CN117768937A

CN117768937A - Information acquisition method, device and storage medium

Info

Publication number: CN117768937A
Application number: CN202211174434.4A
Authority: CN
Inventors: 牛煜霞; 赵嵩
Original assignee: China Telecom Corp Ltd
Current assignee: China Telecom Corp Ltd
Priority date: 2022-09-26
Filing date: 2022-09-26
Publication date: 2024-03-26
Also published as: WO2024066770A1; WO2024066770A9

Abstract

The disclosure provides an information acquisition method, an information acquisition device and a storage medium, and relates to the technical field of communication. The information acquisition method comprises the following steps: acquiring interaction statistical information of the first network entity, wherein the interaction statistical information comprises at least one of the number of requests received by the first network entity or the number of responses of the first network entity; and determining the performance parameters of the first network entity according to the interaction statistical information. By such a method, the management capability of the network analysis service is improved.

Description

Information acquisition method, device and storage medium

Technical Field

The present disclosure relates to the field of communications technologies, and in particular, to an information acquisition method, an information acquisition device, and a storage medium.

Background

The NWDAF (Network Data Analytics Function, network data analysis Function) entity is a Network element in 5GC (5 g core,5g core Network), supports collection of data from NF (Network Function) s, AF (Application Function ) s, and OAM (Operation Administration and Maintenance), and provides analysis information to NFs, AFs, and OAM.

The NWDAF entity can provide services including an analysis service, a data management service, and an ML (Machine Learning) model-related service.

Disclosure of Invention

An object of the present disclosure is to provide a method for obtaining performance parameters of a network entity, so as to improve management capability of network analysis service.

According to an aspect of some embodiments of the present disclosure, there is provided an information acquisition method including: acquiring interaction statistical information of the first network entity, wherein the interaction statistical information comprises at least one of the number of requests received by the first network entity or the number of responses of the first network entity; and determining the performance parameters of the first network entity according to the interaction statistical information.

In some embodiments, the performance parameter comprises at least one of a request success rate, a service success rate, a request failure rate, a service type ratio, wherein the service type comprises at least one of an analytics service, a data management service, or a model-dependent service.

In some embodiments, the number of requests includes a number of request receptions; the response times comprise response transmission comprehensive times; and the performance parameters include a service success rate.

In some embodiments, the method corresponds to at least one of: the request times further include at least one of a request acceptance integration time, a request rejection integration time, a request acceptance time corresponding to the service type, or a request rejection time corresponding to the service type; or the response times further comprise at least one of a successful response integration times, an error response integration times, a response transmission times corresponding to the service type, a successful response times corresponding to the service type or an error response times corresponding to the service type.

In some embodiments, the method corresponds to at least one of: the request success rate includes at least one of a request success integrated rate, a request success rate of an analysis service, a request success rate of a data management service, or a request success rate of a model-dependent service; the service success rate includes at least one of a service success integrated rate, a service success rate of an analysis service, a service success rate of a data management service, or a service success rate of a model-dependent service; the request failure rate includes at least one of a request failure integrated rate, a request failure rate of an analysis service, a request failure rate of a data management service, or a request failure rate of a model-related service; or the service failure rate includes at least one of a service failure integrated rate, a service failure rate of an analysis service request, a service failure rate of a data management service, or a service failure rate of a model-related service.

In some embodiments, the method further comprises: determining a filtering condition, wherein the filtering condition comprises at least one of a network slice identifier or a network analysis identifier; the step of obtaining interaction statistical information of the first network entity comprises the following steps: acquiring interaction statistical information of a first network entity, wherein the interaction statistical information accords with a filtering condition; determining the performance parameter of the first network entity according to the interaction statistical information comprises: and determining the performance parameters of the first network entity under the filtering condition according to the interaction statistical information.

In some embodiments, obtaining interaction statistics of the first network entity includes: acquiring at least two interaction statistical information through a counter; determining the performance parameter of the first network entity according to the interaction statistical information comprises: and acquiring at least one performance parameter according to the relation between the at least two interactive statistical information.

In some embodiments, obtaining interaction statistics of the first network entity includes: acquiring the request receiving comprehensive times and the request receiving comprehensive times of a first network entity; determining a performance parameter of the first network entity based on the interaction statistics comprises at least one of: determining a request success comprehensive ratio according to the ratio of the request receiving comprehensive times to the request receiving comprehensive times; or determining a request success comprehensive ratio according to the ratio of the request receiving comprehensive times to the request receiving comprehensive times, and determining a request failure comprehensive ratio according to the request success comprehensive ratio, wherein the sum of the request success comprehensive ratio and the request failure comprehensive ratio is 1.

In some embodiments, obtaining interaction statistics of the first network entity includes: acquiring the comprehensive number of request receiving and the comprehensive number of request rejecting of a first network entity; determining the performance parameter of the first network entity according to the interaction statistical information comprises: determining a request failure comprehensive ratio according to the ratio of the request rejection comprehensive times to the request receiving comprehensive times; or determining a request failure comprehensive ratio according to the ratio of the request rejection comprehensive times to the request receiving comprehensive times, and determining a request success comprehensive ratio according to the request failure comprehensive ratio, wherein the sum of the request success comprehensive ratio and the request failure comprehensive ratio is 1.

In some embodiments, obtaining interaction statistics of the first network entity includes: acquiring the successful response comprehensive times of the first network entity, and acquiring the response transmission comprehensive times or the request receiving comprehensive times of the first network entity; determining the performance parameter of the first network entity according to the interaction statistical information comprises: determining a service success comprehensive ratio according to the ratio of the successful response comprehensive times to the response transmission comprehensive times or the request receiving comprehensive times; or determining a service success comprehensive ratio according to the ratio of the successful response comprehensive times to the response transmission comprehensive times or the request receiving comprehensive times, and determining a service failure comprehensive ratio according to the service success comprehensive ratio, wherein the sum of the service success comprehensive ratio and the service failure comprehensive ratio is 1.

In some embodiments, obtaining interaction statistics of the first network entity includes: acquiring the error response comprehensive times of the first network entity, and acquiring the response transmission comprehensive times or the request receiving comprehensive times of the first network entity; determining the performance parameter of the first network entity according to the interaction statistical information comprises: determining a service failure comprehensive ratio according to the ratio of the error response comprehensive times to the response transmission comprehensive times or the request receiving comprehensive times; or determining a service failure comprehensive ratio according to the ratio of the error response times to the response transmission comprehensive times or the request receiving comprehensive times, and determining a service success comprehensive ratio according to the service failure comprehensive ratio, wherein the sum of the service success comprehensive ratio and the service failure comprehensive ratio is 1.

In some embodiments, obtaining interaction statistics of the first network entity includes: acquiring the request receiving times of the first type service and the request receiving times of the first type service of the first network entity; determining a performance parameter of the first network entity based on the interaction statistics comprises at least one of: determining the request success rate of the first type service according to the ratio of the request receiving times of the first type service to the request receiving times of the first type service; or determining the request success ratio of the first type service according to the ratio of the request receiving times of the first type service and the request receiving times of the first type service, and determining the request failure ratio of the first type service according to the request success ratio of the first type service, wherein the sum of the request success ratio of the first type service and the request failure ratio of the first type service is 1, and the first type service comprises analysis service, data management service or model related service.

In some embodiments, obtaining interaction statistics of the first network entity includes: acquiring the request receiving times of the first type service and the request rejecting times of the first type service of the first network entity; determining a performance parameter of the first network entity based on the interaction statistics comprises at least one of: determining the request failure rate of the first type service according to the ratio of the request rejection times of the first type service to the request receiving times of the first type service; or determining the request failure rate of the first type service according to the ratio of the request rejection times of the first type service and the request receiving times of the first type service, and determining the request success rate of the first type service according to the request failure rate of the first type service, wherein the sum of the request success rate and the request failure rate of the first type service is 1, and the first type service comprises analysis service, data management service or model related service.

