Performance detection method and device, service system and storage medium

Abstract

The present disclosure provides a performance detection method and device, a service system and a storage medium, and relates to the technical field of communication. The performance detection method comprises the following steps: obtaining a performance detection request; obtaining performance detection information of a target network entity, wherein the performance detection information comprises at least one of data collection performance or data processing performance of the target network entity; and feeding back the performance detection information to a source node of the performance detection request.

Description

TECHNICAL FIELD

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

[0002] The NWDAF (Network Data Analytics Function) entity is a network element in the 5GC (5G Core), which supports collecting data from NFs (Network Functions), AFs (Application Functions) and OAM (Operation Administration and Maintenance), and providing analysis information to the NFs, AFs and OAM.

[0003] The amount of storage resources and computing resources of the NWDAF depends on the configuration of the operator, and the rationality of the configuration will affect the performance of the NWDAF in providing data analysis services. SUMMARY

[0004] One purpose of the present disclosure is to provide a scheme for monitoring the performance of a network entity, which helps to improve the efficiency and rationality of resource configuration of the network entity.

[0005] According to an aspect of some embodiments of the present disclosure, a performance detection method is provided, comprising: obtaining a performance detection request; obtaining performance detection information of a target network entity, the performance detection information including at least one of data collection performance or data processing performance 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 data collection performance includes at least one of data load or signaling load.

[0007] In some embodiments, obtaining the performance detection information of the target network entity includes: obtaining the signaling load of the target network entity, comprising: measuring the number of at least one of data collection requests or subscriptions sent by the target network entity within a predetermined first time length as a first number; measuring the number of at least one of data collection responses or notifications received by the target network entity within the predetermined first time length as a second number; and obtaining the sum of the first number and the second number as the signaling load.

[0008] In some embodiments, obtaining the performance detection information of the target network entity comprises: obtaining a data load of the target network entity, including: measuring a third number of at least one of response or notification first data packets related to data collection received by the target network entity within a predetermined second time length; when the target network entity receives at least one of the response or notification first data packets related to data collection within the predetermined second time length, counting a size of the first data packet; and determining the data load according to the third number and the size of the first data packet.

[0009] In some embodiments, obtaining the performance detection information of the target network entity comprises: obtaining a data processing performance of the target network entity, including: determining a fourth number of at least one of response or notification second data packets processed by the target network entity within a predetermined third time length; when the target network entity processes at least one of the response or notification second data packets related to data collection within the predetermined third time length, counting a size of the second data packet; determining a data processing amount according to the fourth number and the size of the second data packet; and determining the data processing performance according to the data processing amount and the predetermined third time length.

[0010] In some embodiments, the data collection performance further comprises a data collection rate; and obtaining the performance detection information of the target network entity comprises: determining the data collection rate according to at least one of the signaling load and a time length of counting the signaling load, or the data load and a time length of counting the data load.

[0011] In some embodiments, the method further comprises: adjusting a resource configuration of the target network entity according to the performance detection information.

[0012] In some embodiments, adjusting the resource configuration of the target network entity according to the performance detection information comprises at least one of: determining to adjust a storage resource configuration of the target network entity when the signaling load is greater than or equal to a predetermined signaling load threshold; determining to adjust the storage resource configuration of the target network entity when the data load is greater than or equal to a predetermined data load threshold; determining to adjust a computing resource configuration of the target network entity when a data processing rate is greater than or equal to a predetermined processing rate threshold; or determining to adjust at least one of the storage resource configuration or the computing resource configuration of the target network entity when the data collection rate is greater than the data processing performance, and a difference is greater than a predetermined difference threshold.

[0013] In some embodiments, feeding back the performance detection information to a source node of the performance detection request comprises at least one of: feeding back at least one of the signaling load, the data load, the data collection rate, or the data processing rate to the source node of the performance detection request.

[0014] In some embodiments, the obtaining the performance detection request comprises: obtaining the performance detection request by at least one of information subscription or information request.

[0015] In some embodiments, the feeding back the performance detection information to the source node of the performance detection request comprises: feeding back the performance detection information to the source node of the performance detection request by at least one of notification or response.

[0016] In some embodiments, the target network entity comprises a NWDAF entity.

