Analysis method using analysis control server, analysis control function server, program, and core system

Abstract

To provide an analysis method using an analysis control server so as to reduce the processing load on a repository function server and to satisfy a desired waiting time for an analysis request from a network function consumer as much as possible.SOLUTION: For each analysis function server, there is an analysis control function server serving as a producer server having an inference time table that stores an inference time corresponding to the length of the analysis request period. The analysis control function server has a first step of receiving an analysis request including an analysis item, an analysis request period, and a desired waiting time from a consumer server, a second step of using the inference time table to search for a first analysis function server whose inference time corresponding to the length of the analysis request period is equal to or shorter than the desired waiting time, and a third step of transferring the analysis request to the first analysis function server.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to a technology for an analysis control server arranged in a core system of a mobile communication network, and in particular to a technology for a Network Data Analytics Function (NWDAF). [Background technology]

[0002] A mobile communication network, common to both 5G (5th Generation) and 4G (4th Generation) standards, is conceptually composed of a user equipment (UE), a radio access network (RAN), and a core network system.

[0003] FIG. 1 is a functional configuration diagram of a core system in the prior art.

[0004] The core system has a group of physical user plane network devices arranged on a virtualization platform of a group of logical control plane network devices. The control plane network devices accommodate a large number of user terminals via the RAN and send and receive control signals such as for establishing communications. The user plane network devices send and receive user data from user terminals via the RAN. Furthermore, in the core system, various NF (Network Function) servers are arranged to control a group of control plane network devices.

[0005] The core system includes an NWDAF (analysis control server) that acquires and analyzes a wide variety of log data from various data sources as communication equipment. The NWDAF receives analysis requests from other NF consumers and returns the analysis results. This is standardized by 3GPP (3rd Generation Partnership Project) (registered trademark) (see, for example, Non-Patent Document 1).

[0006] According to Figure 1, it is composed of the following NF servers: Application Function Server (NF Consumer) 10 Repository Function Server (NRF) 11 Analysis Function Server (NWDAF) 121, 122, 123 The NF consumer 10 is an NF that is the source of an analysis request. In response to a discovery request from an NF consumer, the NRF 11 selects one NWDAF from among multiple NWDAFs 12 and responds. NWDAF12 has a learning (AI) model corresponding to the analysis item, performs inference processing in response to an analysis request, and returns the analysis results. The control plane network devices and the user plane network devices serve as data resources, and serve as input data for the NWDAF to execute inference processing using a learning model.

[0007] FIG. 2 is a sequence diagram of analysis by an analysis function server in the prior art.

[0008] The sequence in FIG. 2 is based on Non-Patent Document 1. (S01) Assume that the NF consumer 10 wishes to obtain an analysis result for analysis item α. At this time, the NF consumer 10 queries the NRF 11 for an NWDAF that can analyze analysis item α. The NF consumer 10 sends a discover request to the NRF 11. The NF consumer 10 sets, in the discovery request, a Preferred Supported Analytics Delay, which is a parameter for the time required to obtain the analysis result for the analysis item from the NWDAF 12 (see, for example, Non-Patent Document 2).

[0009] (S02) The NRF 11 manages the Supported Analytics Delay (input data collection delay + inference delay) for each analysis item for each NWDAF 12. Then, when the NRF 11 receives a discovery request from the NF consumer 10, it selects an NWDAF that can respond with the analysis result within the Preferred Supported Analytics Delay. For example, if the NRF 11 selects the NWDAF 121 as being able to respond, it returns the address of the NWDAF 121 to the NF consumer 10.

[0010] (S03) The NF consumer 10 sends an analysis request to the address of the NWDAF returned from the NRF 11. The analysis request includes the following information: Analysis item (Analytics ID): Specify the analysis process Analytics target period: The period for which the analysis results are requested Expected waiting time: The time to wait for a response from the analysis process

[0011] According to FIG. 2, it is assumed that the NF consumer 10 has transmitted an analysis request for analysis item α to the NWDAF 121. When the NWDAF 121 receives an analysis request and is unable to return the analysis results within the desired waiting time, it returns an error response to the NF consumer 10. At this time, the NWDAF 121 may set a revised waiting time for the analysis process in the error response. This reduces the possibility that an error response will be returned again when the NF consumer 10 sends the analysis request again.

[0012] (S04) When the NWDAF 121 receives an analysis request from the NF consumer 10, it must possess a learning model for analyzing the analysis item α. If the NWDAF 121 does not possess a learning model for the analysis item α, in the case of Figure 2, it sends a model acquisition request to the NWDAF 123 that possesses the learning model C for the analysis item α. In response to this, the NWDAF 123 transmits the learning model C to the NWDAF 121. The time related to S24 will be delayed as the "model acquisition time" in the NWDAF 121.

[0013] (S05) Next, the NWDAF 121 transmits a network data request to the data source to acquire input data for analysis on the analysis item α. In response, the data source transmits the network data to the NWDAF 121 . The time related to S25 will be delayed as "data collection time" in the NWDAF 121.

[0014] (S06) Finally, the NWDAF 121 inputs the network data into the learning model, executes inference processing, and obtains the analysis results. The time related to S26 is delayed as "inference time" in the NWDAF 121.