In some embodiments, obtaining interaction statistics of the first network entity includes: acquiring the successful response times of the second type service of the first network entity, and acquiring the response sending times of the second type service or the request receiving times of the second type service of the first network entity; determining the performance parameter of the first network entity according to the interaction statistical information comprises: determining the service success ratio of the second type service according to the ratio of the successful response times of the second type service to the response sending times or the request receiving times of the second type service; or determining the service success ratio of the second type service according to the ratio of the successful response times of the second type service to the response sending times or the request receiving times of the second type service, and determining the service failure ratio of the second type service according to the service success ratio of the second type service, wherein the sum of the service success ratio of the second type service and the service failure ratio of the second type service is 1, and the second type service comprises analysis service, data management service or model related service.

In some embodiments, obtaining interaction statistics of the first network entity includes: acquiring the error response times of the second type service of the first network entity, and acquiring the response sending times of the second type service or the request receiving times of the second type service of the first network entity; determining the performance parameter of the first network entity according to the interaction statistical information comprises: determining the service failure rate of the second type service according to the ratio of the error response times of the second type service to the response sending times or the request receiving times of the second type service; or determining the service failure rate of the second type service according to the ratio of the error response times of the second type service to the response sending times or the request receiving times of the second type service, and determining the service success rate of the second type service according to the service failure rate of the second type service, wherein the sum of the service success rate and the service failure rate of the second type service is 1, and the second type service comprises analysis service, data management service or model related service.

In some embodiments, obtaining interaction statistics of the first network entity includes: acquiring the comprehensive number of request receiving times of a first network entity and the number of request receiving times of a third type of service; determining the performance parameter of the first network entity according to the interaction statistical information comprises: and determining the third type service duty ratio according to the ratio of the request receiving times and the request receiving comprehensive times of the third type service, wherein the third type service comprises an analysis service, a data management service or a model related service.

In some embodiments, the method further comprises: evaluating performance of the first network entity based on the performance parameter, including at least one of: determining an operational status of the first network entity based on at least one of a request success rate, a service success rate, a request failure rate, or a service failure rate; or determining the resource occupation state of the service of the corresponding service type to the first network entity according to the request duty ratio of the service type.

In some embodiments, the method further comprises at least one of: determining at least one of resource allocation rationality, task allocation rationality or fault state of the first network entity according to the running state of the first network entity; or allocating resources for the first network entity according to the resource occupancy state.

In some embodiments, the method further comprises: determining a target service type; the step of obtaining interaction statistical information of the first network entity comprises the following steps: acquiring interaction statistical information related to a target service type; determining the performance parameter of the first network entity according to the interaction statistical information comprises: and determining performance parameters related to the target service type of the first network entity according to the interaction statistical information related to the target service type.

In some embodiments, the method corresponds to at least one of: the number of requests includes a number of at least one of service requests or service subscriptions; or the number of responses includes a number of at least one of service responses or subscription notifications.

In some embodiments, the method further comprises: acquiring a performance analysis request, wherein the acquisition of the interaction statistical information of the first network entity is performed under the triggering of the performance analysis request; and transmitting the determined performance parameters to one or more network elements.

In some embodiments, the first network entity comprises an entity having network data analysis functionality.

According to an aspect of some embodiments of the present disclosure, there is provided an information acquisition apparatus including: a statistic information obtaining unit configured to obtain interaction statistic information of the first network entity, where the interaction statistic information includes at least one of a request number received by the first network entity and a response number of the first network entity; and the performance parameter determining unit is configured to determine the performance parameter of the first network entity according to the interaction statistical information.

In some embodiments, the apparatus further comprises: a performance evaluation unit configured to evaluate the performance of the first network according to the performance parameters, comprising at least one of: determining an operational status of the first network based on at least one of a request success rate, a service success rate, a request failure rate, or a service failure rate; or determining the resource occupation state of the service of the corresponding service type to the first network according to the request duty ratio of the service type.

In some embodiments, the apparatus further comprises at least one of: a state analysis unit configured to determine at least one of resource allocation rationality, task allocation rationality, or failure state of the first network according to an operation state of the first network; or a resource allocation unit configured to allocate resources for the first network entity according to the resource occupancy state.

In some embodiments, the apparatus further comprises: a type determining unit configured to determine a target service type; the statistical information acquisition unit is configured to acquire interactive statistical information related to the target service type; the performance parameter determination unit is configured to determine a performance parameter related to a target service type of the first network entity based on the interaction statistics related to the target service type.

In some embodiments, the apparatus further comprises: the receiving unit is configured to acquire a performance analysis request, and trigger the statistical information acquisition unit to work according to the performance analysis request; and a transmitting unit configured to transmit the determined performance parameters to one or more network elements.

According to an aspect of some embodiments of the present disclosure, there is provided an information acquisition apparatus including: a memory; and a processor coupled to the memory, the processor configured to perform any of the information retrieval methods mentioned above based on instructions stored in the memory.

According to an aspect of some embodiments of the present disclosure, a computer-readable storage medium is presented, on which computer program instructions are stored, which instructions, when executed by a processor, implement the steps of any of the information acquisition methods mentioned above.

Drawings

The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the present disclosure, and together with the description serve to explain the present disclosure. In the drawings:

fig. 1 is a flow chart of some embodiments of the information acquisition method of the present disclosure.

Fig. 2 is a flow chart of other embodiments of the information acquisition method of the present disclosure.

Fig. 3 is a schematic diagram of some embodiments of network elements for which the information acquisition method of the present disclosure is directed.

Fig. 4 is a flow chart of still other embodiments of the information acquisition method of the present disclosure.

Fig. 5 is a schematic diagram of some embodiments of an information acquisition device of the present disclosure.

Fig. 6 is a schematic diagram of other embodiments of an information acquisition device of the present disclosure.

Fig. 7 is a schematic diagram of still further embodiments of an information acquisition apparatus of the present disclosure.

Detailed Description

The technical scheme of the present disclosure is described in further detail below through the accompanying drawings and examples.

The inventor finds that when the NWDAF entity in the related art provides the service, the service failure or the low user experience may occur due to the mismatch between the requirements of the consumer of the service and the processing capability of the NWDAF entity.

In view of the above problems, the information acquisition method and apparatus of the present disclosure enable the performance of an NWDAF entity to be evaluated, thereby providing a data basis for determining whether the NWDAF entity can provide a service.

A flowchart of some embodiments of the information acquisition method of the present disclosure is shown in fig. 1. In some embodiments, the information acquisition method of the present disclosure may be performed by the first network entity, may be performed by a service consumer or a service producer entity, and may be performed by other network entities having information interaction functions with the first network entity.

In step 130, interaction statistics of the first network entity are obtained. In some embodiments, the first network entity comprises a network entity, such as an NWDAF entity, that is provided with network data analysis functionality.

In some embodiments, if the information acquisition method is performed by the first network entity itself, the interaction statistics may be acquired through monitoring of the behavior of the first network entity itself; if the information acquisition method is executed by the service producer, the interactive statistical information can be acquired by acquiring the information transmitted and received by the first network entity through the service producer or by monitoring the first network entity; if the information acquisition method is performed by a service consumer or other network entity having an information interaction function with the first network entity, the information acquisition method may be implemented by monitoring the first network entity.

In some embodiments, the interaction statistics include at least one of a number of requests received by the first network entity or a number of responses by the first network entity. In some embodiments, the number of requests may include the number of requests received, i.e. the number of requests received by the first network entity, and may also include the number of requests accepted, i.e. the number of times the first network entity determines to provide the corresponding service after receiving the request. In some embodiments, the number of responses may include a number of successful responses, i.e., responses sent if the service is successful; the number of responses may also include the number of false responses, i.e., responses sent in the event of service failure.