[0017] According to an aspect of some embodiments of the present disclosure, a performance detection apparatus is provided, comprising: a request obtaining unit configured to obtain a performance detection request; an information obtaining unit configured to obtain performance detection information of a target network entity, the performance detection information comprising at least one of data collection performance of the target network entity or data processing performance of the target network entity; and an information feeding back unit configured to feed back the performance detection information to a source node of the performance detection request.

[0018] In some embodiments, the apparatus further comprises: a configuration adjusting unit configured to adjust resource configuration of the target network entity according to the performance detection information.

[0019] According to an aspect of some embodiments of the present disclosure, a performance detection apparatus is provided, comprising: a memory; and a processor coupled to the memory, the processor being configured to execute any of the performance detection methods described above based on instructions stored in the memory.

[0020] According to an aspect of some embodiments of the present disclosure, a non-transitory computer readable storage medium is provided, having computer program instructions stored thereon, the instructions being executed by a processor to implement the steps of any of the performance detection methods described above.

[0021] According to an aspect of some embodiments of the present disclosure, a service system is provided, comprising: a consumer entity configured to send a performance detection request to a performance detection apparatus and receive performance detection information fed back by the performance detection apparatus; the performance detection apparatus configured to execute any of the performance detection methods described above; and one or more NWDAF entities configured to accept detection by the performance detection apparatus. BRIEF DESCRIPTION OF DRAWINGS

[0022] The accompanying drawings, which are included to provide a further understanding of the present disclosure and constitute a part of the present disclosure, illustrate the exemplary embodiments of the present disclosure and serve to explain the present disclosure together with the description. In the drawings:

[0023] Figure 1 Flowchart of some embodiments of the performance detection method of the present disclosure.

[0024] Figure 2 Flowchart of some embodiments of the performance detection method of the present disclosure.

[0025] Figures 3A-3C Schematic diagram of some embodiments of information interaction in the performance detection method of the present disclosure.

[0026] Figure 4 Schematic diagram of some embodiments of the performance detection device of the present disclosure.

[0027] Figure 5 Schematic diagram of some other embodiments of the performance detection device of the present disclosure.

[0028] Figure 6 Schematic diagram of some other embodiments of the performance detection device of the present disclosure.

[0029] Figure 7 Schematic diagram of some embodiments of the service system of the present disclosure. DETAILED DESCRIPTION

[0030] The technical solutions of the present disclosure are described in further detail below with reference to the accompanying drawings and embodiments.

[0031] The inventors have found that if each data source sends too much data and signaling to the NWDAF, the NWDAF can not be able to store and process the data in time, thereby causing data loss, and the more data loss, the greater the impact on the data collection performance and data analysis performance of the NWDAF.

[0032] In related technologies, the NWDAF can take some measures to reduce the data load and the signaling load, in order to reduce the impact of limited storage resources on data collection performance, such as reducing the amount of collected data and the signaling load by prioritizing the requests of analysis consumers, reducing the range (e.g. duration) of data collection, modifying the sampling rate, etc., but the inventors have found that this does not fundamentally solve the problem of unreasonable configuration of storage resources and computing resources.

[0033] To solve the above problems, the present disclosure proposes a performance detection method, device and service system, and a storage medium, which monitors network entities such as NWDAF, in order to provide necessary reference for operators to optimize the configuration of storage resources and computing resources of network entities.

[0034] The flowchart of some embodiments of the performance detection method of the present disclosure is shown in Figure 1 In some embodiments, the performance detection method can be executed by an executor node of a performance detection event of a target network entity, and the executor node is in signal connection with one or more target network entities.

[0035] In step S11, a performance detection request is acquired. 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 referred to as a consumer node of the performance detection event, such as a network management node of an operator, or a node that has the authority to detect the target network entity. In some embodiments, the target network entity is an NWDAF entity. In some embodiments, the consumer node can send the performance detection request to the performer node in at least one of the following manners: information subscription or information request.

[0036] In step S12, performance detection information of the target network entity is acquired, and the performance detection information includes at least one of data collection performance or data processing performance of the target network entity. In some embodiments, the data collection performance of the target network entity can be quantified and measured by data load or signaling load during 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 of the target network entity when performing the data collection service. The data collection performance includes at least one of the data load or the signaling load.

[0037] In some embodiments, the performance detection information of the target network entity can be acquired in at least one of the following manners: 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.

