to the service request or subscription in a specified time period to obtain a plurality of sampling performance parameters;

If the parameter type of the service processing parameter is a service response or notification, sampling the value of a second accumulation counter corresponding to the service response or notification in a specified time period to obtain a plurality of sampling performance parameters;

if the parameter type of the service processing parameter is a service success response or notification, sampling the value of a third accumulation counter corresponding to the service success response or notification in a specified time period to obtain a plurality of sampling performance parameters;

and if the parameter type of the service processing parameter is the service error response or notification, sampling the value of the fourth accumulation counter corresponding to the service error response or notification in a specified time period to obtain a plurality of sampling performance parameters.

The sampling performance parameter refers to the specific content of the accumulation counter at a certain moment. For example, as shown in fig. 4, the preset time interval is 1 minute, the designated time period is from the start time t1 to the end time t2, and after every 1 minute from the time t1, the content of the accumulation counter is collected once to obtain a sampling performance parameter, and the sampling performance parameter obtained by collection and the collection time are recorded. Until the current moment is not in the appointed time period, n sampling performance parameters at different moments are obtained as follows: parameter 1, parameter 2, parameter 3,..parameter n-1, parameter n.

Further, statistics is performed on the plurality of sampling performance parameters, so as to obtain second performance measurement data of the plurality of NWDAF devices. For example, when the sampling performance parameter is a numerical value, the statistical method is to calculate the numerical value, such as average value calculation, median value calculation, and the like. It can be understood that, according to the different data types of the sampling performance parameters, a corresponding statistical method can be selected to perform statistics on the sampling performance parameters, so as to generalize the information of the performance parameters in the specified time period to obtain the second performance measurement data.

acquiring a statistical strategy matched with the first performance measurement data;

if the statistical strategy of the first performance measurement data is an average statistical strategy, calculating an average value of a plurality of sampling performance parameters corresponding to the first performance measurement data, and taking the average value as second performance measurement data corresponding to the first performance measurement data;

if the statistical strategy of the first performance measurement data is the maximum statistical strategy, calculating the maximum value of a plurality of sampling performance parameters corresponding to the first performance measurement data, and taking the maximum value as second performance measurement data corresponding to the first performance measurement data.

If the statistical strategy of the first performance measurement data is the minimum value statistical strategy, calculating the minimum value of a plurality of sampling performance parameters corresponding to the first performance measurement data, and taking the minimum value as second performance measurement data corresponding to the first performance measurement data.

For example, a statistical policy matched with the first performance measurement data may be obtained according to a parameter type of the first performance measurement data, where the parameter type is used to characterize category information of the first performance measurement data, and the statistical policy refers to a specific method used for performing statistics on the sampled performance parameter.

For example, the first performance measurement data may be divided into different parameter types according to NWDAF devices, for example, the parameter type of the first performance measurement data in NWDAF devices including AnLF is an analysis logic function class; the parameter type of the first performance measurement data in the NWDAF equipment containing the MTLF is a model training logic function class; the parameter type of the first performance measurement data in the NWDAF device containing both AnLF and MTLF is an analysis and model training logic function class.

It will be appreciated that the parameter type of the first performance measurement data may be selected according to the actual application, and the present application is not limited herein.

After the parameter type of the first performance measurement data is determined, a statistical strategy matched with the parameter type is obtained. For example, the performance analysis service production end may store a parameter type and a statistical policy mapping table, and query the parameter type and the statistical policy mapping table according to the parameter type of the first performance measurement data to obtain a matched statistical policy.

Optionally, if the statistical policy of the performance parameters is an average statistical policy, an average value of a plurality of sampled performance parameters corresponding to the first performance measurement data is calculated, and the average value is used as second performance measurement data corresponding to the first performance measurement data.

For example, calculating a plurality of sampled performance parameters for a first performance measurement data having a parameter type of service request or service subscription may be implemented using the following formula:

wherein, reqSubNbrNW DAFMean is the second performance measurement data with the parameter type of service request or service subscription _i And the sampling performance parameters are obtained by sampling the first performance measurement data with the parameter type of service request or service subscription in a specified time period, and n is the number of the sampling performance parameters.

For example, calculating the second performance measurement data with the parameter type of service response or service notification may be implemented using the following formula:

wherein, responsennotifyNbrNW DAFMean is the second performance measurement data with the parameter type of service response or service notification _i And the sampling performance parameters corresponding to the performance parameters are obtained by sampling first performance measurement data with the parameter type of service response or service notification in a specified time period, and n is the number of the sampling performance parameters.

For example, calculating the second performance measurement data with the parameter type of service success response or service success notification may be implemented using the following formula:

wherein, the SucResponseNotifyNcrNW DAFMeanFor the second performance measurement data of which the parameter type is a service success response or a service success notification, sucResponseNotifyNcrNWDAF _i And the sampling performance parameters corresponding to the performance parameters are obtained by sampling first performance measurement data with the parameter type of service success response or service success notification in a specified time period, and n is the number of the sampling performance parameters.