In some embodiments, the service types provided by the first network entity may include one or more of analysis service, data management service or model related service, and the interaction statistics may be statistics of times of integrating various service types, or statistics of times of classification type for each service type. In some embodiments, the model-dependent service is an ML model-dependent service.

In some embodiments, the number of requests may include a number of at least one of service requests or service subscriptions. In some embodiments, the number of responses includes a number of at least one of service responses or subscription notifications.

In some embodiments, the number of requests, responses may be counted using a CC (Cumulative Counter ), which performs "+1" operations on its counted requests or responses, ensuring accuracy of the interaction statistics. In some embodiments, a counter may be set for each number of times that statistics are required, so as to further ensure accuracy of the interaction statistics.

In step 140, performance parameters of the first network entity are determined based on the interaction statistics. In some embodiments, the at least one performance parameter may be obtained based on a relationship, such as a proportional relationship, between at least two interaction statistics.

In some embodiments, the performance parameters may include at least one of request success rate, service success rate, request failure rate, service type duty cycle. The service type duty cycle may include at least one of an analysis service duty cycle, a data management service, or a model-dependent service duty cycle.

In some embodiments, the request success rate, the service success rate, the request failure rate, and the service failure rate may be ratio values for one or more service types. For example, the request success rate includes at least one of a request success integrated rate, a request success rate of an analysis service, a request success rate of a data management service, or a request success rate of a model-dependent service; the service success rate includes at least one of a service success integrated rate, a service success rate of an analysis service, a service success rate of a data management service, or a service success rate of a model-dependent service; the request failure rate includes at least one of a request failure integrated rate, a request failure rate of an analysis service, a request failure rate of a data management service, or a request failure rate of a model-related service; the service failure rate includes at least one of a service failure integrated rate, a service failure rate of an analysis service request, a service failure rate of a data management service, or a service failure rate of a model-related service.

In some embodiments, in a case where the number of requests includes a request reception integration number (number of request reception without differentiating service types) and the number of response times includes a response transmission integration number (number of response transmission without differentiating service types), the performance parameter may include a request success integration ratio, and may also include a service success integration ratio. In some embodiments, the service success rate may be a ratio of the number of response transmissions to the request success rate.

Based on the method in the embodiment, the performance of the network entity can be evaluated through the collection of the related times of the request and the response of the network entity, so that the quantification of the performance of the network entity is realized, a data base is provided for the scheduling of the subsequent request and the distribution of the service resources, and the management capability of the network analysis service is improved.

In some embodiments, as shown in fig. 1, the information acquisition method may further include step 150.

In step 150, the performance of the first network entity is evaluated according to the performance parameters.

In some embodiments, the performance of the first network entity may include an operational state of the first network entity, which in some embodiments may be determined based on at least one of a request success rate, a service success rate, a request failure rate, or a service failure rate. For example, if the request of the first network entity is low and the service success rate is high, the first network entity may be in an abnormal state and not suitable for continuing to provide the service; if the success rate is high and the failure rate is low, the system is in a normal operation state, and can continue to provide services. In some embodiments, the resource configuration rationality of the first network entity may also be determined according to the operational status of the first network entity, e.g., the resource configuration for the first network entity may be adjusted if the failure rate exceeds a threshold; in some embodiments, the task allocation rationality of the first network entity may also be determined according to the operational status of the first network entity, e.g., in case the failure rate of a certain class or classes of services exceeds a threshold, scheduling of that class of services to the first network entity is reduced or avoided.

In some embodiments, the performance of the first network entity may include a resource occupancy state of the first network entity, in some embodiments, a resource type and a resource amount occupied by each type of service may be preconfigured, and further, the resource occupancy state of the service of the corresponding service type to the first network entity is determined according to a request occupancy ratio of the service type. In some embodiments, the first network entity may be allocated resources, e.g., one or more of storage resources, computing resources, etc., according to the resource occupancy status.

In some embodiments, the condition of success or failure of the target type service is evaluated, so that it can be known that the first network entity provides the working state or performance of each type service, for example, if the rate of failure of the analysis service is relatively high, but the rate of failure of other types of services is not high, the working performance of the first network entity when the analysis service is currently provided is poor, and at this time, the manager needs to find out the problem and solve the problem, so as to improve the performance of the first network entity when the analysis service is provided. Therefore, the condition of success or failure of the target type service is evaluated, the working state of the first network entity when providing each type of service can be known in time, and necessary reference is provided for performance management of the first network entity.

In some embodiments, when evaluating the situation of the target type service request, it can be known that the first network entity provides the workload of each type of service, for example, if the rate of the model training service requests is higher and the rate of the other types of service requests is not higher, it indicates that the current main task of the first network entity is the model training service, and more computing resources are needed to provide the service, and at this time, the manager can adjust the resource allocation scheme to improve the performance of the first network entity for providing the service. Therefore, the condition of the target type service request is evaluated, the working state of the first network entity when providing each type of service can be known in time, and necessary reference is provided for the performance management of the first network entity.

Based on the mode in the embodiment, the performance of the network entity can be evaluated based on the performance parameter of the network entity, and further the service request or the network entity resource and the like are adjusted in a targeted manner by utilizing the evaluation result, so that the service level of the first network entity and the success rate of the network service request are improved; unnecessary consumption of the first network entity resources with higher failure rate can be reduced, and the effective utilization rate of the network entity resources can be improved.

In some embodiments, the number of requests may include at least one of a number of request acceptance combinations, a number of request rejection combinations, a number of request acceptance combinations corresponding to a service type, or a number of request rejection combinations corresponding to a service type, in addition to the number of request acceptance combinations. In some embodiments, the response times may include at least one of a successful response integration time, an error response integration time, a response transmission time corresponding to a service type, a successful response time corresponding to a service type, or an error response time corresponding to a service type, in addition to the response transmission integration time.

Based on the method in the embodiment, more detailed interaction statistical information can be obtained, further more dimensional performance parameters can be obtained, and performance evaluation capability of the first network entity is improved.

In some embodiments, in the above step 130, various interaction statistics may be obtained, and in turn, in step 140, performance parameters of multiple dimensions may be obtained, which may be specifically described as follows.

[ for request success ratio and request failure ratio ]

In some embodiments, the request success rate and the request failure rate of the various service types can be obtained without considering the service types corresponding to the requests.

In some embodiments, in the above step 130, the number of request receptions and the number of request receptions for the first network entity may be obtained, and in the above step 140, a request success composite ratio is determined according to a ratio of the number of request receptions to the number of request receptions, for example:

request success rate = request acceptance number/request reception number.

In some embodiments, in the above step 130, the number of request receptions and the number of request rejections to the first network entity may be obtained, and in the above step 140, a request failure synthesis ratio may be determined according to a ratio of the number of request rejections to the number of request receptions, for example:

request failure synthesis ratio = request rejection synthesis number/request reception synthesis number.

By such a method, the proportion of requests accepted or rejected by the first network entity can be obtained, thereby facilitating the knowledge of the busy or fault status of the first network entity based on the parameter.

In some embodiments, in the step 130, the request success rate may be determined based on a relationship between the request success rate and the request failure rate after obtaining one of the request success rate and the request failure rate through the ratio calculation, for example, after determining the request failure rate, the request success rate may be determined according to the request success rate=1—the request failure rate; or after determining the request success rate, determining the request failure rate according to the request failure rate=1-request success rate.

By the method, the success and failure parameters can be obtained by utilizing the relation between the performance parameters, the data quantity required to be counted is reduced, and the data counting burden of the equipment is reduced.

