Performance detection method and device, service system and storage medium

Abstract

The invention provides a method and device for performance detection, a service system and a storage medium, and relates to the technical field of communication. The method for performance detection comprises the following steps: obtaining a performance detection request; obtaining performance detection information of the target network entity, wherein the performance detection information comprises distribution information of data collection sources of the target network entity; and feeding back the performance detection information to the source end node of the performance detection request.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a method, device, service system and storage medium for performance detection. Background Art

[0002] The NWDAF (Network Data Analytics Function) entity is a network element in the 5GC (5G Core) that can collect data from different data sources for analysis, such as from NFs (Network Functions) and OAM (Operation Administration and Maintenance) for network analysis.

[0003] The data source of the NWDAF entity can be distributed at various locations in the network, and data transmission is achieved through the network. Summary of the invention

[0004] One purpose of the present disclosure is to propose a solution for monitoring the performance of a network entity, to provide data support for the deployment of the network entity, and to help improve the service capability of the network entity.

[0005] According to one aspect of some embodiments of the present disclosure, a method for performance detection is proposed, including: obtaining a performance detection request; obtaining performance detection information of a target network entity, the performance detection information including distribution information of a data collection source of the target network entity; and feeding back the performance detection information to a source node of the performance detection request.

[0006] In some embodiments, the distribution information includes a correspondence between data collection performance and a data source, and the data collection performance includes at least one of a data load or a signaling load.

[0007] In some embodiments, there are multiple data sources, and obtaining performance detection information of the target network entity includes: obtaining the correspondence information between the signaling load of the target network entity and the data source, including: for each data source, measuring the number of times the target network entity sends at least one of the data collection requests or subscriptions to the data source within a predetermined first time length, as a first number; measuring the number of times the target network entity receives at least one of the data collection responses or notifications from the data source within the predetermined first time length, as a second number; and obtaining the sum of the first number and the second number to determine the signaling load corresponding to the data source.

[0008] In some embodiments, obtaining performance detection information of the target network entity includes: obtaining correspondence information between the data load of the target network entity and the data source, including: for each data source, measuring at least one of the data packets of response or notification related to data collection received by the target network entity from the data source within a predetermined second time length as a third number; when the target network entity receives at least one of the data packets of response or notification related to data collection received from the data source within the predetermined second time length, counting the size of the statistical data packet; and determining the data load corresponding to the data source based on the third number and the size of the data packet.

[0009] In some embodiments, obtaining performance detection information of the target network entity includes: obtaining the correspondence between the data collection performance of the target network entity and the data source; determining the degree of discreteness of at least one of the signaling load or data load between the target network entity and the data source based on the correspondence, wherein the distribution information of the data collection source includes the degree of discreteness.

[0010] In some embodiments, the method further includes: determining to adjust one or more data sources of the target network entity, or a deployment of at least one of the target network entities according to the distribution information.

[0011] In some embodiments, determining to adjust one or more data sources of the target network entity, or the deployment of at least one of the target network entities based on the distribution information includes at least one of the following: determining to adjust one or more data sources of the target network entity, or the deployment of at least one of the target network entities when the discrete degree of the signaling load is greater than or equal to a predetermined first threshold; or determining to adjust one or more data sources of the target network entity, or the deployment of at least one of the target network entities when the discrete degree of the data load is greater than or equal to a predetermined second threshold.

[0012] In some embodiments, obtaining the data source measurement request includes: obtaining the data source measurement request by at least one of information subscription or information request.

[0013] In some embodiments, feeding back the performance detection information to the source node of the performance detection request includes: feeding back the performance detection information to the source node of the data source measurement request by at least one of notification or response.

[0014] In some embodiments, the target network entity comprises a NWDAF entity.

[0015] According to one aspect of some embodiments of the present disclosure, a device for performance detection is proposed, including: a request acquisition unit, configured to obtain a performance detection request; an information acquisition unit, configured to obtain performance detection information of a target network entity, the performance detection information including distribution information of a data collection source of the target network entity; an information feedback unit, configured to feed back the performance detection information to a source node of the performance detection request.