[0038] In step S13, the performance detection information is fed back to the source node of the performance detection request.

[0039] In some embodiments, all types or part of the types of the acquired performance detection information can be fed back to the source node according to the category of the performance detection information in the performance detection request of the source node, or the category of the currently detected performance detection information. In some embodiments, at least one of the signaling load, the data load, the data collection rate, or the data processing rate is fed back to the source node of the performance detection request.

[0040] Based on the manner in the above-mentioned embodiments, the data collection and processing performance of the target network entity can be quantified, and the data collection and processing performance of the target network entity can be determined in a timely manner, which improves the state monitoring capability of the target network entity, provides a reference for the storage resource and computing resource configuration of the network entity, and thus helps to improve the efficiency and rationality of the resource configuration of the network entity.

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

[0042] In step S14, the resource configuration of the target network entity is adjusted according to the performance detection information.

[0043] In some embodiments, for example, the NWDAF node, it is evaluated whether the observed signaling load measurement value is greater than or equal to the preconfigured signaling load threshold value, and based on the evaluation result, it is judged whether to update the storage resource configuration of the NWDAF; it is evaluated whether the observed data processing rate measurement value is less than the preconfigured data processing rate threshold value, and based on the evaluation result, it is judged whether to update the computing resource configuration of the NWDAF; it is evaluated whether the observed data processing rate measurement value is less than the preconfigured data processing rate threshold value, and based on the evaluation result, it is judged whether to update the computing resource configuration of the NWDAF; it is evaluated whether the observed data collection rate is much greater than the data processing rate (for example, the difference between the data collection rate and the data processing rate is greater than a predetermined difference threshold value), and if the data collection rate is much greater than the data processing rate, it is possible that the computing resource or storage resource configuration is unreasonable, and the storage resource or computing resource configuration of the NWDAF needs to be updated based on the evaluation result.

[0044] In some embodiments, the resource configuration strategy can be generated by the performer node of the performance detection event and sent to the consumer node of the performance detection event; in some embodiments, the performer node of the performance detection event sends the generated resource configuration strategy to the network management node of the operator or relevant personnel, so that the resource configuration of the target network entity can be adjusted in time. Such a way has low requirements for the data processing capability of the performer node and is conducive to global adjustment by the operator.

[0045] In some embodiments, the performer node of the performance detection event generates the resource configuration strategy and can adjust the resource configuration of the target network entity by itself, thereby improving the timeliness of the resource configuration of the target network entity and reducing the burden of the network management node and signaling transmission.

[0046] Based on the method in the above-mentioned embodiments, the resource configuration of the target network entity can be adjusted in time based on the performance detection information, thereby improving the rationality of the resource configuration of the target network entity and the adaptability to network state changes, and improving the intelligent degree of the network node.

[0047] The flowchart of another embodiment of the performance detection method of the present disclosure is shown in Figure 2 .

[0048] In step 211, a performance detection request is acquired. In some embodiments, the performance detection request can be a detection request for data collection performance of the target network entity, in some embodiments, a detection request for signaling load of the target network entity, in some embodiments, a detection request for data load of the target network entity. In some embodiments, the performance detection request can be a detection request for data processing amount of the target network entity. In this way, the corresponding detection can be performed as needed, reducing the detection burden and improving the pertinence.

[0049] In some embodiments, the performance detection request can be a detection request for data collection performance and data processing amount of the target network entity, so as to perform comprehensive detection for signaling load, data load and data processing, improving the comprehensiveness of performance detection.

[0050] In some embodiments, the flowchart of the "subscription-notification" service and the "request-response" service between NFs is as shown in Figure 3A 、 3B The service user (NF_A) can subscribe to or request services from the service provider (NF_B) based on the "subscription-notification" service and the "request-response" service.

[0051] In some embodiments, the NF_A is the NWDAF, which can subscribe to or request services such as data collection from the service provider (NF_B) based on the subscription-notification service in 311 and 312, and the request-response service in 321 and 322.

[0052] In some embodiments, the architecture of the NWDAF collecting data from other 5GC NFs is as shown in Figure 3C The NWDAF can collect data from any other 5GC NF through the Nnf interface.

[0053] In some embodiments, according to the performance detection request, at least one of the following steps 2211-2213, steps 2221-2223, steps 2231-2234 is performed.