In some embodiments, request success rates and request failure rates corresponding to service types may be obtained for one or more service types. The service types include analysis, data management, or model-related service types.

In some embodiments, in step 130, a number of request receptions for an analytics service and a number of request receptions for the analytics service for the first network entity are obtained; in step 140, a request success rate of the analysis service is determined according to a ratio of the number of request receptions of the analysis service and the number of request receptions of the analysis service, such as:

Request success rate of analysis service = request acceptance number of analysis service/request acceptance number of analysis service.

In some embodiments, in step 130, a number of request receptions for an analytics service and a number of request rejections for the analytics service for the first network entity are obtained; in step 140, a request failure rate of the analysis service is determined according to a ratio of the request rejection number of the analysis service and the request reception number of the analysis service, such as:

request failure rate of analysis service = request rejection number of analysis service/request reception number of analysis service.

By such a method, the proportion of the request of the analysis service of the first network entity to be accepted or rejected can be obtained, thereby helping to know the service acceptance state of the first network entity for the analysis service type based on the parameter.

In some embodiments, the number of request rejections of the analysis service and the number of request receptions of the analysis service may be alternatively obtained in the above step 130, and after one of the request failure rate of the analysis service and the request success rate of the analysis service is obtained by the ratio calculation, the other is determined based on the relationship between the request failure rate of the analysis service and the request success rate of the analysis service, for example, after the request failure rate of the analysis service is determined, the request success rate of the analysis service is determined according to the request success rate of the analysis service=1—the request failure rate of the analysis service; or after determining the request success rate of the analysis service, determining the request failure rate of the analysis service according to the request failure rate=1 of the analysis service.

In some embodiments, in step 130, a number of times of request reception of the data management service and a number of times of request acceptance of the data management service of the first network entity are obtained; in step 140, a request success rate of the data management service is determined according to the ratio of the request receiving times of the data management service to the request receiving times of the data management service, such as:

request success rate of data management service = request acceptance number of data management service/request acceptance number of data management service.

In some embodiments, in step 130, a number of times of request receipt of the data management service and a number of times of request rejection of the data management service of the first network entity are obtained; in step 140, a request failure rate of the data management service is determined according to the request rejection rate of the data management service and the ratio of the request receiving rate of the data management service, such as:

request failure rate of data management service = request rejection number of data management service/request reception number of data management service.

By such a method, the proportion of the request of the data management service of the first network entity to be accepted or rejected can be obtained, thereby helping to know the service acceptance state of the first network entity for the data management service type based on the parameter.

In some embodiments, the number of request rejections of the data management service and the number of request receptions of the data management service may be alternatively obtained in the above step 130, and after one of the request failure rate of the data management service and the request success rate of the data management service is obtained by the scaling calculation, another item is determined based on the relationship between the request failure rate of the data management service and the request success rate of the data management service, for example, after the request failure rate of the data management service is determined, the request success rate of the data management service is determined according to the request success rate of the data management service=1—the request failure rate of the data management service; or after determining the request success rate of the data management service, determining the request failure rate of the data management service according to the request failure rate of the data management service=1-the request success rate of the data management service.

In some embodiments, in step 130, a number of request receptions for the model-related service and a number of request receptions for the model-related service for the first network entity are obtained; in step 140, a request success rate of the model-related service is determined according to a ratio of the number of request receptions of the model-related service to the number of request receptions of the model-related service, such as:

request success rate of model-related services = request acceptance number of model-related services/request acceptance number of model-related services.

In some embodiments, in step 130, a number of request receptions for the model-related service and a number of request rejections for the model-related service of the first network entity are obtained; in step 140, a request failure rate of the model-related service is determined according to a ratio of the number of request rejections of the model-related service and the number of request receptions of the model-related service, such as:

request failure rate of model-related service = number of request rejections of model-related service/number of request receptions of model-related service.

By such a method, the proportion of the request of the model-related service of the first network entity to be accepted or rejected can be obtained, thereby helping to know the service acceptance state of the first network entity for the model-related service type based on the parameter.

In some embodiments, in the step 130, the number of request rejections of the model-related service and the number of request receptions of the model-related service may be alternatively obtained, and after one of the request failure rate of the model-related service and the request success rate of the model-related service is obtained by the scaling calculation, another item is determined based on the relationship between the request failure rate of the model-related service and the request success rate of the model-related service, for example, after the request failure rate of the model-related service is determined, the request success rate of the model-related service is determined according to the request success rate=1-the request failure rate of the model-related service; or after determining the request success rate of the model-related service, determining the request failure rate of the model-related service according to the request failure rate of the model-related service=1-the request success rate of the model-related service.

[ for service success ratio and service failure ratio ]

In some embodiments, the service success rate and the service failure rate of the various service types can be obtained without considering the service type corresponding to the request.

In some embodiments, in the above step 130, the number of successful response complexes of the first network entity may be obtained, and the number of response transmission complexes or the number of request reception complexes for the first network entity may be obtained, and in step 140, the service success complex ratio is determined according to the ratio of the number of successful response complexes to the number of response transmission complexes or the number of request reception complexes, for example:

service success rate = successful response number/response transmission number

Or (b)

Service success rate = successful response number/request reception number.

The specific calculation mode can be preset according to the system convention.

In some embodiments, in the above step 130, the number of error response combinations of the first network entity may be obtained, and the number of response transmission combinations or the number of request reception combinations for the first network entity may be obtained, and in step 140, the service failure combination ratio is determined according to the ratio of the number of error response combinations to the number of response transmission combinations or the number of request reception combinations, for example:

service failure integrated ratio=error response integrated number/response transmission integrated number

Or (b)

Service failure integrated ratio = error response integrated number/request reception integrated number.

The specific calculation mode can be preset according to the system convention.

By such a method, a ratio of successful responses, or a ratio of erroneous responses, of requests of the first network entity can be obtained, thereby helping to understand the request processing state of the first network entity based on the parameter.

In some embodiments, in step 130, the error response synthesis number and the success response synthesis number of the first network entity may be alternatively obtained, and after one of the request success synthesis ratio and the request failure synthesis ratio is obtained through the scaling calculation, the other term is determined based on the relationship between the service failure synthesis ratio and the service success synthesis ratio, for example, after determining the service failure synthesis ratio, the service success synthesis ratio is determined according to the service success synthesis ratio=1—the service failure synthesis ratio; or after determining the service success integration ratio, determining the service failure integration ratio according to the service failure integration ratio=1-the service success integration ratio.

In some embodiments, a service success rate and a service failure rate corresponding to a service type may be obtained for one or more service types. The service types include analysis, data management, or model-related service types.

In some embodiments, in the step 130, the success response times of the analysis service of the first network entity are obtained, and the response sending times of the analysis service or the request receiving times of the analysis service of the first network entity are obtained, and in step 140, the service success ratio of the analysis service is determined according to the ratio of the success response times of the analysis service to the response sending times or the request receiving times of the analysis service, for example:

service success ratio of analysis service = number of successful responses of analysis service/number of response transmissions of analysis service

Or (b)

Service success rate of analysis service = number of successful responses of analysis service/number of request receptions of analysis service.

The specific calculation mode can be preset according to the system convention.

In some embodiments, in the step 130, the number of error responses of the analysis service of the first network entity is obtained, and the number of response transmission times of the analysis service or the number of request reception times of the analysis service of the first network entity is obtained, and in step 140, the service failure rate of the analysis service is determined according to the ratio of the number of error responses of the analysis service to the number of response transmission times of the analysis service or the number of request reception times, for example:

Service failure rate of analysis service = error response number of analysis service/response transmission number of analysis service

Or (b)

Service failure rate of analysis service = error response number of analysis service/request reception number of analysis service.