[0016] In some embodiments, the apparatus further includes: a deployment adjustment unit configured to determine, according to the distribution information, to adjust the deployment of the target network entity and one or more data sources of the target network entity.

[0017] According to one aspect of some embodiments of the present disclosure, a device for performance detection is proposed, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute any one of the above methods for performance detection based on instructions stored in the memory.

[0018] According to one aspect of some embodiments of the present disclosure, a non-transitory computer-readable storage medium is provided, on which computer program instructions are stored, and when the instructions are executed by a processor, the steps of any one of the above methods for performance detection are implemented.

[0019] According to one aspect of some embodiments of the present disclosure, a service system is proposed, comprising: a consumer entity, configured to send a performance detection request to a device for performance detection, and receive performance detection information fed back by the device for performance detection; a device for performance detection, configured to execute any one of the above methods for performance detection; and one or more NWDAF entities, configured to accept detection by the device for performance detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings:

[0021] Figure 1 The present invention is a flowchart of some embodiments of the method for performance detection of the present invention.

[0022] Figure 2 Flow charts of other embodiments of the method for performance detection disclosed in the present invention.

[0023] Figure 3A -C is a schematic diagram of some embodiments of information interaction in the method for performance detection disclosed in the present invention.

[0024] Figure 4Schematic diagrams of some embodiments of the device for performance detection disclosed in the present invention.

[0025] Figure 5 Schematic diagrams of other embodiments of the device for performance detection disclosed in the present invention.

[0026] Figure 6 Schematic diagrams of some further embodiments of the device for performance detection disclosed in the present invention.

[0027] Figure 7 Schematic diagrams of some embodiments of the service system disclosed herein. DETAILED DESCRIPTION

[0028] The technical solution of the present disclosure is further described in detail below through the accompanying drawings and embodiments.

[0029] The inventors found that the amount of data collected by NWDAF from different data sources may be different, and the deployment location of NWDAF and the data source will directly affect the transmission resources and transmission time consumed by NWDAF when collecting data. If NWDAF needs to collect more data from a data source that is deployed farther away and less data from a data source that is deployed closer, it will consume more transmission resources, resulting in longer transmission delays, affecting the timeliness of NWDAF's data collection and analysis.

[0030] Therefore, unreasonable deployment of NWDAF and data sources will affect the performance of NWDAF in collecting data, and further affect the performance of NWDAF in providing data analysis services.

[0031] In response to the above problems, the present disclosure proposes a method, device, service system and storage medium for performance detection, which monitors the situation of target network entities such as NWDAF entities collecting data from different data sources, understands the performance of NWDAF data collection, provides necessary reference for managers to optimize the deployment of NWDAF, and helps to improve the service capabilities of the target network entities.

[0032] The flowcharts of some embodiments of the method for performance detection disclosed in the present invention are as follows: Figure 1 In some embodiments, the method for performance detection may be performed by an executor node of a performance detection event of a target network entity, the executor node being signal-connected to one or more target network entities.

[0033] In step S11, a performance detection request is obtained. In some embodiments, the source node of the performance detection request is a node that has a detection demand for the target network entity, which can be called a consumer node of the performance detection event, such as an operator's network management node, or a node with authority to detect the target network entity. In some embodiments, the target network entity is a NWDAF entity. In some embodiments, the consumer node can send a performance detection request to the executor node by at least one of information subscription or information request.

[0034] In step S12, performance detection information of the target network entity is obtained, and the performance detection information includes distribution information of data collection sources of the target network entity. In some embodiments, the distribution information of data collection sources may include identifiers of data sources and data collection performance for each data source. In some embodiments, the distribution information of data collection sources includes a correspondence between each data source and the data collection performance for the data source.

[0035] In some embodiments, the data collection performance of the target network entity can be quantified and measured by the data load or signaling load when performing data collection. The data load can be used to evaluate the throughput of the target network entity when performing the data collection service, and the signaling load can be used to evaluate the signaling interaction frequency when the target network entity performs the data collection service. The data collection performance includes at least one of the data load or the signaling load.

[0036] In some embodiments, the performance detection information of the target network entity may be obtained by at least one of requiring the target network entity to actively report its own performance detection information, detecting the interface of the target network entity, monitoring the processor of the target network entity, etc.