For example, calculating the second performance measurement data with the parameter type of service error response or service error notification may be implemented using the following formula:

wherein ErrResponseNotifyNbrNWDAFMean is second performance measurement data with parameter type of service error response or service error notification _i And the sampling performance parameters corresponding to the performance parameters are obtained by sampling first performance measurement data with the parameter type of service error response or service error notification in a specified time period, and n is the number of the sampling performance parameters.

Optionally, if the statistical policy of the first performance measurement data is a maximum statistical policy, calculating a maximum value of a plurality of sampling performance parameters corresponding to the first performance measurement data, and taking the maximum value as second performance measurement data corresponding to the first performance measurement data. If the statistical strategy of the first performance measurement data is the minimum value statistical strategy, calculating the minimum value of a plurality of sampling performance parameters corresponding to the first performance measurement data, and taking the minimum value as second performance measurement data corresponding to the first performance measurement data.

For example, calculating the second performance measurement data with the parameter type of service request or service subscription may be implemented by the following formula:

ReqSubNbrNWDAFMax＝max(ReqSubNbrNWDAF _i ),1≤i≤n

Wherein, reqSubNbrNWDAFMax is a parameter type of service requestOr second performance measurement data corresponding to the first performance measurement data of the service subscription, reqsubnbrnnwdaf _i And the sampling performance parameters are obtained by sampling the first performance measurement data with the parameter type of service request or service subscription in a specified time period, and n is the number of the sampling performance parameters.

ResponseNotifyNbrNWDAFMax＝max(ResponseNotifyNbrNWDAF _i ),1≤i

≤n

wherein, responsennotifynbrnwdafmax is the second performance measurement data with the parameter type of service response or service notification, responsennotifynbrnwdaf _i And the sampling performance parameters corresponding to the first performance measurement data are obtained by sampling the first performance measurement data with the parameter type of service response or service notification in a specified time period, and n is the number of the sampling performance parameters.

SucResponseNotifyNbrNWDAFMax

＝max(SucResponseNotifyNbrNWDAF _i ),1≤i≤n

Wherein, the SucResponseNotifyNcrNWDAFMax is the second performance measurement data with parameter type of service success response or service success notification _i And sampling the first performance measurement data corresponding to the first performance measurement data, wherein the sampling performance parameters are obtained by sampling the first performance measurement data with the parameter type of service success response or service success notification in a specified time period, and n is the number of the sampling performance parameters.

For example, calculating the second performance measurement data with a parameter type of service error response or service error notification class may be implemented using the following formula:

ErrResponseNotifyNbrNWDAFMax

＝max(ErrResponseNotifyNbrNWDAF _i ),1≤i≤n

wherein ErrResponseNotifyNbrNWDAFMax is second performance measurement data with parameter type of service error response or service error notification type _i And the sampling performance parameters corresponding to the first performance measurement data are obtained by sampling the first performance measurement data with the parameter type of service error response or service error notification in a specified time period, and n is the number of the sampling performance parameters.

Further, second performance measurement data corresponding to the first performance measurement data contained in each NWDAF device are summarized, and the second performance measurement data of each NWDAF device are obtained.

If the performance measurement is for S-nsai, the performance measurement may include performance measurement data for each NWDAF associated with S-nsai, respectively; if the performance measure is for analysis type information, request type information, or subscription type information, the performance measure may include performance measure data for each NWDAF associated with each analysis type information, request type information, or subscription type information, respectively; if the performance measurements are for an analysis ID, the performance measurements may include performance measurement data for each NWDAF associated with the analysis ID, respectively.

In some embodiments, a process for performing a performance analysis on a plurality of data analysis functional entities based on measured values of performance parameters includes: performing numerical exception analysis on the parameter item of the second performance measurement data to obtain a parameter item numerical analysis result; carrying out numerical variation trend analysis of parameter items on the second performance measurement data according to the historical second performance measurement data to obtain a numerical variation trend analysis result of the parameter items; and determining a performance analysis result according to the parameter item numerical analysis result and the parameter item numerical variation trend analysis result.

The performance analysis results are used to characterize whether anomalies exist in the individual parameter items in the second performance measurement data.

The second performance measurement data comprises at least one parameter item, numerical value exception analysis is carried out on the numerical value corresponding to each parameter item, so that abnormal parameter items in the second performance measurement data are identified, and a parameter item numerical value analysis result is obtained. The performance analysis service production end stores a normal value range of the value corresponding to each parameter item of the second performance measurement data, and if the value corresponding to the parameter item is detected to be in the normal value range of the value corresponding to the parameter item, the parameter item value analysis result of the parameter item is a normal result; if the numerical value corresponding to the parameter item is detected not to be in the normal numerical value range of the numerical value corresponding to the parameter item, the numerical analysis result of the parameter item is an abnormal result.