The specific calculation mode can be preset according to the system convention.

By such a method, a proportion of successful execution or execution failure of the analysis service of the first network entity can be obtained, thereby helping to know the ability of the first network entity to further provide the analysis service based on the parameter.

In some embodiments, in the step 130, the error response integrated number of the analysis service and the success response integrated number of the analysis service of the first network entity may be alternatively obtained, and after one of the request success rate of the analysis service and the request failure rate of the analysis service is obtained through the ratio calculation, another item is determined based on a relationship between the service failure rate of the analysis service and the service success rate of the analysis service, for example, after determining the service failure rate of the analysis service, the service success rate of the analysis service is determined according to the service success rate=1 of the analysis service failure rate of the analysis service; or after determining the service success rate of the analysis service, determining the service failure rate of the analysis service according to the service failure rate=1 of the analysis service.

In some embodiments, in the step 130, the successful response times of the data management service of the first network entity are obtained, and the response sending times of the data management service or the request receiving times of the data management service of the first network entity are obtained, and in step 140, the service success ratio of the data management service is determined according to the ratio of the successful response times of the data management service to the response sending times or the request receiving times of the data management service, for example:

service success rate of data management service = number of successful responses of data management service/number of response transmissions of data management service

Or (b)

Service success rate of data management service = number of successful responses of data management service/number of request receptions of data management service.

The specific calculation mode can be preset according to the system convention.

In some embodiments, in the step 130, the number of error responses of the data management service of the first network entity is obtained, and the number of response transmission times of the data management service or the number of request reception times of the data management service of the first network entity is obtained, and in step 140, the service failure rate of the data management service is determined according to the ratio of the number of error responses of the data management service to the number of response transmission times or the number of request reception times of the data management service, for example:

Service failure rate of data management service = error response number of data management service/response transmission number of data management service

Or (b)

Service failure rate of data management service = number of error responses of data management service/number of request receptions of data management service.

The specific calculation mode can be preset according to the system convention.

By such a method, a proportion of successful execution or execution failure of the data management service of the first network entity can be obtained, thereby facilitating knowledge of the first network entity's ability to further provide the data management service based on the parameter.

In some embodiments, in the step 130, the error response integrated number of the data management service and the successful response integrated number of the data management service of the first network entity may be alternatively obtained, after one of the request success rate of the data management service and the request failure rate of the data management service is obtained through the proportion calculation, another item is determined based on a relationship between the service failure rate of the data management service and the service success rate of the data management service, for example, after the service failure rate of the data management service is determined, the service success rate of the data management service is determined according to the service success rate=1-the service failure rate of the data management service; or after determining the service success rate of the data management service, determining the service failure rate of the data management service according to the service failure rate=1 of the data management service.

In some embodiments, in the step 130, the number of successful responses of the model-related service of the first network entity is obtained, and the number of response transmission times of the model-related service or the number of request reception times of the model-related service of the first network entity is obtained, and in step 140, the service success ratio of the model-related service is determined according to the ratio of the number of successful responses of the model-related service to the number of response transmission times or the number of request reception times of the model-related service, for example:

service success ratio of model-related service = number of successful responses of model-related service/number of response transmissions of model-related service

Or (b)

Service success ratio of model-related service = number of successful responses of model-related service/number of request receptions of model-related service.

The specific calculation mode can be preset according to the system convention.

In some embodiments, in the step 130, the number of error responses of the model-related service of the first network entity is obtained, and the number of response transmissions of the model-related service or the number of request receptions of the model-related service of the first network entity is obtained, and in step 140, the service failure rate of the model-related service is determined according to the ratio of the number of error responses of the model-related service to the number of response transmissions of the model-related service or the number of request receptions, for example:

Service failure rate of model-related service = number of false responses of model-related service/number of response transmissions of model-related service

Or (b)

Service failure rate of model-related service = number of false responses of model-related service/number of request receptions of model-related service.

The specific calculation mode can be preset according to the system convention.

By such a method, a proportion of successful execution or execution failure of the model-related service of the first network entity can be obtained, thereby facilitating understanding of the ability of the first network entity to further provide the model-related service based on the parameter.

In some embodiments, in the step 130, the error response synthesis number of the model-related service and the success response synthesis number of the model-related service of the first network entity may be alternatively obtained, after one of the request success rate of the model-related service and the request failure rate of the model-related service is obtained through the ratio calculation, another item is determined based on a relationship between the service failure rate of the model-related service and the service success rate of the model-related service, for example, after the service failure rate of the model-related service is determined, the service success rate of the model-related service is determined according to the service success rate=1 of the model-related service; or after determining the service success ratio of the model-related service, determining the service failure ratio of the model-related service according to the service failure ratio of the model-related service=1-the service success ratio of the model-related service.

[ for service type occupancy ]

In some embodiments, the request duty cycle, or the service duty cycle, of each of the service types that the first network entity is capable of providing may also be obtained as a corresponding service type duty cycle.

In some embodiments, if the service type ratio is the ratio of the service request, in the step 130, the integrated number of times of receiving the request of the first network entity and the number of times of receiving the request of the analysis service may be obtained, and in the step 150, the analyzed service type ratio is determined according to the ratio of the number of times of receiving the request of the analysis service and the integrated number of times of receiving the request, for example:

analysis service type ratio=number of request receptions/number of request receptions synthesis of analysis service.

In some embodiments, if the service type ratio is the served request ratio, in the step 130, the comprehensive number of request acceptances of the first network entity and the comprehensive number of request acceptances of the analysis service may be obtained, and in the step 150, the analyzed service type ratio is determined according to the ratio of the comprehensive number of request acceptances of the analysis service to the comprehensive number of request acceptances, for example:

Based on the method in this embodiment, the ratio of the analysis service in the service received or accepted by the first network entity can be obtained, thereby helping to know the resource consumption condition of the first network entity.

In some embodiments, if the service type ratio is the service request ratio, in the step 130, the integrated number of times of receiving the request of the first network entity and the integrated number of times of receiving the request of the data management service may be obtained, and in the step 150, the data management service type ratio is determined according to the ratio of the number of times of receiving the request of the data management service and the integrated number of times of receiving the request, for example:

data management service type ratio=number of request receptions/number of request receptions composite of data management service.

In some embodiments, if the service type ratio is the served request ratio, in the step 130, the integrated number of request acceptances of the first network entity and the integrated number of request acceptances of the data management service may be obtained, and in the step 150, the data management service type ratio is determined according to the ratio of the integrated number of request acceptances and the integrated number of request acceptances of the data management service, for example:

Data management service type ratio=request acceptance number/request acceptance total number of data management services.

Based on the method in this embodiment, the duty ratio of the data management service in the service received or accepted by the first network entity can be obtained, thereby helping to understand the resource consumption condition of the first network entity.

In some embodiments, if the service type ratio is the ratio of service requests, in the step 130, the integrated number of times of receiving the request of the first network entity and the integrated number of times of receiving the request of the model-related service may be obtained, and in the step 150, the service type ratio related to the model may be determined according to the ratio of the number of times of receiving the request of the model-related service to the integrated number of times of receiving the request, for example:

model-related service type ratio=number of request receptions/number of request receptions composite of model-related service.

In some embodiments, if the service type ratio is the served request ratio, in the step 130, the comprehensive number of request acceptances of the first network entity and the comprehensive number of request acceptances of the model-related services may be obtained, and in the step 150, the service type ratio related to the model may be determined according to the ratio of the number of request acceptances and the comprehensive number of request acceptances of the model-related services, for example:

Model-related service type ratio=number of request receptions/number of request receptions complex of model-related services.

Based on the method in this embodiment, the duty ratio of the model-related service in the service received or accepted by the first network entity can be obtained, thereby helping to understand the resource consumption condition of the first network entity.