[0037] In some embodiments, the distribution information of the data collection source may also include the discreteness of the data source. According to the correspondence between the collected data collection performance and the data source, the discreteness of the data source is further determined, so as to quantify the rationality of the deployment of the current target network entity and the data source, and improve the convenience of determining whether to adjust the deployment later.

[0038] In step S13, the performance detection information is fed back to the source node that sent the performance detection request in step S11. In some embodiments, the performance detection information is fed back to the source node of the data source measurement request by at least one of notification or response.

[0039] In some embodiments, if in the above step S11, the performance detection information is obtained by subscription, then in the current step, the performance detection information is fed back to the source node through notification; if in the above step S11, the performance detection information is obtained by request, then in the current step, the performance detection information is fed back to the source node through response.

[0040] Based on the method in the embodiment shown above, it is possible to quantify the situation in which the target network entity collects data from different data sources and determine the data source distribution of the target network entity, thereby providing a data basis for evaluating whether the current deployment of the target network entity and the data source is reasonable, which helps to improve the rationality of the deployment of the target network entity and the data source and improve the efficiency of data transmission.

[0041] In some embodiments, Figure 1 As shown, the method for performance detection further includes step S14.

[0042] In step S14, it is determined to adjust the data source or the deployment of at least one of the target network entities according to the distribution information, wherein the number of the data source may be one or more.

[0043] In some embodiments, taking the NWDAF node as an example, it is evaluated whether the discrete degree of the signaling load measurement between the NWDAF and different data sources is greater than or equal to a preconfigured threshold, and based on the evaluation result, it is determined whether the deployment of the relative position between the NWDAF and the data source needs to be updated; it is evaluated whether the discrete degree of the data load between the NWDAF and different data sources is greater than or equal to a preconfigured threshold, and based on the evaluation result, it is determined whether the deployment of the relative position between the NWDAF and the data source needs to be updated.

[0044] In some embodiments, the executor node of the performance detection event can generate an evaluation result or a deployment strategy and send it to the consumer node of the performance detection event; in some embodiments, the executor node of the performance detection event sends the generated evaluation result or deployment strategy to the operator's network management node or relevant personnel so that the deployment of the target network entity and data source can be adjusted in time.

[0045] Based on the method in the embodiment shown above, it is possible to determine whether the relative deployment position of the target network entity and the data source needs to be adjusted based on performance detection information, thereby improving the rationality of the resource configuration of the target network entity and improving the timeliness of the adjustment.

[0046] Flow charts of other embodiments of the method for performance detection disclosed in the present invention are as follows Figure 2 shown.

[0047] In step 211, a performance detection request is obtained. In some embodiments, the performance detection request may be a detection request for data collection performance of the target network entity for different data sources. In some embodiments, the performance detection request is sent by at least one of service subscription or sending request.

[0048] In some embodiments, the flowchart of the "subscribe-notify" service and the "request-response" service between NFs is as follows: Figure 3A , 3B As shown, the service user (NF_A) can subscribe to or request services from the service provider (NF_B) based on the "subscribe-notify" service and the "request-response" service.

[0049] In some embodiments, NF_A is a NWDAF, and the NWDAF can subscribe to or request services from the service provider (NF_B) based on the subscription-notification services in 311 and 312 , and the request-response services in 321 and 322 .

[0050] In some embodiments, the architecture of NWDAF collecting data from other 5GC NFs is as follows Figure 3C As shown, NWDAF can collect data from any other 5GC NF through the Nnf interface.

[0051] In some embodiments, according to the performance detection request, at least one of the following steps 2211 to 2213 and steps 2221 to 2223 is executed.

[0052] In steps 2211 to 2213, the correspondence information between the signaling load of the target network entity and the data source is obtained.

[0053] In step 2211, for each data source, the number of at least one of a data collection request or a subscription sent by the target network entity to the data source within a predetermined first time length is measured as a first number.