[0054] In steps 2211-2213, the signaling load of the target network entity is acquired.

[0055] In step 2211, the number of at least one of the data collection request or the subscription sent by the target network entity within a predetermined first time length is measured as a first number.

[0056] In some embodiments, taking the NWDAF as an example of the target network entity, the number of data collection requests sent by the NWDAF to other data sources in a period of time is measured: when the NWDAF triggers a data collection related request / subscription, the value of a cumulative counter (CC) related to the number of data collection requests / subscription triggered by the NWDAF is incremented by 1; the value of the CC is the measurement value of the number of data collection requests / subscription triggered by the NWDAF, as the first number.

[0057] In step 2212, the number of at least one of data collection responses or notifications received by the target network entity in a predetermined first length of time is measured as the second number.

[0058] In some embodiments, the number of data collection responses received by the NWDAF from other data sources in a period of time is measured: when the NWDAF receives a data collection related response / notification, the value of a cumulative counter (CC) related to the number of data collection responses / notifications received by the NWDAF is incremented by 1; the value of the CC is the measurement value of the number of data collection responses / notifications received by the NWDAF, as the second number.

[0059] In step 2213, the sum of the first number and the second number, i.e., the sum of the measurement value of the number of data collection requests / subscription triggered by the target network entity and the measurement value of the number of data collection responses / notifications received by the target network entity in a period of time, is calculated as the signaling load.

[0060] In some embodiments, step 231 can be further performed to feed back the signaling load to the source node. In some embodiments, the signaling load can be divided by the predetermined first length of time to determine the amount of signaling load per unit time, which is fed back to the source node. In this way, a longer predetermined first length of time can be set, and the deviation caused by incidental events can be averaged out, thereby improving the accuracy of the signaling load statistics.

[0061] In steps 2221-2223, the data load of the target network entity is obtained.

[0062] In step 2221, the number of at least one of the data packets of the responses or notifications related to data collection received by the target network entity in a predetermined second length of time is measured as the third number.

[0063] In some embodiments, taking the NWDAF as an example of the target network entity, the number of data packets received by the NWDAF from other data sources in a period of time is measured: when the NWDAF receives a data collection related response / notification data packet, the value of a cumulative counter (CC) related to the number of data packets received by the NWDAF is incremented by 1; the value of the CC is the measurement value of the number of data packets received by the NWDAF, as the third number.

[0064] In step 2222, when the target network entity receives at least one of the response or the notification data packet related to data collection within the predetermined second time length, the size of the first data packet is counted.

[0065] In some embodiments, the NWDAF measures the size of the data packet received from other data sources within a period of time: when the NWDAF receives a data collection related response / notification data packet, the size of the data packet is measured in bits or bytes; for each 1 bit or 1 byte collected, the value of the related cumulative counter (CC) is incremented by 1; the value of the CC is the measurement value of the size of the data packet received by the NWDAF.

[0066] In step 2223, the data load is determined according to the third number and the size of the data packet.

[0067] In some embodiments, the data load can be determined by accumulating the measurement values of the sizes of the data packets that meet the third number, according to the third number, to ensure the accuracy of the data load statistics.

[0068] In some embodiments, in the case of the same or small difference in the size of the data packet, step 2222 can be performed only for one data packet, and then the data load can be determined by multiplying the third number by the measurement value of the size of the data packet, thereby reducing the computational load of data monitoring and processing.

[0069] In some embodiments, step 231 can be further performed to feed back the data load to the source node. In some embodiments, the data load can be divided by the predetermined second time length to determine the data load amount per unit time, which is fed back to the source node. In this way, a longer predetermined second time length can be set, and then the average method can be used to avoid the deviation caused by accidental events, thereby improving the accuracy of data load statistics.

[0070] In some embodiments, the predetermined first time length and the predetermined second time length are equal. The processing results of steps 2213 and 2223 are added together as the data collection amount, and step 231 is performed, thereby improving 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 the data collection amount by the predetermined first time length, thereby avoiding the deviation caused by accidental events and improving the accuracy of data collection performance statistics.

[0071] In steps 2231-2234, the data processing performance of the target network entity is obtained.

[0072] In step 2231, at least one of the received response or notification data packets processed by the target network entity within the predetermined third length of time is determined as the fourth number.