In some embodiments, the sum of service types of the various services that the first network entity can provide is 1, so statistics on the number of times of one of the services, such as obtaining the service type ratio of two of the analysis service, the data management service and the model-related service, and further calculating the other service ratio, can be reduced, thereby reducing the consumption of counting resources.

By means of the method in the embodiments, performance parameters of multiple dimensions can be obtained, and the method is beneficial to providing a rich data base for subsequent performance evaluation of the first network entity.

In some embodiments, the target service type may be predetermined, or the performance parameter to be obtained may be predetermined, and the service type corresponding to the interaction statistics to be obtained is determined according to the performance parameter to be obtained, which is called the target service type, so as to obtain the interaction statistics for the target service type, and determine the performance parameter related to the target service type.

By the method, the burden on equipment caused by acquiring the interaction statistical information can be reduced on the basis of acquiring the required performance parameters, and the data processing pressure is reduced.

A flowchart of further embodiments of the information acquisition method of the present disclosure is shown in fig. 2.

In step 220, a performance analysis request is obtained, which may include a service consumer entity, or from a network management device, etc. In some embodiments, the performance analysis request may include a target service type, or a target performance parameter.

In step 230, interaction statistics of the first network entity are obtained, the interaction statistics comprising at least one of a number of requests received by the first network entity or a number of responses of the first network entity. In some embodiments, the operations in step 230 may be the same as the operations in one or more of steps 130 described above. In some embodiments, if the performance analysis request includes a target service type or a target performance parameter, acquiring interaction statistics related to the target service type and the target performance parameter by the first network entity; and if the performance analysis request does not comprise the target service type or the target performance parameter, acquiring each interaction statistical information.

In some embodiments, the interaction statistics of the first network entity may be continuously counted, and the performance parameter is calculated after the performance analysis request is obtained by extracting the corresponding data, so as to improve the feedback efficiency. In other embodiments, the performance parameters may also be calculated after the performance analysis request is obtained and the performance parameters are calculated for a predetermined period of time, thereby reducing the stress on the data statistics.

In step 240, performance parameters of the first network entity are determined based on the interaction statistics. In some embodiments, the operations in step 240 may be the same as the operations in one or more of steps 140 described above. In some embodiments, if the performance analysis request includes a target service type or a target performance parameter, acquiring the performance parameter or the target performance parameter related to the target service type of the first network entity; and if the performance analysis request does not comprise the target service type or the target performance parameter, acquiring each interaction statistical information.

In step 260, the determined performance parameters are sent to one or more network elements, where the target network element of the performance parameters includes a network element that sends a performance analysis request, and may also include a service consumer entity, a network management device, and so on.

By the method in the above embodiment, the operation of performing performance parameter acquisition can be initiated according to the performance analysis request, so that other network elements can know the performance of the first network entity.

In some embodiments, the performance parameters of the first network entity may further comprise performance parameters related to slicing or analytical identification. In some embodiments, a filter condition may be set, the filter condition including at least one of a network slice identity or a network analysis identity.

In the case that the filtering condition is the network slice identifier (S-NSSAI), in the step 130, interaction statistics of the first network entity, which meets the filtering condition, are obtained, and in the step 140, performance parameters of the first network entity under the filtering condition are determined according to the interaction statistics.

In the case that the filtering condition is an analysis identifier (analytical ID), the interaction statistics corresponding to the target analytical ID is acquired in the above step 130, and further, the request rate, service or request success rate, service or request failure rate, etc. of the service corresponding to the target analytical ID are acquired in the above step 140.

By the method, pertinence of performance parameters can be improved, follow-up fine scheduling of the request and fine management of the first network entity are facilitated.

In some embodiments, the first network entity of the present disclosure may be an NWDAF entity, as shown in fig. 3, and in the information acquisition system, a performance assessment service producer 32 and NWDAFs 331-33 n may be included, where n is a positive integer. The performance evaluation service producer 32 can execute any of the above-mentioned information acquisition methods, provide a management function or service of NWDAF, quantify a service state of NWDAF, evaluate performance of NWDAF. Each of the NWDAFs 331 to 33n may be the first network entity mentioned above. In some embodiments, the information acquisition system may further include a performance evaluation service consumer 31 capable of sending a performance analysis request and obtaining at least one of performance parameters or evaluation results fed back by the performance evaluation service producer. Based on this network structure, a flow chart of still other embodiments of the information acquisition method of the present disclosure is shown in fig. 4.

In step 410, data on NWDAF interactions of the target type of service is measured according to the service type of the NWDAF service, including the number of requests received by the NWDAF and the number of responses generated by the NWDAF. The target type of service includes: at least one of an analytics service, a data management service, and a model-related service.

In some embodiments, the request means includes: at least one of request, subscription.

In some embodiments, the response means comprises: at least one of response and notification.

In step 420, the performance index of NWDAF is quantified according to the service type of NWDAF service and the corresponding measurement data of NWDAF interaction aspect.

In step 450, the NWDAF service performance is evaluated based on the performance metrics.

By the method, the service states and the service performances of the NWDAF supporting various service types can be quantified and evaluated, the measurement data and the performance indexes in the NWDAF interaction aspect are determined according to the service types of the NWDAF service, the service states and the service performances of the NWDAF are evaluated, and necessary performance indexes are provided for the management of the NWDAF as references.

A schematic diagram of some embodiments of the information acquisition device 50 of the present disclosure is shown in fig. 5.

The statistical information acquisition unit 503 acquires interactive statistical information of the first network entity. In some embodiments, the interaction statistics include at least one of a number of requests received by the first network entity or a number of responses by the first network entity. In some embodiments, the statistics acquisition unit 503 is capable of performing any of the methods shown in steps 130 or 230 above.

The performance parameter determination unit 504 is capable of determining a performance parameter of the first network entity based on the interaction statistics. In some embodiments, the at least one performance parameter may be obtained based on a relationship, such as a proportional relationship, between at least two interaction statistics. In some embodiments, the performance parameter determination unit 504 is capable of performing any of the methods shown in steps 140 or 240 above.

The information acquisition device can evaluate the performance of the network entity through the acquisition of the request and response related times of the network entity, thereby realizing the quantification of the performance of the network entity, providing a data base for the scheduling of the subsequent request and the distribution of service resources, and improving the management capability of network analysis service.

In some embodiments, as shown in fig. 5, the information acquisition apparatus further comprises a performance evaluation unit 505 capable of determining the operation state of the first network entity according to at least one of a request success rate, a service success rate, a request failure rate or a service failure rate. For example, if the request of the first network entity is low and the service success rate is high, the first network entity may be in an abnormal state and not suitable for continuing to provide the service; if the success rate is high and the failure rate is low, the system is in a normal operation state, and can continue to provide services. In some embodiments, the information obtaining apparatus may further include a state analysis unit 507 capable of determining a resource configuration rationality of the first network entity according to an operation state of the first network entity, e.g. in case the failure rate exceeds a threshold value, the resource configuration for the first network entity may be adjusted; in some embodiments, the state analysis unit 507 may further determine a task allocation rationality of the first network entity according to the operation state of the first network entity, e.g. in case the failure rate of a certain class or classes of services exceeds a threshold, scheduling of the class of services to the first network entity is reduced or avoided. In some embodiments, the state analysis unit 507 may further determine whether the network entity is faulty according to the operation state of the network entity, for example, if the request of the first network entity is low, the service success rate is high, and the first network entity may be in an abnormal state and not suitable for continuing to provide the service; if the success rate is high and the failure rate is low, the system is in a normal operation state, and can continue to provide services.