[0054] In some embodiments, taking NWDAF as the target network entity as an example, the number of data collection requests sent by NWDAF to other data sources within a period of time is measured: when NWDAF triggers a request / subscription related to data collection, the counterpart node of the request / subscription is determined, and a cumulative counter (CC) related to the number of data collection requests / subscriptions triggered by NWDAF and associated with the counterpart node is determined, and the CC value is incremented by 1; the value of the CC is the measured value of the number of data collection requests / subscriptions triggered by NWDAF for the counterpart node, which is the first number for the counterpart node.

[0055] In some embodiments, the type of data source (such as OAM, NF) can also be determined, statistics can be taken for each type of data source, and the first number associated with each type of data source can be determined, thereby achieving statistics on the data source type of the target network entity, facilitating statistics on the service type of the target network entity, and improving the understanding of the target network entity.

[0056] In step 2212, for each data source, the correspondence information between the data load of the target network entity and the data source is obtained.

[0057] In some embodiments, the number of data collection responses received by NWDAF from other data sources within a period of time is measured: when NWDAF receives a response / notification related to data collection, the peer node of the response / notification is determined, a cumulative counter (CC) related to the number of data collection responses / notifications received by NWDAF and associated with the peer node is determined, and the CC value is incremented by 1; the value of the CC is the measured value of the number of data collection responses / notifications received by NWDAF for the peer node, which is used as the second number for the peer node.

[0058] In step 2213, the sum of the first number and the second number is calculated, that is, the sum of the measured value of the number of data collection requests / subscriptions triggered by the target network entity for the same data source within a period of time and the measured value of the number of data collection responses / notifications received by the target network entity is used as the signaling load.

[0059] In some embodiments, in the above steps 2211 to 2213, the first number and the second number can also be counted for each type of data source to determine the signaling load of the target network entity for each type of data source, thereby facilitating the statistics of the service object type of the target network entity and improving the understanding of the target network entity.

[0060] In some embodiments, step 224 may be further performed to further process the signaling load. In some embodiments, the signaling load may be divided by the predetermined first time length to determine the signaling load amount per unit time, and then step 224 may be performed. By such a method, a longer predetermined first time length may be set, and then the deviation caused by accidental events may be averaged to improve the accuracy of signaling load statistics.

[0061] In some embodiments, step 231 may be further performed to feed back the correspondence between the signaling load and the data source to the source node so that the source node can obtain statistical data for further analysis and use to improve the utilization of the original data.

[0062] In steps 2221 to 2223, the correspondence information between the data load of the target network entity and the data source is obtained.

[0063] In step 2221, for each data source, at least one of the response or notification data packets related to data collection received by the target network entity from the data source within a predetermined second time length is measured as a third number.

[0064] In some embodiments, taking NWDAF as the target network entity, the number of data packets received by NWDAF from other data sources within a period of time is measured: when NWDAF receives a response / notification data packet related to data collection, the peer node of the response / notification is determined, and the value of a cumulative counter (CC) related to the number of data packets received by NWDAF and associated with the peer node is incremented by 1; the value of the CC is the measured value of the number of data packets received by NWDAF for the peer node, which is used as the third number.

[0065] In step 2222, when the target network entity receives at least one of the response or notification data packets related to data collection within the predetermined second time length, the size of the first data packet is counted.

[0066] In some embodiments, the size of data packets received by NWDAF from other data sources over a period of time is measured: when NWDAF receives a response / notification data packet related to data collection, it determines the requesting / subscribing peer node and measures the size of the data packet in bits or bytes; for each bit or byte collected, the value of a cumulative counter (CC) associated with the peer node for counting the size of statistical data packets is incremented by 1; the value of the CC is the measured value of the data packet size received by NWDAF for the peer node.

[0067] In step 2223, the data load is determined based on the third number and the size of the data packet.

[0068] In some embodiments, the measured values ​​of the sizes of data packets that meet the third number may be accumulated according to the third number to determine the data load for the data source, thereby ensuring the accuracy of the data load statistics.

[0069] In some embodiments, when the sizes of the data packets are the same or have small differences, step 2222 may be performed only for one data packet, and the data load for the data source may be determined by multiplying the third divisor by the measured value of the data packet size, thereby reducing the amount of computational effort for data monitoring and processing.