Further, each time the performance analysis service production end generates a second performance measurement data, the second performance measurement data and the generation time association of the second performance measurement data are stored in a historical second performance measurement data set, and the historical second performance measurement data are obtained by querying the historical second performance measurement data set. And then, carrying out numerical variation trend analysis on the parameter item according to the historical second performance measurement data to obtain a numerical variation trend analysis result of the parameter item. For example, the respective historical second performance measurement data are ranked based on the generation time of the respective historical second performance measurement data, and a numerical variation trend of each parameter item of the ranked historical second performance measurement data is determined, wherein the numerical variation trend includes an increasing trend or a decreasing trend.

According to the embodiment of the application, the abnormal parameter item in the second performance measurement data is obtained by combining the parameter item numerical analysis result and the parameter item numerical variation trend analysis result, so that the performance analysis result is generated according to the abnormal parameter item.

For example, according to the parameter item numerical analysis result of each parameter item in the second performance measurement data, obtaining the parameter item with abnormality in the second performance measurement data currently; predicting the value of each parameter item at the next moment according to the parameter item value change trend of each parameter item in the second performance measurement data, and carrying out value anomaly analysis on the value of the parameter item at the next moment to obtain a parameter item which possibly has anomaly at the next moment in the second performance measurement data.

In some embodiments, processing the plurality of data analysis functional entities according to the performance analysis results obtained by the performance analysis includes: performing fault prediction on a plurality of data analysis functional entities according to the performance analysis result to obtain a fault prediction result; determining a fault solution according to the fault prediction result; and executing the fault solution on the data analysis functional entity.

It should be noted that, the performance analysis service production end stores a fault solution, performs fault prediction according to the performance analysis result, determines whether the NWDAF device corresponding to the performance analysis result has a fault, so as to obtain a fault prediction result, and then sends the fault prediction result matched with the fault solution to the NWDAF device, so that the NWDAF device executes the fault solution to eliminate the fault of each NWDAF device.

In some implementations, referring to fig. 5, fig. 5 is a flowchart illustrating a data processing method according to another exemplary embodiment, where a process of processing a plurality of data analysis functional entities according to a performance analysis result obtained by a performance analysis in step S130 may include steps S131 to S133:

In step S131, a data analysis functional entity allocation request is received, where the data analysis functional entity allocation request carries parameter information of the data analysis service to be processed.

The data analysis service to be processed may be sent by the NWDAF device to the performance analysis service production end, for example, the NWDAF device with a load exceeding a load threshold sends the data analysis service to be processed to the performance analysis service production end, so that the performance analysis service production end redistributes the data analysis service to be processed, and load balance is achieved on the NWDAF device; the data analysis service may also be that an NWDAF service request end with NWDAF service requirements is sent to a performance analysis service production end, so that the performance analysis service production end distributes the NWDAF service according to the performance analysis result of each NWDAF device, and further load balance is achieved for the NWDAF device. The NWDAF service request end includes, but is not limited to, NF equipment, OAM equipment, or AF equipment.

Step S132, selecting a target data analysis functional entity matched with the parameter information of the data analysis service to be processed according to the performance analysis results of the plurality of data analysis functional entities.

Based on the NWDAF service discovery request, the performance analysis service production end obtains a target NWDAF device which meets parameter information carried in the data analysis service discovery request.

The profile information of the corresponding NWDAF devices in the NWDAF devices registered on the profile information is stored in the performance analysis service production end, the profile information comprises the information such as the S-nsai of the network slice corresponding to the corresponding NWDAF device, the analysis ID of each supported network data analysis service, the service area information and the like, the performance analysis result of the corresponding NWDAF device is used for representing the performance parameter information of each network data analysis service supported by the NWDAF device, and the performance parameter information of the NWDAF device on different network data analysis services can be used for representing the performance of the NWDAF device on the different network data analysis services.

The network slice corresponding to the NWDAF device may be determined according to the S-NSSAI corresponding to the NWDAF device, and the type of the network data analysis service supported by the NWDAF device may be determined according to the analysis ID corresponding to the NWDAF device, which may be understood that each NWDAF device may support one or more types of network data analysis services. The service area information of the NWDAF device may be used to determine a service area of the NWDAF device, e.g., the service area information may be tracking area list information.

Step S133, distributing the data analysis service to be processed to the target data analysis functional entity.

Generating an NWDAF service discovery response according to the performance parameter information of the target NWDAF device, and sending the NWDAF service discovery response to the NWDAF service request terminal.

The NWDAF service request terminal receives the NWDAF service discovery response, determines NWDAF equipment to be requested according to performance parameter information corresponding to each target NWDAF equipment in the NWDAF service discovery response, and sends a service request to the NWDAF equipment to be requested, wherein the service request contains other parameter information such as analysis ID of the requested network data analysis service.

After the NWDAF device receives the service request, data analysis is performed on service data corresponding to the service request, a service response is generated according to the data analysis result, and the service response is sent to the NWDAF service request terminal. The NWDAF device may collect network operation data from the core network control plane network function NF, obtain terminal and network related statistics data from the OAM, obtain application data from the AF to obtain service data according to the network data analysis service that needs to be processed, then analyze the service data, and feed back the analysis result to the NF device, the OAM device, or the AF device.