[0073] In some embodiments, taking the NWDAF as an example of the target network entity, the number of data packets processed by the NWDAF within a period of time is measured: when the NWDAF processes the received response / notification data packet, the value of a cumulative counter (CC) related to the number of data packets processed by the NWDAF is incremented by 1; the value of the CC is the measurement value of the number of data packets processed by the NWDAF, as the fourth number.

[0074] In step 2232, when the target network entity processes at least one of the response or notification data packets related to data collection within the predetermined third length of time, the size of the data packet is counted.

[0075] In some embodiments, the size of the data packet processed by the NWDAF within a period of time is measured: when the NWDAF processes the received response / notification data packet, the size of the data packet is measured in bits or bytes, and the value of a related cumulative counter (CC) is incremented by 1 for each bit or byte processed; the value of the CC is the measurement value of the size of the data packet processed by the NWDAF.

[0076] In step 2233, the data processing amount is determined according to the fourth number and the size of the data packet. In some embodiments, the data processing amount can be taken as the data processing performance, and the subsequent step 231 is performed. In some embodiments, step 2234 can also be continued.

[0077] In some embodiments, the measurement value of the size of the data packet that meets the fourth number can be accumulated according to the fourth number to determine the data load, so as to ensure the accuracy of the data processing amount statistics. In some embodiments, in the case where the sizes of the data packets are the same or have little difference, step 2232 can be performed only for one data packet, and then the data processing amount is determined by multiplying the fourth number by the measurement value of the size of the data packet, so as to reduce the computational amount of data monitoring and processing.

[0078] In step 2234, the data processing performance is determined according to the data processing amount and the predetermined third length of time. In some embodiments, the data processing amount can be divided by the predetermined third length of time, and the data processing amount per unit time is taken as the data processing performance, so as to avoid the deviation caused by accidental events and improve the accuracy of the data processing performance statistics. Further, step 231 is performed.

[0079] In step 231, the performance detection information obtained in the previous steps is fed back to the source node of the performance detection request.

[0080] In some embodiments, it can also be determined whether to adjust the resource configuration of the target network entity according to the performance detection result.

[0081] Based on the manner in the above-mentioned embodiments, the signaling load, the data load, the data processing rate and the data collection rate can be taken as the reference quantity for measuring the running burden of the target network entity, and then it can be measured whether the current resource configuration of the target network entity matches the running burden, ensuring the reliability and accuracy of the running burden measurement, thereby improving the rationality of the storage and computing resource configuration of the target network entity, and being beneficial to improving the service capability of the target network entity on the basis of saving resources.

[0082] In some embodiments, the resource configuration of the target network entity can also be adjusted according to the performance detection information.

[0083] In some embodiments, the signaling load is compared with a predetermined signaling load threshold value, and in the case that the signaling load is greater than or equal to the predetermined signaling load threshold value, it is determined to adjust the storage resource configuration of the target network entity. In some embodiments, the adjustment manner can be to determine the storage resource increment of the target network entity according to a predetermined granularity or according to a predetermined correspondence between the signaling load and the storage resource increment, and to increase the storage resource of the target network entity.

[0084] In some embodiments, the data load is compared with a predetermined data load threshold value. In the case that the data load is greater than or equal to the predetermined data load threshold value, it is determined to adjust the storage resource configuration of the target network entity. In some embodiments, the adjustment manner can be to determine the storage resource increment of the target network entity according to a predetermined granularity or according to a predetermined correspondence between the data load and the storage resource increment, and to increase the storage resource of the target network entity.

[0085] In some embodiments, the data processing rate is compared with a predetermined processing rate. In the case that the data processing rate is greater than or equal to the predetermined processing rate threshold value, it is determined to adjust the computing resource configuration of the target network entity. In some embodiments, the adjustment manner can be to determine the computing resource increment of the target network entity according to a predetermined granularity or according to a predetermined correspondence between the data processing rate and the computing resource increment, and to increase the computing resource of the target network entity.

[0086] In some embodiments, the data collection rate is compared with the data processing performance (wherein the data processing performance here refers to the amount of data processed per unit time), and in the case that the data collection rate is greater than the data processing performance and the difference is greater than a predetermined difference threshold value, it is determined to adjust at least one of the storage resource configuration and the computing resource configuration of the target network entity. In some embodiments, the computing resource is increased to improve the data processing performance; in some embodiments, the storage resource is increased to improve the data caching capability and provide sufficient time for data processing.