[0070] In some embodiments, in the above steps 2221 to 2223, statistics on the third number and size of data packets can also be performed for each type of data source to determine the data load of the target network entity for each type of data source, thereby facilitating statistics on the service object type of the target network entity and improving the understanding of the target network entity.

[0071] In some embodiments, step 224 may be further performed to further process the data load. In some embodiments, the data load may be divided by the predetermined second time length to determine the data load amount per unit time, and then step 224 may be performed. In this way, a longer predetermined first time length may be set, and then the deviation caused by accidental events may be averaged to improve the accuracy of data load statistics.

[0072] In some embodiments, step 231 may be further performed to feed back the correspondence between the data load and the data source to the source node so that the source node can obtain statistical data for further analysis and use to improve the utilization of the original data.

[0073] In some embodiments, the predetermined first time length is equal to the predetermined second time length. The processing results of step 2213 and step 2223 are combined, such as adding the two together as the data collection amount, and step 231 is performed to improve the comprehensiveness of the data collection performance measurement. In some embodiments, after obtaining the data collection amount, the data collection rate can be determined by dividing it by the predetermined first time length, thereby avoiding deviations caused by accidental events and improving the accuracy of data collection performance statistics.

[0074] In step 224, the correspondence between the data collection performance of the target network entity and the data source is obtained. In some embodiments, the signaling load and data load obtained in the above steps 2211 to 2213 and 2221 to 2223 are not counted for each data source or each data type, so the association between the signaling load and the data source (or data source type) and the association between the data load and the data source (or data source type) can be obtained. In some embodiments, the signaling load and the data load of the same data source (or data source type) can be added to determine the correspondence between the data collection performance and the data source (or data source type).

[0075] In some embodiments, the signaling load or the data load may be used as the data collection performance, and the correspondence between the data collection performance and the data source may be determined based on the data obtained in the above steps 2211 to 2213 and 2221 to 2223.

[0076] In step 225, the degree of dispersion of at least one of the signaling load or the data load between the target network entity and the data source is determined according to the corresponding relationship, wherein the distribution information of the data collection source includes the degree of dispersion.

[0077] In some embodiments, the degree of discreteness can be calculated based on any algorithm for calculating the degree of discreteness in the relevant technology and according to the correspondence between the data collection performance and the data source. For example, the variance can be used to measure the degree of discreteness of the signaling load or data load between the target network entity and different data sources, or the degree of discreteness of the signaling load or data load between the target network entity and different data sources can be measured based on the ratio of performance indicators between the target network entity and different data sources.

[0078] In step 231, the performance detection information obtained in the previous step is fed back to the source node of the performance detection request.

[0079] Based on the method in the embodiment shown above, signaling load and data load can be used as reference parameters for measuring the data collection distribution of the target network entity and the data source, so as to implement data collection distribution analysis for each data source or each type of data source, and determine whether it is necessary to adjust the relative deployment position of the target network entity and the data source, thereby improving the rationality of the resource allocation of the target network entity, which is beneficial to saving network resource consumption and improving the service capability of the target network entity.

[0080] In some embodiments, it may also be determined whether to adjust the deployment of the target network entity or data source according to the performance detection result.

[0081] In some embodiments, when the discrete degree of the signaling load is greater than or equal to a predetermined first threshold, it is determined to adjust the deployment of at least one of the data source or the target network entity. In some embodiments, the number of the adjusted data sources may be one or more. In some embodiments, the adjustment strategy may be, when the distribution of the data source is relatively concentrated but the distance from the target network entity is relatively far, adjust the location of the target network entity to reduce the information transmission distance between the data source and the target network entity; when the distribution of the data source is relatively dispersed and there is a data source with a large signaling load (greater than a predetermined first high threshold) and a long distance (greater than a predetermined first transmission distance) from the target network entity, adjust the location of the data source with a large signaling load and a long distance from the target network entity; in some embodiments, if the signaling load of the data source far away from the target network entity is small (lower than a predetermined first low threshold), there is no need to adjust the location of the data source to reduce the burden of unnecessary adjustments and data migration.