For example, referring to fig. 6, fig. 6 is an interaction diagram of load balancing processing for multiple NWDAF devices, including: step S610, the performance analysis service production end performs performance analysis according to the service processing parameters of each NWDAF device to obtain performance analysis results corresponding to n NWDAF devices respectively, wherein n is an integer greater than 1; step S620, the NWDAF service request end sends an NWDAF service discovery request to the performance analysis service production end, where the NWDAF service discovery request includes parameter information of the network data analysis service requested by the NWDAF service request end; step S630, the performance analysis service production end obtains a plurality of target NWDAF devices according to the parameter information of the network data analysis service and the performance analysis results corresponding to each NWDAF device respectively, and feeds back an NWDAF service discovery response to the NWDAF service request end according to the plurality of target NWDAF devices; step S640, the NWDAF service request terminal determines that the NWDAF device to be requested is NWDAF-1 from the multiple target NWDAF devices, and sends a service request to NWDAF-1; step S650, NWDAF-1 performs data analysis according to the service request, and sends a service response to the NWDAF service request terminal according to the data analysis result.

The performance analysis service production end stores the profile information and the performance parameter information of each NWDAF device, so that the performance analysis service production end can allocate proper NWDAF devices to the received NWDAF service discovery request, and the performance analysis service production end uniformly manages each NWDAF device. Meanwhile, the performance analysis service production end can select NWDAF equipment with smaller load as the target NWDAF equipment when acquiring the target NWDAF equipment, so that the load balance of each NWDAF equipment is ensured.

In some embodiments, the data processing method further comprises: generating a performance analysis report from at least one of the first performance measurement data and the second performance measurement data; and sending the performance analysis report to a performance analysis service consumer.

In some implementations, referring to fig. 7, fig. 7 is a flowchart of a data processing method according to another exemplary embodiment, applied to a performance analysis service consumer, the method at least includes steps S710 to S730, and the detailed description is as follows:

step S710, receiving a measured value of a performance parameter sent by a performance analysis service production end; the measured value of the performance parameter is obtained by measuring the performance parameters of the plurality of data analysis functional entities according to the business processing parameters of the plurality of data analysis functional entities by the performance analysis service production end.

Step S720, performing performance analysis on the plurality of data analysis functional entities according to the measured values of the performance parameters.

Step S730, processing the plurality of data analysis functional entities according to the performance analysis result obtained by the performance analysis.

After the performance analysis service consumer receives the performance analysis report sent by the performance analysis service production end, the performance analysis service consumer acquires the measured value of the performance parameter of each data analysis functional entity contained in the performance analysis report, and performs performance analysis on the plurality of data analysis functional entities according to the measured value of the performance parameter so as to process the plurality of data analysis functional entities according to the performance analysis result obtained by the performance analysis.

The specific embodiment of the performance analysis service consumer performing performance analysis on the plurality of data analysis functional entities and processing the plurality of data analysis functional entities according to the performance analysis result obtained by the performance analysis may refer to the specific embodiment of step S120 and step S130 in fig. 1, which is not described herein in detail.

In some implementations, referring to fig. 8, fig. 8 is an interaction diagram of a data processing method according to an exemplary embodiment, where the data processing method is applied to a data processing system, and the data processing system includes a performance analysis service production end, a performance analysis service consumption end, and n NWDAF devices, where n is an integer greater than 1. The performance analysis service production end is in communication connection with the performance analysis service consumption end and the n NWDAF devices.

In this embodiment, the data processing method includes: step S810, a performance analysis service consumption end sends a performance analysis request to a performance analysis service production end, wherein the performance analysis request carries a data analysis functional entity identifier; step S820, the performance analysis service production end confirms the NWDAF equipment to be analyzed according to the data analysis function entity identification in the performance analysis request, and sends a service processing parameter acquisition request to the NWDAF equipment to be analyzed so as to enable the NWDAF equipment to feed back service processing parameters; step S830, the performance analysis service production end obtains the performance parameters of the NWDAF equipment to be analyzed according to the service processing parameters, measures the performance parameters, and sends the measured values of the performance parameters to the performance analysis service consumption end; in step S840, the performance analysis service consumer obtains the performance analysis result of the NWDAF device to be analyzed according to the measurement value of the performance parameter, so as to process each NWDAF device according to the performance analysis result, such as load balancing, fault prediction, fault resolution, etc.

According to the data processing method, the performance parameters of the data analysis functional entity are calculated according to the service processing parameters of the data analysis functional entity, so that the data analysis functional entity with different logic functions can be flexibly subjected to performance analysis, further, the data analysis functional entity is subjected to service performance evaluation, the performance problems of the data analysis functional entity are detected according to the evaluation result, the data analysis functional entity is processed according to the performance problems, the service performance of the data analysis functional entity is improved, and unified management of the data analysis functional entity supporting logic function decomposition is realized.