In some embodiments, the performance evaluation unit 505 is further capable of determining a resource occupancy state of the service of the corresponding service type to the first network according to the request duty cycle of the service type. In some embodiments, the information obtaining apparatus may further include a resource allocation unit 506 capable of allocating resources to the first network entity according to the resource occupancy state.

The device can evaluate the performance of the network entity based on the performance parameters of the network entity, and further utilize the evaluation result to pertinently adjust the service request or network entity resources and the like, thereby improving the service level of the first network entity and the success rate of the network service request; unnecessary consumption of the first network entity resources with higher failure rate can be reduced, and the effective utilization rate of the network entity resources can be improved.

In some embodiments, as shown in fig. 5, the information obtaining apparatus may further include a type determining unit 501 capable of determining a target service type, and in some embodiments, the target service type may be predetermined, or a performance parameter to be obtained may be predetermined, and a service type corresponding to the interaction statistics to be obtained is determined according to the performance parameter to be obtained, which is referred to as a target service type. The statistics information acquisition unit 503 is capable of acquiring interaction statistics information related to a target service type, and the performance parameter determination unit 504 is configured to determine a performance parameter related to the target service type of the first network entity according to the interaction statistics information related to the target service type. The device can reduce the burden on equipment caused by acquiring the interaction statistical information and reduce the data processing pressure on the basis of acquiring the required performance parameters.

In some embodiments, as shown in fig. 5, the information obtaining apparatus may further include a receiving unit 502 capable of obtaining a performance analysis request, and triggering the statistical information obtaining unit 503 to operate according to the performance analysis request. In some embodiments, the performance analysis request may include a target service type, or a target performance parameter. The sending unit 508 may send the determined performance parameter to one or more network elements, where the target network element of the performance parameter includes a network element that sends a performance analysis request, and may further include a service consumer entity, a network management device, and so on.

Such an apparatus is capable of initiating an operation to perform performance parameter acquisition according to the performance analysis request, thereby satisfying the knowledge of the performance of the first network entity by other network elements.

A schematic structural diagram of one embodiment of the information acquisition apparatus of the present disclosure is shown in fig. 6. The information acquisition device comprises a memory 601 and a processor 602. Wherein: the memory 601 may be a magnetic disk, flash memory or any other non-volatile storage medium. The memory is used to store instructions in the corresponding embodiments of the information retrieval method above. The processor 602 is coupled to the memory 601 and may be implemented as one or more integrated circuits, such as a microprocessor or microcontroller. The processor 602 is configured to execute instructions stored in the memory to enhance the management capabilities of the network analysis service.

In one embodiment, as also shown in FIG. 7, the information acquisition device 700 includes a memory 701 and a processor 702. The processor 702 is coupled to the memory 701 through a BUS 703. The information acquisition device 700 may also be connected to an external storage device 705 via a storage interface 704 for invoking external data, and may also be connected to a network or another computer system (not shown) via a network interface 706. And will not be described in detail herein.

In this embodiment, the management capability of the network analysis service can be improved by storing the data instruction in the memory and processing the instruction by the processor.

In another embodiment, a computer readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement the steps of the method in the corresponding embodiments of the information acquisition method. It will be apparent to those skilled in the art that embodiments of the present disclosure may be provided as a method, apparatus, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Thus far, the present disclosure has been described in detail. In order to avoid obscuring the concepts of the present disclosure, some details known in the art are not described. How to implement the solutions disclosed herein will be fully apparent to those skilled in the art from the above description.

The methods and apparatus of the present disclosure may be implemented in a number of ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present disclosure may also be implemented as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

Finally, it should be noted that: the above embodiments are merely for illustrating the technical solution of the present disclosure and are not limiting thereof; although the present disclosure has been described in detail with reference to preferred embodiments, those of ordinary skill in the art will appreciate that: modifications may be made to the specific embodiments of the disclosure or equivalents may be substituted for part of the technical features; without departing from the spirit of the technical solutions of the present disclosure, it should be covered in the scope of the technical solutions claimed in the present disclosure.

Claims

1. An information acquisition method, comprising:

acquiring interaction statistical information of a first network entity, wherein the interaction statistical information comprises at least one of the number of requests received by the first network entity or the number of responses of the first network entity;

and determining the performance parameters of the first network entity according to the interaction statistical information.

2. The method of claim 1, wherein the performance parameters comprise at least one of a request success rate, a service success rate, a request failure rate, a service type duty cycle, wherein the service type comprises at least one of an analytics service, a data management service, or a model-related service.

3. The method of claim 1, wherein,

the request times comprise request receiving comprehensive times;

the response times comprise response sending comprehensive times; and

the performance parameters include a service success composite ratio.

4. A method according to claim 3, wherein the method corresponds to at least one of:

the request times further comprise at least one of request acceptance times, request rejection times, request receiving times corresponding to the service types, request acceptance times corresponding to the service types or request rejection times corresponding to the service types; or (b)

The response times further include at least one of a successful response integration time, an error response integration time, a response transmission time corresponding to the service type, a successful response time corresponding to the service type, or an error response time corresponding to the service type.

5. The method of claim 2, wherein the method conforms to at least one of:

the request success rate includes at least one of a request success integrated rate, a request success rate of an analysis service, a request success rate of a data management service, or a request success rate of a model-dependent service;

The service success rate includes at least one of a service success integrated rate, a service success rate of an analysis service, a service success rate of a data management service, or a service success rate of a model-related service;

the request failure rate includes at least one of a request failure integrated rate, a request failure rate of an analysis service, a request failure rate of a data management service, or a request failure rate of a model-related service; or (b)

The service failure rate includes at least one of a service failure integrated rate, a service failure rate of an analysis service request, a service failure rate of a data management service, or a service failure rate of a model-related service.

6. The method of claim 1, further comprising: determining a filtering condition, wherein the filtering condition comprises at least one of a network slice identifier or a network analysis identifier;

the step of obtaining the interaction statistical information of the first network entity comprises the following steps: acquiring interaction statistical information of the first network entity, which accords with the filtering condition;

the determining the performance parameter of the first network entity according to the interaction statistical information comprises: and determining the performance parameters of the first network entity under the filtering condition according to the interaction statistical information.

7. The method of claim 1, wherein,

the step of obtaining the interaction statistical information of the first network entity comprises the following steps: acquiring at least two kinds of interaction statistical information through a counter;

the determining the performance parameter of the first network entity according to the interaction statistical information comprises: and acquiring at least one performance parameter according to the relation between at least two interactive statistical information.

8. The method of claim 1, wherein,

the step of obtaining the interaction statistical information of the first network entity comprises the following steps: acquiring the request receiving comprehensive times and the request receiving comprehensive times of the first network entity;

the determining the performance parameter of the first network entity according to the interaction statistical information comprises at least one of the following:

determining a request success comprehensive ratio according to the ratio of the request receiving comprehensive times to the request receiving comprehensive times; or (b)

And determining a request success comprehensive ratio according to the ratio of the request acceptance comprehensive times to the request receiving comprehensive times, and determining a request failure comprehensive ratio according to the request success comprehensive ratio, wherein the sum of the request success comprehensive ratio and the request failure comprehensive ratio is 1.

9. The method of claim 1, wherein,

the step of obtaining the interaction statistical information of the first network entity comprises the following steps: acquiring the comprehensive times of receiving requests and the comprehensive times of rejecting the requests of the first network entity;

the determining the performance parameter of the first network entity according to the interaction statistical information comprises:

determining a request failure comprehensive ratio according to the ratio of the request rejection comprehensive times to the request receiving comprehensive times; or (b)

And determining a request failure comprehensive ratio according to the ratio of the request rejection comprehensive times to the request receiving comprehensive times, and determining a request success comprehensive ratio according to the request failure comprehensive ratio, wherein the sum of the request success comprehensive ratio and the request failure comprehensive ratio is 1.