[0082] In some embodiments, when the degree of discreteness of the data load is greater than or equal to a predetermined second threshold, it is determined to adjust the deployment of at least one of the data source or the target network entity. In some embodiments, the number of data sources to be adjusted may be one or more. In some embodiments, the adjustment strategy may be, when the distribution of the data source is relatively concentrated but the distance from the target network entity is relatively far, adjust the position of the target network entity to reduce the information transmission distance between the data source and the target network entity; when the distribution of the data source is relatively dispersed and there is a data source with a large data load (greater than the predetermined second high threshold) and a long distance (greater than the predetermined second transmission distance) from the target network entity, adjust the position of the data source with a large data load and a long distance from the target network entity; in some embodiments, if the data load of the data source far away from the target network entity is small (lower than the predetermined second low threshold), there is no need to adjust the position of the data source to reduce the burden of adjustment and data migration.

[0083] In some embodiments, the data collection load can also be determined based on the sum of the data load and the signaling load. When the discrete degree of the data collection load is greater than or equal to the predetermined third threshold, it is determined to adjust the deployment of at least one of the data source or the target network entity. In some embodiments, the number of adjusted data sources can be one or more. In some embodiments, the adjustment strategy can be, when the distribution of the data source is relatively concentrated but the distance from the target network entity is relatively far, adjust the position of the target network entity to reduce the information transmission distance between the data source and the target network entity; when the distribution of the data source is relatively dispersed and there is a data source with a large data collection load (greater than the predetermined third high threshold) and a long distance (greater than the predetermined third transmission distance) from the target network entity, adjust the position of the data source with a large data collection load and a long distance from the target network entity; in some embodiments, if the data collection load of the data source far away from the target network entity is small (lower than the predetermined third low threshold), there is no need to adjust the position of the data source to reduce the burden of adjustment and data migration.

[0084] In some embodiments, the predetermined first, second, and third high thresholds may be the same or different, the predetermined first, second, and third low thresholds may be the same or different, and the predetermined first, second, and third transmission distances may be the same or different, thereby improving the flexibility of the configuration.

[0085] Based on the methods in the various embodiments shown above, it is possible to determine from multiple perspectives whether the deployment of the target network entity or data source needs to be adjusted according to the performance detection information, thereby reducing the processing difficulty of subsequent deployment adjustments and improving the implementation efficiency of deployment adjustments.

[0086] Schematic diagrams of some embodiments of the device for performance detection disclosed in the present invention are as follows Figure 4 shown.

[0087] The request acquisition unit 401 can acquire a performance detection request. In some embodiments, the source node of the performance detection request is a node that has a detection demand for the target network entity, which can be called a consumer node of the performance detection event, such as a network management node of an operator, or a node with authority to detect the target network entity. In some embodiments, the target network entity is a NWDAF entity. In some embodiments, the request acquisition unit 401 can acquire the performance detection request by receiving at least one of a subscription or an information request.

[0088] The information detection unit 402 can obtain performance detection information of the target network entity, and the performance detection information includes distribution information of the data collection source of the target network entity. In some embodiments, the distribution information of the data collection source may include an identifier of the data source and data collection performance for each data source. In some embodiments, the distribution information of the data collection source includes a corresponding relationship between each data source and the data collection performance for the data source.

[0089] In some embodiments, the information detection unit 402 may perform the above steps as follows: Figure 2 At least one of steps 2211 to 2213, steps 2221 to 2223, and the performance detection operations in steps 224 and 225 of the illustrated embodiment.

[0090] The information feedback unit 403 feeds back the performance detection information to the source node of the performance detection request. In some embodiments, the performance detection information can be fed back to the source node of the data source measurement request by at least one of notification or response.

[0091] Such a device can quantify the situation of the target network entity collecting data from different data sources and determine the data source distribution of the target network entity, thereby providing a data basis for evaluating whether the current deployment of the target network entity and the data source is reasonable, helping to improve the rationality of the deployment of the target network entity and the data source and improve the efficiency of data transmission.