Fig. 9 is a block diagram of a data processing apparatus 900 according to an embodiment of the present application, which is applied to a performance analysis service production end, and as shown in fig. 9, the data processing apparatus 900 includes:

a performance measurement module 910 configured to measure performance parameters of the plurality of data analysis functional entities according to service processing parameters of the plurality of data analysis functional entities;

a first performance analysis module 920 configured to perform performance analysis on the plurality of data analysis functional entities according to the measured values of the performance parameters;

the first processing module 930 is configured to process the plurality of data analysis functional entities according to a performance analysis result obtained by the performance analysis.

In one embodiment of the application, the traffic handling parameters include: at least one of service request information and service response information; the performance measurement module 910 may include:

the service processing parameter acquisition unit is configured to acquire service request information received by the data analysis functional entity in a specified period and/or acquire service response information generated by the data analysis functional entity in the specified period to obtain service processing parameters.

A first performance measurement data generation unit configured to generate at least one first performance measurement data for the data analysis functional entity based on the traffic processing parameters.

And a second performance measurement data generation unit configured to sample and count the first performance measurement data for a specified period of time, and generate at least one second performance measurement data for the data analysis functional entity.

In one embodiment of the present application, the data analysis functional entity includes at least one of a data analysis functional entity having an analysis logic function, a data analysis functional entity having a model training logic function, and a data analysis functional entity having both the analysis logic function and the model training logic function

In one embodiment of the application, the first performance measurement data and the second performance measurement data each comprise measurement data associated with a logical function of the data analysis functional entity; the first performance measurement data generation unit and the second performance measurement data generation unit include:

the first data measurement unit is configured to generate first performance measurement data and second performance measurement data corresponding to the data analysis functional entity with the analysis logic function if the data analysis functional entity is the data analysis functional entity with the analysis logic function.

And the second data measurement unit is configured to generate first performance measurement data and second performance measurement data corresponding to the data analysis functional entity with the model training logic function if the data analysis functional entity is the data analysis functional entity with the model training logic function.

And the third data measurement unit is configured to generate first performance measurement data and second performance measurement data corresponding to the data analysis functional entity with the analysis logic function and the model training logic function if the data analysis functional entity is the data analysis functional entity with the analysis logic function and the model training logic function.

In one embodiment of the application, the service request information includes at least one of a service request, a service subscription; the service response information comprises at least one of a service response and a service notification; the parameter type of the business processing parameter comprises at least one of a service request or subscription, a service response or notification, a service success response or notification, a service error response or notification;

the first performance measurement data generation unit includes:

and the counter determining unit is configured to determine an accumulated counter corresponding to the service processing parameter according to the parameter type of the service processing parameter.

And the counting unit is configured to count the business processing parameters by utilizing the accumulated counter in a specified period of time to obtain first performance measurement data.

And the first counting subunit is configured to update the value of the accumulated counter corresponding to the service request or subscription parameter to obtain first performance measurement data if the parameter type of the service processing parameter comprises the service request or subscription.

And the second counting subunit is configured to update the value of the accumulated counter corresponding to the service response or notification parameter to obtain the first performance measurement data if the parameter type of the service processing parameter comprises the service response or notification.

And the third counting subunit is configured to update the value of the accumulated counter corresponding to the service success response or the notification parameter if the parameter type of the service processing parameter comprises the service success response or the notification parameter.

And the fourth counting subunit is configured to update the value of the accumulated counter corresponding to the service error response or notification parameter if the parameter type of the service processing parameter comprises the service error response or notification parameter.

In one embodiment of the application, the first performance measurement data comprises at least one of a corresponding number of service requests or subscriptions, a corresponding number of service responses or notifications, a corresponding number of service success responses or notifications, a corresponding number of service error responses or notifications;

the counting unit includes:

the first accumulation counter updating unit is configured to add 1 to the value of the first accumulation counter related to the service request or the subscription if the data analysis functional entity receives the service request or the subscription.

And the second cumulative counter updating unit is configured to increase the value of the second cumulative counter related to the service response or the notification by 1 if the data analysis functional entity generates the service response or the notification.

And the third accumulation counter updating unit is configured to increase the value of the third accumulation counter related to the service success response or notification by 1 if the data analysis functional entity generates the service success response or notification.

And a fourth cumulative counter updating unit configured to increment the value of the fourth cumulative counter related to the service error response or notification by 1 if the data analysis functional entity generates the service error response or notification.

In one embodiment of the present application, the second performance measurement data generation unit includes:

the first sampling unit is configured to sample the value of the first accumulation counter corresponding to the service request or subscription in a specified time period if the parameter type of the service processing parameter is the service request or subscription, so as to obtain a plurality of sampling performance parameters.

And the second sampling unit is configured to sample the value of the second accumulation counter corresponding to the service response or notification in a specified time period if the parameter type of the service processing parameter is the service response or notification, so as to obtain a plurality of sampling performance parameters.