[0087] Based on the manners in the various embodiments shown above, it can be determined from the performance detection information whether the storage resources and the computing resources need to be adjusted and the adjustment manner is determined from various perspectives, the self-adaptive adjustment capability of the resource configuration is improved, and the service capability of the network entity is ensured.

[0088] A schematic diagram of some embodiments of the performance detection apparatus of the present disclosure is shown in Figure 4 .

[0089] The request obtaining unit 401 can obtain 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 referred to as a consumer node of the performance detection event, such as a network management node of an operator or a node that has the authority to detect the target network entity. In some embodiments, the target network entity is an NWDAF entity. In some embodiments, the request obtaining unit 401 can obtain the performance detection request by at least one of receiving a subscription or an information request.

[0090] The information obtaining unit 402 can obtain performance detection information of the target network entity, and the performance detection information includes at least one of data collection performance or data processing performance of the target network entity. In some embodiments, the data collection performance of the target network entity can be quantified and measured by performing data load or signaling load during data collection, and the data collection performance includes at least one of the data load or the signaling load.

[0091] In some embodiments, the information obtaining unit 402 can perform at least one of the performance detection operations in steps 2211-2213, steps 2221-2223, and steps 2231-2234 of the embodiments shown above. Figure 2

[0092] The information feedback unit 403 feeds back the performance detection information to the source node of the performance detection request. In some embodiments, all types or part of the types of the performance detection information obtained can be fed back to the source node according to the category of the performance detection information in the performance detection request of the source node or the category of the currently detected performance detection information. In some embodiments, at least one of the signaling load, the data load, the data collection rate, or the data processing rate is fed back to the source node of the performance detection request.

[0093] Such an apparatus can timely determine the data collection and processing performance of the target network entity, improve the state monitoring capability for the target network entity, provide a reference for the storage resource and computing resource configuration of the network entity, and thus help to improve the efficiency and rationality of the resource configuration of the network entity.

[0094] In some embodiments, as​Figure 4 As shown, the performance detection apparatus further comprises a configuration adjustment unit 404, which is capable of adjusting the resource configuration of the target network entity according to the performance detection information. In some embodiments, taking the NWDAF node as an example, it is evaluated whether the observed signaling load measurement value is greater than or equal to the preconfigured signaling load threshold value, and based on the evaluation result, it is judged whether to update the storage resource configuration of the NWDAF; it is evaluated whether the observed data processing rate measurement value is less than the preconfigured data processing rate threshold value, and based on the evaluation result, it is judged whether to update the computing resource configuration of the NWDAF; it is evaluated whether the observed data processing rate measurement value is less than the preconfigured data processing rate threshold value, and based on the evaluation result, it is judged whether to update the computing resource configuration of the NWDAF; it is evaluated whether the observed data collection rate is much greater than the data processing rate (for example, the difference between the data collection rate and the data processing rate is greater than a predetermined difference threshold value), and if the data collection rate is much greater than the data processing rate, it is possible that the computing resource or storage resource configuration is unreasonable, and the storage resource or computing resource configuration of the NWDAF needs to be updated based on the evaluation result.

[0095] Such an apparatus can timely adjust the resource configuration of the target network entity based on the performance detection information, thereby improving the rationality of the resource configuration of the target network entity and the adaptability to network state changes, and improving the intelligent degree of the network node.

[0096] An embodiment of the performance detection apparatus of the present disclosure is shown in the structural schematic diagram Figure 5 The performance detection apparatus comprises a memory 501 and a processor 502. 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 performance detection method 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 improve the state monitoring capability for the target network entity, provide a reference for the storage resource and computing resource configuration of the network entity, thereby helping to improve the efficiency and rationality of the resource configuration of the network entity.

[0097] In one embodiment, as shown in Figure 6 The performance detection apparatus 600 comprises a memory 601 and a processor 602. The processor 602 is coupled to the memory 601 through a BUS bus 603. The performance detection apparatus 600 can also be connected to an external storage device 605 through a storage interface 604 to call external data, and can also be connected to a network or another computer system (not shown) through a network interface 606. Details are not described here.