[0092] In some embodiments, Figure 4As shown, the device for performance detection also includes a deployment adjustment unit 404, which can determine to adjust the data source, or the deployment of at least one of the target network entities according to the distribution information, wherein the number of data sources can be one or more. In some embodiments, taking the NWDAF node as an example, it is evaluated whether the discrete degree of the signaling load measurement between the NWDAF and different data sources is greater than or equal to a preconfigured threshold, and based on the evaluation result, it is determined whether the deployment of the relative position between the NWDAF and the data source needs to be updated; it is evaluated whether the discrete degree of the data load between the NWDAF and different data sources is greater than or equal to a preconfigured threshold, and based on the evaluation result, it is determined whether the deployment of the relative position between the NWDAF and the data source needs to be updated.

[0093] Such a device can determine whether the relative deployment position of the target network entity and the data source needs to be adjusted based on the performance detection information, thereby improving the rationality of the resource configuration of the target network entity and improving the timeliness of the adjustment.

[0094] A schematic diagram of the structure of an embodiment of the device for performance testing disclosed in the present invention is shown in FIG. Figure 5 As shown. The device for performance detection includes a memory 501 and a processor 502. Wherein: the memory 501 can be a disk, a flash memory or any other non-volatile storage medium. The memory is used to store the instructions in the corresponding embodiment of the method for performance detection described above. The processor 502 is coupled to the memory 501 and can be implemented as one or more integrated circuits, such as a microprocessor or a microcontroller. The processor 502 is used to execute the instructions stored in the memory, and can provide a data basis for evaluating whether the current deployment of the target network entity and the data source is reasonable, which helps to improve the rationality of the deployment of the target network entity and the data source and improve the efficiency of data transmission.

[0095] In one embodiment, it is also possible to Figure 6 As shown, the device 600 for performance testing includes a memory 601 and a processor 602. The processor 602 is coupled to the memory 601 via a BUS 603. The device 600 for performance testing can also be connected to an external storage device 605 via a storage interface 604 to call external data, and can also be connected to a network or another computer system (not shown) via a network interface 606. No further detailed description will be given here.

[0096] In this embodiment, by storing data instructions in a memory and then processing the instructions through a processor, a data basis can be provided for evaluating whether the current deployment of the target network entity and the data source is reasonable, which helps to improve the rationality of the deployment of the target network entity and the data source and improve the efficiency of data transmission.

[0097] In another embodiment, a computer-readable storage medium stores thereon computer program instructions, which, when executed by a processor, implement the steps of the method in the corresponding embodiment of the method for performance detection. It should be understood by those skilled in the art that the embodiments of the present disclosure may be provided as methods, devices, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable non-transient storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0098] Schematic diagrams of some embodiments of the service system disclosed in the present invention are as follows Figure 7 shown.

[0099] The consumer entity 71 of the performance detection event can send a performance detection request to the device for performance detection, and receive performance detection information fed back by the device for performance detection. In some embodiments, the consumer entity 71 is a node that has a detection demand for the target network entity, such as a network management node of an operator, or a node with authority to detect the target network entity.

[0100] The device 72 for performance detection serves as a provider entity of performance detection events and may be any of the devices for performance detection mentioned above, and may execute any of the methods for performance detection mentioned above.

[0101] The service system also includes at least one NWDAF node 731-73n, where n is a positive integer. The NWDAF node supports collecting data from NFs, AFs and OAM, and provides analysis information to NFs, AFs and OAM, and can be detected by devices used for performance detection.

[0102] In such a service system, the data collected by the target network entity from different data sources can be quantified and processed, and the data source distribution of the target network entity can be determined, thereby providing a data basis for evaluating whether the current deployment of the target network entity and the data source is reasonable, which helps to improve the rationality of the deployment of the target network entity and the data source and improve the efficiency of data transmission.

[0103] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0104] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0105] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0106] So far, the present disclosure has been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Based on the above description, those skilled in the art can fully understand how to implement the technical solution disclosed here.

[0107] The method and apparatus of the present disclosure may be implemented in many ways. For example, the method and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present disclosure. Therefore, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0108] It should be noted that the terms "first", "second", etc. in the specification, claims, and drawings of the present disclosure are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present disclosure rather than to limit it. Although the present disclosure has been described in detail with reference to the preferred embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present disclosure can still be modified or some technical features can be replaced by equivalents without departing from the spirit of the technical solution of the present disclosure, which should be included in the scope of the technical solution for protection of the present disclosure.