10. The method of claim 1, wherein,

the step of obtaining the interaction statistical information of the first network entity comprises the following steps: acquiring the successful response comprehensive times of the first network entity, and acquiring the response transmission comprehensive times or the request receiving comprehensive times of the first network entity;

Determining a service success comprehensive ratio according to the ratio of the successful response comprehensive times to the response transmission comprehensive times or the request receiving comprehensive times; or (b)

And determining a service success comprehensive ratio according to the ratio of the successful response comprehensive times to the response transmission comprehensive times or the request receiving comprehensive times, and determining a service failure comprehensive ratio according to the service success comprehensive ratio, wherein the sum of the service success comprehensive ratio and the service failure comprehensive ratio is 1.

11. The method of claim 1, wherein,

the step of obtaining the interaction statistical information of the first network entity comprises the following steps: acquiring the error response comprehensive times of the first network entity, and acquiring response transmission comprehensive times or request receiving comprehensive times of the first network entity;

determining a service failure comprehensive ratio according to the ratio of the error response comprehensive times to the response transmission comprehensive times or the request receiving comprehensive times; or (b)

And determining a service failure comprehensive ratio according to the ratio of the error response times to the response transmission comprehensive times or the request receiving comprehensive times, and determining a service success comprehensive ratio according to the service failure comprehensive ratio, wherein the sum of the service success comprehensive ratio and the service failure comprehensive ratio is 1.

12. The method of claim 1, wherein,

the step of obtaining the interaction statistical information of the first network entity comprises the following steps: acquiring the request receiving times of the first type service and the request receiving times of the first type service of the first network entity;

determining the request success ratio of the first type service according to the ratio of the request receiving times of the first type service to the request receiving times of the first type service; or (b)

Determining a request success ratio of a first type service according to a ratio of a request receiving number of the first type service to a request receiving number of the first type service, and determining a request failure ratio of the first type service according to the request success ratio of the first type service, wherein the sum of the request success ratio of the first type service and the request failure ratio of the first type service is 1, and the first type service comprises an analysis service, a data management service or a model related service.

13. The method of claim 1, wherein,

the step of obtaining the interaction statistical information of the first network entity comprises the following steps: acquiring the request receiving times of the first type service and the request rejecting times of the first type service of the first network entity;

determining the request failure rate of the first type service according to the ratio of the request rejection times of the first type service to the request receiving times of the first type service; or (b)

Determining a request failure rate of a first type service according to a ratio of a request rejection number of the first type service to a request receiving number of the first type service, and determining a request success rate of the first type service according to the request failure rate of the first type service, wherein the sum of the request success rate and the request failure rate of the first type service is 1, and the first type service comprises an analysis service, a data management service or a model related service.

14. The method of claim 1, wherein,

the step of obtaining the interaction statistical information of the first network entity comprises the following steps: acquiring the successful response times of the second type service of the first network entity, and acquiring the response sending times of the second type service or the request receiving times of the second type service of the first network entity;

Determining a service success ratio of the second type service according to a ratio of the successful response times of the second type service to the response transmission times or the request receiving times of the second type service; or (b)

Determining a service success ratio of a second type service according to a ratio of the successful response times of the second type service to the response transmission times or the request receiving times of the second type service, and determining a service failure ratio of the second type service according to the service success ratio of the second type service, wherein the sum of the service success ratio of the second type service and the service failure ratio of the second type service is 1, and the second type service comprises an analysis service, a data management service or a model-related service.

15. The method of claim 1, wherein,

the step of obtaining the interaction statistical information of the first network entity comprises the following steps: acquiring the error response times of the second type service of the first network entity, and acquiring the response sending times of the second type service or the request receiving times of the second type service of the first network entity;

Determining a service failure rate of the second type service according to a ratio of the error response times of the second type service to the response transmission times or the request receiving times of the second type service; or (b)

Determining a service failure rate of a second type service according to a ratio of the error response times of the second type service to the response transmission times or the request receiving times of the second type service, and determining a service success rate of the second type service according to the service failure rate of the second type service, wherein the sum of the service success rate and the service failure rate of the second type service is 1, and the second type service comprises an analysis service, a data management service or a model related service.

16. The method of claim 1, wherein,

the step of obtaining the interaction statistical information of the first network entity comprises the following steps: acquiring the comprehensive number of request receiving times of the first network entity and the number of request receiving times of the third type of service;

the determining the performance parameter of the first network entity according to the interaction statistical information comprises: and determining the third type service duty ratio according to the ratio of the request receiving times of the third type service to the request receiving comprehensive times, wherein the third type service comprises an analysis service, a data management service or a model related service.

17. The method of claim 2, further comprising: evaluating the performance of the first network entity according to the performance parameters, including at least one of:

determining an operational status of the first network entity based on at least one of the request success rate, the service success rate, the request failure rate, or the service failure rate; or (b)

And determining the resource occupation state of the service of the corresponding service type to the first network entity according to the request duty ratio of the service type.

18. The method of claim 17, further comprising at least one of:

determining at least one of resource allocation rationality, task allocation rationality or fault state of the first network entity according to the running state of the first network entity; or (b)

And allocating resources for the first network entity according to the resource occupation state.

19. The method of claim 1, further comprising:

determining a target service type;

the step of obtaining the interaction statistical information of the first network entity comprises the following steps: acquiring the interaction statistical information related to the target service type;

the determining the performance parameter of the first network entity according to the interaction statistical information comprises: and determining performance parameters related to the target service type of the first network entity according to the interaction statistical information related to the target service type.

20. The method of claim 1, wherein the method is consistent with at least one of:

the number of requests includes a number of at least one of service requests or service subscriptions; or (b)

The response times include times of at least one of service responses or subscription notifications.

21. The method of claim 1, further comprising:

acquiring a performance analysis request, wherein the acquisition of the interactive statistical information of the first network entity is performed under the triggering of the performance analysis request; and

and sending the determined performance parameters to one or more network elements.

22. The method of claim 1, wherein the first network entity comprises a network data analysis enabled entity.

23. An information acquisition apparatus comprising:

a statistic information obtaining unit configured to obtain interaction statistic information of a first network entity, where the interaction statistic information includes at least one of a request number received by the first network entity and a response number of the first network entity;

and the performance parameter determining unit is configured to determine the performance parameter of the first network entity according to the interaction statistical information.

24. The apparatus of claim 23, further comprising: a performance evaluation unit configured to evaluate the performance of the first network according to the performance parameters, comprising at least one of:

Determining an operational status of the first network based on at least one of the request success rate, the service success rate, the request failure rate, or the service failure rate; or (b)

And determining the resource occupation state of the service of the corresponding service type to the first network according to the request duty ratio of the service type.

25. The apparatus of claim 24, further comprising at least one of:

a state analysis unit configured to determine at least one of resource configuration rationality, task allocation rationality, or failure state of the first network according to an operational state of the first network; or (b)

And a resource allocation unit configured to allocate resources for the first network entity according to the resource occupancy state.

26. The apparatus of claim 23, further comprising: a type determining unit configured to determine a target service type;

the statistical information acquisition unit is configured to acquire the interaction statistical information related to the target service type;

the performance parameter determination unit is configured to determine a performance parameter related to the target service type of the first network entity based on the interaction statistics related to the target service type.

27. The apparatus of claim 23, further comprising:

the receiving unit is configured to acquire a performance analysis request, and trigger the statistical information acquisition unit to work according to the performance analysis request; and

and a transmitting unit configured to transmit the determined performance parameter to one or more network elements.

28. An information acquisition apparatus comprising:

a memory; and

a processor coupled to the memory, the processor configured to perform the method of any of claims 1-22 based on instructions stored in the memory.

29. A computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the method of any of claims 1 to 22.