[0098] In the embodiment, the data instruction is stored in the memory, and the processor processes the instruction, so that the state monitoring capability for the target network entity is improved, and reference is provided for the storage resource and the computing resource configuration of the network entity, thereby helping to improve the efficiency and rationality of the resource configuration of the network entity.

[0099] In another embodiment, a computer readable storage medium stores computer program instructions, which are executed by a processor to implement the steps of the method in the corresponding embodiment of the performance detection method. Those skilled in the art should understand that the embodiments of the disclosure can be provided as a method, an apparatus, or a computer program product. Therefore, the disclosure can be in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the disclosure can be in the form of a computer program product implemented on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0100] A schematic diagram of some embodiments of the service system of the disclosure is shown in Figure 7

[0101] The consumer entity 71 of the performance detection event can send a performance detection request to the performance detection apparatus and receive the performance detection information fed back by the performance detection apparatus. 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 that has the authority to detect the target network entity.

[0102] The performance detection apparatus 72 as the provider entity of the performance detection event can be any one of the performance detection apparatuses mentioned above and can execute any one of the performance detection methods mentioned above.

[0103] The service system further includes at least one NWDAF node 731~73n, n being a positive integer, which supports data collection from NFs, AFs and OAM and provides analysis information to NFs, AFs and OAM and can accept detection of the performance detection apparatus.

[0104] In such a service system, the data collection and processing performance of the target network entity can be determined in time, the state monitoring capability for the target network entity is improved, reference is provided for the storage resource and the computing resource configuration of the network entity, and the efficiency and rationality of the resource configuration of the network entity are improved.

[0105] ​The computer program instructions can 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 or flows and / or block diagram block or blocks. Figure 1 one or more flow or flows and / or block diagram block or blocks. Figure 1 one or more flow or flows and / or block diagram block or blocks.

[0106] These computer program instructions can 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 instructions which implement the flowchart or flows and / or block diagram block or blocks. Figure 1 one or more flow or flows and / or block diagram block or blocks. Figure 1 one or more flow or flows and / or block diagram block or blocks.

[0107] These computer program instructions can 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 or flows and / or block diagram block or blocks. Figure 1 one or more flow or flows and / or block diagram block or blocks. Figure 1 Figure 1 one or more flow or flows and / or block diagram block or blocks.

[0108] Thus far, the present disclosure has been described in detail. In order to avoid obscuring the concept of the present disclosure, some details well-known in the art are not described. Those skilled in the art can fully understand how to implement the technical solutions disclosed herein according to the above description.

[0109] The method and apparatus of the present disclosure can be implemented in many ways. For example, the method and apparatus of the present disclosure can be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the method is merely for illustration, and the steps of the method of the present disclosure are not limited to the above specifically described order, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure can also be implemented as programs recorded in recording media, which include machine-readable instructions for implementing the method according to the present disclosure. Thus, the present disclosure also covers the recording media storing the programs for executing the method according to the present disclosure.

[0110] It should be noted that the terms "first", "second" and the like in the description and in the claims of the present disclosure are used to distinguish between similar objects and not necessarily describe a particular chronological or sequential order. It should be understood that the use of such terms is arbitrary and made merely for the sake of ease of description and that the embodiments of the present disclosure described herein are capable of functioning in the absence of an order or sequence as described herein. Furthermore, the terms "comprise" and "include" and variations thereof, are intended to cover a non-exclusive inclusion, such that processes, methods, systems, products, or devices that comprise, include, or are otherwise including a list of steps or units are not necessarily limited to those steps or units explicitly listed, but can include additional steps or units not expressly listed or inherent to such processes, methods, products, or devices.

[0111] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present disclosure, rather than limit the technical solutions of the present disclosure; although the present disclosure has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the specific embodiments of the present disclosure can be modified or equivalent replacements can be made to some technical features; without departing from the spirit of the technical solutions of the present disclosure, all of them should be covered in the technical solution range of the present disclosure.

Claims

1. A performance detection method, comprising: obtaining a performance detection request; obtaining performance detection information of a target network entity, the performance detection information comprising at least one of data collection performance or data processing performance of the target network entity, wherein the data collection performance comprises data load; feeding back the performance detection information to a source node of the performance detection request; wherein the obtaining the performance detection information of the target network entity comprises obtaining data load of the target network entity, comprising: measuring a third number of at least one of response or notification related to data collection received by the target network entity within a second time length; when the target network entity receives at least one of the response or notification related to data collection within the second time length, counting a size of the at least one of the response or notification; and determining the data load according to the third number and the size of the at least one of the response or notification.