And the third sampling unit is configured to sample the value of a third accumulation counter corresponding to the service success response or notification in a specified time period if the parameter type of the service processing parameter is the service success response or notification, so as to obtain a plurality of sampling performance parameters.

And the fourth sampling unit is configured to sample the value of a fourth accumulation counter corresponding to the service error response or notification in a specified time period if the parameter type of the service processing parameter is the service error response or notification, so as to obtain a plurality of sampling performance parameters.

and a statistical policy confirmation unit configured to acquire a statistical policy matching the first performance measurement data.

And the first statistics unit is configured to calculate an average value of a plurality of sampling performance parameters corresponding to the first performance measurement data if the statistical strategy of the first performance measurement data is an average value statistical strategy, and take the average value as second performance measurement data corresponding to the first performance measurement data.

And the second statistical unit is configured to calculate the maximum value of the plurality of sampling performance parameters corresponding to the first performance measurement data if the statistical strategy of the first performance measurement data is the maximum value statistical strategy, and take the maximum value as second performance measurement data corresponding to the first performance measurement data.

And the third statistical unit is configured to calculate the minimum value of the plurality of sampling performance parameters corresponding to the first performance measurement data if the statistical strategy of the first performance measurement data is the minimum value statistical strategy, and take the minimum value as second performance measurement data corresponding to the first performance measurement data.

In one embodiment of the application, the second performance measurement data comprises at least one of an average or maximum or minimum number of service requests or subscriptions, an average or maximum or minimum number of service responses or notifications, an average or maximum or minimum number of service success responses or notifications, an average or maximum or minimum number of service error responses or notifications.

In one embodiment of the application, the first performance measurement data and the second performance measurement data each comprise measurement data associated with specific information, the specific information comprising: the single network slice selects at least one of auxiliary information, analysis type information, request type information, subscription type information, service type information, and analysis identification information.

In one embodiment of the present application, the data processing apparatus 900 further includes:

and a performance analysis report generation unit configured to generate a performance analysis report from at least one of the first performance measurement data and the second performance measurement data.

And a performance analysis report transmitting unit configured to transmit the performance analysis report to the performance analysis service consumer.

In one embodiment of the application, the first processing module 930 includes:

and the fault prediction unit is configured to perform fault prediction on the plurality of data analysis functional entities according to the performance analysis result to obtain a fault prediction result.

And a fault solution acquisition unit configured to determine a fault solution according to the fault prediction result.

And a fault solution transmitting unit configured to execute a fault solution to the data analysis functional entity.

In one embodiment of the application, the first processing module 930 includes:

the allocation request receiving unit is configured to receive an allocation request of the data analysis functional entity, wherein the allocation request of the data analysis functional entity carries parameter information of the data analysis service to be processed.

And the data analysis functional entity matching unit is configured to select a target data analysis functional entity matched with the parameter information of the data analysis service to be processed according to the performance analysis results of the plurality of data analysis functional entities.

And the distribution unit is configured to distribute the data analysis service to be processed to the target data analysis functional entity.

In one embodiment of the application, the first processing module 930 includes:

and the computing resource use condition acquisition unit is configured to acquire computing resource use conditions of the plurality of data analysis functional entities according to the performance analysis results.

And the computing resource allocation unit is configured to allocate computing resources to the plurality of data analysis functional entities according to the computing resource use condition.

It should be noted that, the information transmission apparatus provided in the above embodiment and the data processing method provided in the above embodiment belong to the same concept, and the specific manner in which each module and unit perform the operation has been described in detail in the method embodiment, which is not described herein again. In practical application, the information transmission device provided in the above embodiment may distribute the functions to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above, which is not limited herein.

Fig. 10 is a block diagram of a data processing apparatus 1000 according to an embodiment of the present application, which is applied to a performance analysis service consumer, and as shown in fig. 10, the data processing apparatus 1000 includes:

a performance parameter receiving module 1010 configured to receive a measured value of a performance parameter sent by the performance analysis service production end; the measured value of the performance parameter is obtained by measuring the performance parameters of the plurality of data analysis functional entities according to the business processing parameters of the plurality of data analysis functional entities by the performance analysis service production end.

A second performance analysis module 1020 configured to perform a performance analysis on the plurality of data analysis functional entities based on the measured values of the performance parameters;

the second processing module 1030 is configured to process the plurality of data analysis functional entities according to a performance analysis result obtained by the performance analysis.

Fig. 11 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the application.

It should be noted that, the computer system 1100 of the electronic device shown in fig. 11 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.

As shown in fig. 11, the electronic device 1100 is embodied in the form of a general purpose computing device. Components of electronic device 1100 may include, but are not limited to: the at least one processing unit 1110, the at least one memory unit 1120, a bus 1130 connecting the different system components (including the memory unit 1120 and the processing unit 1110), and a display unit 1140.

Wherein the storage unit stores program code that is executable by the processing unit 1110 such that the processing unit 1110 performs steps according to various exemplary embodiments of the present disclosure described in the above-described "exemplary methods" section of the present specification.