Claims

1. A method for performance testing, comprising: Get performance test request; Acquire performance detection information of a target network entity, wherein the performance detection information includes distribution information of a data collection source of the target network entity; The performance detection information is fed back to the source node of the performance detection request.

2. The method according to claim 1, wherein: The distribution information includes a correspondence between data collection performance and a data source, and the data collection performance includes at least one of a data load or a signaling load.

3. The method according to claim 2, wherein: The data sources are multiple, and the acquiring the performance detection information of the target network entity includes: acquiring the corresponding relationship information between the signaling load of the target network entity and the data source, including: For each data source, measuring the number of at least one of a data collection request or a subscription sent by the target network entity to the data source within a predetermined first time length as a first number; measuring the number of times the target network entity receives at least one of a data collection response or a notification from the data source within a predetermined first time length, as a second number; and Obtain the sum of the first number of times and the second number of times, and determine the signaling load corresponding to the data source.

4. The method according to claim 1, wherein: The obtaining of the performance detection information of the target network entity includes: obtaining the corresponding relationship information between the data load and the data source of the target network entity, including: For each data source, measuring at least one of the response or notification data packets related to data collection received by the target network entity from the data source within a predetermined second time length as a third number; When the target network entity receives at least one of the response or notification data packets related to data collection from the data source within the predetermined second time period, counting the size of the data packet; and The data load corresponding to the data source is determined according to the third number and the size of the data packet.

5. The method according to claim 2, wherein: The obtaining of the performance detection information of the target network entity includes: Acquire the corresponding relationship between the data collection performance and the data source of the target network entity; The degree of discreteness of at least one of the signaling load or the data load between the target network entity and the data source is determined according to the corresponding relationship, wherein the distribution information of the data collection source includes the degree of discreteness.

6. The method according to claim 1, further comprising: Determine, based on the distribution information, to adjust one or more data sources of the target network entity, or a deployment of at least one of the target network entities.

7. The method according to claim 6, wherein: The step of determining, according to the distribution information, to adjust one or more data sources of the target network entity, or the deployment of at least one of the target network entities, comprises at least one of the following: In a case where the discrete degree of the signaling load is greater than or equal to a predetermined first threshold, determining to adjust one or more data sources of the target network entity, or a deployment of at least one of the target network entities; or In a case where the discrete degree of the data load is greater than or equal to a predetermined second threshold, it is determined to adjust one or more data sources of the target network entity, or the deployment of at least one of the target network entities.

8. The method according to claim 1, wherein: The request for obtaining data source measurement includes: The data source measurement request is obtained by at least one of information subscription and information request.

9. The method according to claim 1, wherein: The step of feeding back the performance detection information to the source node of the performance detection request includes: The performance detection information is fed back to the source node of the data source measurement request through at least one of notification and response.

10. The method according to any one of claims 1 to 9, wherein: The target network entity includes a network data analysis function NWDAF entity.

11. A device for performance testing, comprising: A request acquisition unit, configured to acquire a performance detection request; an information acquisition unit, configured to acquire performance detection information of a target network entity, wherein the performance detection information includes distribution information of a data collection source of the target network entity; The information feedback unit is configured to feed back the performance detection information to the source node of the performance detection request.

12. The apparatus according to claim 11, further comprising: The deployment adjustment unit is configured to determine to adjust the deployment of the target network entity and one or more data sources of the target network entity according to the distribution information.

13. A device for performance testing, comprising: Memory; as well as A processor coupled to the memory, the processor being configured to execute the method according to any one of claims 1 to 10 based on instructions stored in the memory.

14. A non-transitory computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 10.

15. A service system comprising: The consumer entity is configured to send a performance detection request to the device for performance detection, and receive performance detection information fed back by the device for performance detection; A device for performance testing, configured to perform the method according to any one of claims 1 to 10; and One or more network data analysis function NWDAF entities are configured to accept detection by the device for performance detection.

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