2. The method of claim 1, wherein, The data collection performance further comprises at least one of signaling load or data collection rate.

3. The method of claim 1 or 2, wherein, The obtaining the performance detection information of the target network entity comprises obtaining signaling load of the target network entity, comprising: measuring a first number of at least one of data collection request or subscription sent by the target network entity within a first time length; measuring a second number of at least one of data collection response or notification received by the target network entity within the first time length; and obtaining a sum of the first number and the second number as the signaling load.

4. The method of claim 1 or 2, wherein, The obtaining the performance detection information of the target network entity comprises obtaining data processing performance of the target network entity, comprising: determining a fourth number of at least one of response or notification received by the target network entity within a third time length; when the target network entity processes at least one of the response or notification related to data collection within the third time length, counting a size of the at least one of the response or notification; determining a data processing amount according to the fourth number and the size of the at least one of the response or notification; determining the data processing performance according to the data processing amount and the third time length. 5.The method of claim 2, wherein the obtaining the performance detection information of the target network entity comprises: determining the data collection rate according to at least one of the signaling load and a time length during which the signaling load is counted, or the data load and a time length during which the data load is counted. 6.The method of claim 1 or 2, further comprising: adjusting resource configuration of the target network entity according to the performance detection information.

7. The method of claim 6, wherein, The adjusting the resource configuration of the target network entity according to the performance detection information comprises at least one of: determining to adjust storage resource configuration of the target network entity when the signaling load is greater than or equal to a signaling load threshold value; determining to adjust storage resource configuration of the target network entity when the data load is greater than or equal to a data load threshold value. determine to adjust a computing resource configuration of the target network entity in a case that the data processing rate is greater than or equal to a processing rate threshold; or determine to adjust at least one of a storage resource configuration or a computing resource configuration of the target network entity in a case that the data collection rate is greater than the data processing performance and a difference between the data collection rate and the data processing rate is greater than a difference threshold.

8. The method of claim 1, wherein, The feeding back the performance detection information to the source node of the performance detection request comprises: feeding back at least one of a signaling load, a data load, the data collection rate, or the data processing rate to the source node of the performance detection request.

9. The method of claim 1, wherein, The obtaining the performance detection request comprises: obtaining the performance detection request by at least one of information subscription or information request.

10. The method of claim 1, wherein, The feeding back the performance detection information to the source node of the performance detection request comprises: feeding back the performance detection information to the source node of the performance detection request by at least one of notification or response.

11. The method of claim 1 or 2, wherein, The target network entity comprises a network data analytics function (NWDAF) entity.

12. An apparatus for performance detection, comprising: a request obtaining unit configured to obtain a performance detection request; an information obtaining unit configured to obtain performance detection information of a target network entity, the performance detection information comprising at least one of a data collection performance of the target network entity or a data processing performance of the target network entity, wherein the data collection performance comprises a data load, the information obtaining unit being configured to obtain the data load of the target network entity, wherein a first data packet of at least one of a response or a notification related to data collection received by the target network entity in a second length of time is measured as a third number, a size of the first data packet is counted when the target network entity receives the first data packet of at least one of the response or the notification related to data collection in the second length of time, and the data load is determined according to the third number and the size of the first data packet; an information feeding back unit configured to feed back the performance detection information to a source node of the performance detection request.

13. The apparatus of claim 12, further comprising: a configuration adjusting unit configured to adjust a resource configuration of the target network entity according to the performance detection information.

14. An apparatus for performance detection, comprising: a memory; and a processor coupled to the memory, the processor being configured to perform the method of any one of claims 1 to 11 based on instructions stored in the memory.

15. A non-transitory computer readable storage medium having computer program instructions stored thereon, the instructions being executable by a processor to implement the steps of the method of any one of claims 1 to 11.

16. A service system, comprising: a consumer entity configured to send a performance detection request to an apparatus for performance detection and receive performance detection information fed back by the apparatus for performance detection; the apparatus for performance detection configured to perform the method of any one of claims 1 to 11; and and One or more network data analytics function, NWDAF, entities configured to accept detection by the performance detection apparatus.

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