The storage unit 1120 may include a readable medium in the form of a volatile storage unit, such as a Random Access Memory (RAM) 1121 and/or a cache memory 1122, and may further include a Read Only Memory (ROM) 1123.

Storage unit 1120 may also include a program/utility 1124 having a set (at least one) of program modules 1125, such program modules 1125 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.

The bus 1130 may be a local bus representing one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a bus using any of a variety of bus architectures.

The electronic device 1100 may also communicate with one or more external devices 1170 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 1100, and/or any device (e.g., router, modem, etc.) that enables the electronic device 1100 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 1150. Also, electronic device 1100 can communicate with one or more networks such as a local area network, a wide area network, and/or a public network such as the Internet via network adapter 1160. As shown, network adapter 1160 communicates with other modules of electronic device 1100 via bus 1130. It should be appreciated that although not shown, other hardware and/or application modules may be used in connection with the electronic device 1100, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, tape drives, data backup storage systems, and the like.

In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer applications. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. The various functions defined in the system of the present application are performed when the computer program is executed by the processing unit 1110.

It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

The units involved in the embodiments of the present application may be implemented by means of application programs, or may be implemented by means of hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.

Another aspect of the application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a data processing method as before. The computer-readable storage medium may be included in the electronic device described in the above embodiment or may exist alone without being incorporated in the electronic device.

Another aspect of the application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions so that the computer device performs the data processing method provided in the above-described respective embodiments.

The foregoing is merely illustrative of the preferred embodiments of the present application and is not intended to limit the embodiments of the present application, and those skilled in the art can easily make corresponding variations or modifications according to the main concept and spirit of the present application, so that the protection scope of the present application shall be defined by the claims.

Claims

1. A data processing method, applied to a performance analysis service production end, the method comprising:

measuring performance parameters of a plurality of data analysis functional entities according to service processing parameters of the plurality of data analysis functional entities;

performing performance analysis on the plurality of data analysis functional entities according to the measured values of the performance parameters;

and processing the plurality of data analysis functional entities according to the performance analysis results obtained by the performance analysis.

2. The method of claim 1, wherein the traffic handling parameters comprise: at least one of service request information and service response information; the measuring the performance parameters of the plurality of data analysis functional entities according to the service processing parameters of the plurality of data analysis functional entities includes:

Collecting the service request information received by the data analysis functional entity in a specified period of time and/or collecting the service response information generated by the data analysis functional entity in the specified period of time to obtain the service processing parameters;

generating at least one first performance measurement data for the data analysis functional entity according to the service processing parameters;

sampling and counting the first performance measurement data within a specified time period, and generating at least one second performance measurement data for the data analysis functional entity.

3. The method of claim 2, wherein the data analysis functional entity comprises at least one of a data analysis functional entity with analysis logic functionality, a data analysis functional entity with model training logic functionality, and a data analysis functional entity with both analysis logic functionality and model training logic functionality.

4. A method according to claim 3, wherein the first performance measurement data and the second performance measurement data each comprise measurement data associated with a logical function of the data analysis functional entity; the generating the first performance measurement data for the data analysis functional entity according to the service processing parameters, sampling and counting the first performance measurement data in a specified time period, and generating the second performance measurement data for the data analysis functional entity comprises the following steps:

If the data analysis functional entity is the data analysis functional entity with the analysis logic function, generating the first performance measurement data and the second performance measurement data corresponding to the data analysis functional entity with the analysis logic function;

if the data analysis functional entity is the data analysis functional entity with the model training logic function, generating the first performance measurement data and the second performance measurement data corresponding to the data analysis functional entity with the model training logic function;

and if the data analysis functional entity is the data analysis functional entity with the analysis logic function and the model training logic function, generating the first performance measurement data and the second performance measurement data corresponding to the data analysis functional entity with the analysis logic function and the model training logic function.

5. The method of claim 2, wherein the service request information comprises at least one of a service request, a service subscription; the service response information comprises at least one of service response and service notification; the parameter type of the business processing parameter comprises at least one of a service request or subscription, a service response or notification, a service success response or notification, a service error response or notification; said generating at least one of said first performance measurement data for said data analysis functional entity according to said traffic handling parameters comprises:

counting the business processing parameters by using the accumulation counter in a specified period of time to obtain the first performance measurement data;

if the parameter type of the service processing parameter comprises the service request or subscription, updating the value of an accumulation counter corresponding to the service request or subscription parameter to obtain the first performance measurement data;

if the parameter type of the service processing parameter comprises the service response or notification, updating the value of an accumulated counter corresponding to the service response or notification parameter to obtain the first performance measurement data;

if the parameter type of the service processing parameter comprises the service success response or notification parameter, updating the value of an accumulation counter corresponding to the service success response or notification parameter;

and if the parameter type of the service processing parameter comprises the service error response or notification parameter, updating the value of the accumulated counter corresponding to the service error response or notification parameter.

6. The method of claim 5, wherein the first performance measurement data comprises at least one of a corresponding number of service requests or subscriptions, a corresponding number of service responses or notifications, a corresponding number of service success responses or notifications, a corresponding number of service error responses or notifications; and counting the service processing parameters by using the accumulation counter in a specified period of time to obtain the first performance measurement data, wherein the method comprises the following steps:

If the data analysis functional entity receives the service request or subscription, the value of a first accumulation counter related to the service request or subscription is increased by 1;

if the data analysis functional entity generates the service response or notification, the value of a second accumulation counter related to the service response or notification is increased by 1;

if the data analysis functional entity generates the service success response or notification, the value of a third accumulation counter related to the service success response or notification is increased by 1;

if the data analysis functional entity generates the service error response or notification, the value of a fourth accumulation counter related to the service error response or notification is increased by 1;

the first performance measurement data includes at least one of a value of the first accumulation counter, a value of the second accumulation counter, a value of the third accumulation counter, and a value of the fourth accumulation counter.

7. The method of claim 2, wherein the sampling and counting the first performance measurement data over a specified period of time to generate at least one of the second performance measurement data for the data analysis functional entity comprises:

and if the parameter type of the service processing parameter is a service error response or notification, sampling the value of a fourth accumulation counter corresponding to the service error response or notification in a specified time period to obtain a plurality of sampling performance parameters.

8. The method of claim 7, wherein the sampling and counting the first performance measurement data over a specified period of time to generate at least one of the second performance measurement data for the data analysis functional entity comprises:

Acquiring a statistical policy matched with the first performance measurement data;

if the statistical strategy of the first performance measurement data is the maximum statistical strategy, calculating the maximum value of a plurality of sampling performance parameters corresponding to the first performance measurement data, and taking the maximum value as second performance measurement data corresponding to the first performance measurement data;

9. The method of claim 8, wherein the second performance measurement data comprises at least one of an average or maximum or minimum number of service requests or subscriptions, an average or maximum or minimum number of service responses or notifications, an average or maximum or minimum number of service success responses or notifications, an average or maximum or minimum number of service error responses or notifications.

10. The method according to any one of claims 2 to 9, wherein the first performance measurement data and the second performance measurement data each comprise measurement data associated with specific information, the specific information comprising: the single network slice selects at least one of auxiliary information, analysis type information, request type information, subscription type information, service type information, and analysis identification information.

11. The method according to claim 2, wherein the method further comprises:

generating a performance analysis report from at least one of the first performance measurement data and the second performance measurement data;

and sending the performance analysis report to the performance analysis service consumer.

12. The method according to claim 1, wherein the processing the plurality of data analysis functional entities according to the performance analysis result obtained by the performance analysis comprises:

performing fault prediction on the plurality of data analysis functional entities according to the performance analysis result to obtain a fault prediction result;

determining a fault solution according to the fault prediction result;

and executing the fault solution on the data analysis functional entity.

13. The method according to claim 1, wherein the processing the plurality of data analysis functional entities according to the performance analysis result obtained by the performance analysis comprises:

receiving a data analysis functional entity allocation request, wherein the data analysis functional entity allocation request carries parameter information of data analysis service to be processed;

selecting a target data analysis functional entity matched with the parameter information of the data analysis service to be processed according to the performance analysis results of the plurality of data analysis functional entities;

and distributing the data analysis service to be processed to the target data analysis functional entity.

14. The method according to claim 1, wherein the processing the plurality of data analysis functional entities according to the performance analysis result obtained by the performance analysis comprises:

respectively acquiring the computing resource use conditions of the data analysis functional entities according to the performance analysis results;

and distributing the computing resources to the plurality of data analysis functional entities according to the computing resource use condition.

15. A data processing method, applied to a performance analysis service consumer, the method comprising:

Receiving a measured value of the performance parameter sent by the performance analysis service production end; the measured value of the performance parameter is obtained by measuring the performance parameters of a plurality of data analysis functional entities according to the service processing parameters of the data analysis functional entities by the performance analysis service production end;

16. A data processing apparatus for use at a performance analysis service production end, the apparatus comprising:

the performance measurement module is configured to measure the performance parameters of the data analysis functional entities according to the service processing parameters of the data analysis functional entities;

a first performance analysis module configured to perform performance analysis on the plurality of data analysis functional entities according to the measured values of the performance parameters;

and the first processing module is configured to process the plurality of data analysis functional entities according to the performance analysis result obtained by the performance analysis.

17. A data processing apparatus for use at a performance analysis service consumer, the apparatus comprising:

The performance parameter receiving module is configured to receive the measured value of the performance parameter sent by the performance analysis service production end; the measured value of the performance parameter is obtained by measuring the performance parameters of a plurality of data analysis functional entities according to the service processing parameters of the data analysis functional entities by the performance analysis service production end;

a second performance analysis module configured to perform performance analysis on the plurality of data analysis functional entities according to the measured values of the performance parameters;

and the second processing module is configured to process the plurality of data analysis functional entities according to the performance analysis result obtained by the performance analysis.