[0015] (S07) Then, the NWDAF 121 returns the analysis result to the NF consumer 10. [Prior art documents] [Non-patent literature]

[0016] [Non-Patent Document 1] 3GPP TS 23.288 “Architecture enhancements for 5G System (5GS) to support network data analytics services”, [online], [searched on March 7, 2020], Internet <URL:https: / / portal.3gpp.org / desktopmodules / Specifications / SpecificationDetails.aspx?specificationId=3579> [Non-patent document 2] 3GPP TS 23.501 “System architecture for the 5G System (5GS)”, [online], [searched on March 7, 2020], Internet <URL:https: / / portal.3gpp.org / desktopmodules / Specifications / SpecificationDetails.aspx?specificationId=3144> Summary of the Invention [Problem to be solved by the invention]

[0017] According to the conventional technology described above, the more diverse the NWDAFs, the greater the processing load on the NRF for discovering those NWDAFs. The NRF needs to frequently update the Supported Analytics Delay for each NWDAF. Furthermore, the NRF needs to instantly select and respond to discovery requests received from different NF consumers at approximately the same time, which requires a reduction in the processing load. Furthermore, the NRF needs to monitor the processing load of each NWDAF to prevent inference processing from concentrating on a single NWDAF.

[0018] However, the NRF processing delay is a processing load imposed by responding in real time to discovery requests received from multiple NF consumers.Furthermore, frequent information exchange with multiple NWDAFs imposes a network load.These may also cause processing delays for other NF servers.

[0019] In addition, as the processing delay of NWDAFs is the model acquisition time required to acquire learning models from other NWDAFs, this is not included in the Supported Analytics Delay, which may result in processing delays greater than expected.

[0020] Therefore, the present invention aims to provide an analysis method, an analysis control function server, a program, and a core system using an analysis control server, so as to reduce the processing load on the repository function server and meet the desired waiting time for analysis requests from network function consumers as much as possible. [Means for solving the problem]

[0021] According to the present invention, there is provided an analysis method for a core system having a plurality of analysis function servers that analyzes predetermined analysis items using a learning model from network data, the method comprising: an analysis control function server as a producer server having an inference time table that stores inference times corresponding to the length of an analysis request period for each analysis function server; A first step in which an analysis control function server receives an analysis request including an analysis item, an analysis request period, and a desired waiting time from a consumer server; a second step in which the analysis control function server uses an inference time table to search for a first analysis function server whose inference time according to the length of the analysis request period is equal to or shorter than the desired waiting time; a third step in which the analysis control function server transfers the analysis request to the first analysis function server; a fourth step in which the first analysis function server executes inference processing for the analysis item using a learning model from the network data and transmits the analysis results to the consumer server; The present invention is characterized by having the following.

[0022] According to another embodiment of the method for analyzing a core system of the present invention, The inference time in the inference time table is a statistical estimate based on the inference time corresponding to the length of the past analysis request period. It is also preferable.

[0023] According to another embodiment of the method for analyzing a core system of the present invention, the analysis control function server further includes a collection time table that stores, for each analysis function server, a data collection time for acquiring network data from a data source according to the length of the analysis request period; In the second step, the analysis control function server searches for a first analysis function server whose sum of the inference time and the data collection time according to the length of the analysis request period using the collection time table is equal to or less than the desired waiting time, In the third step, the analysis control function server sends a network data request to the data source, requesting that the data source send network data for an analysis request period to the first analysis function server; In the fourth step, the first analytical function server receives network data from the data source. It is also preferable.

[0024] According to another embodiment of the method for analyzing a core system of the present invention, The data collection time in the collection time table is statistically estimated from the data collection time corresponding to the time length of the past analysis request period. It is also preferable.

[0025] According to another embodiment of the method for analyzing a core system of the present invention, The analysis control function server further includes a model acquisition table that stores, for each analysis function server, a model acquisition time required to acquire the learning model held by the analysis control function server, and In the second step, if the analysis control function server determines using the model acquisition table that the first analysis function server does not hold a learning model based on the analysis item, it searches for a second analysis function server among multiple other analysis function servers that hold the learning model, for which the sum of the inference time, data collection time, and model acquisition time required to acquire the learning model is less than or equal to the desired waiting time, and sends a model acquisition request to the second analysis function server, requesting that the learning model be sent to the first analysis function server. It is also preferable to perform the process as follows.

[0026] According to another embodiment of the method for analyzing a core system of the present invention, The model acquisition time in the model acquisition table is a statistical estimate based on the model acquisition time for past training models. It is also preferable.

[0027] According to another embodiment of the method for analyzing a core system of the present invention, Regarding the second step, The analysis control function server sends a network data request to the data source requesting that network data for an analysis request period be sent to a second analysis function server; The analysis control function server sends a learning request to the second analysis function server to update its own learning model; The second analysis function server receives the network data for the analysis request period from the data source and trains to update its own learning model. It is also preferable to perform the process as follows.

[0028] According to another embodiment of the method for analyzing a core system of the present invention, The analysis control function server acquires the current computational resource utilization rate for each analysis function server, In the second step, the analysis control function server selects a plurality of analysis function servers that satisfy predetermined conditions in descending order of current computational resource utilization rate as search candidates for the first analysis function server. It is also preferable to perform the process as follows.

[0029] According to another embodiment of the method for analyzing a core system of the present invention, In the second step, the analysis function server with the highest computational resource utilization rate is selected as the search candidate for the first analysis function server. It is also preferable to perform the process as follows.

[0030] According to another embodiment of the method for analyzing a core system of the present invention, The analysis control function server schedules and manages the inference time slots for the analysis items to be executed for each analysis function server. In the second step, the analysis control function server divides and schedules the time slots for inference to be performed by each analysis function server so that the waiting time for each analysis item is less than the desired waiting time. It is also preferable to perform the process as follows.

[0031] According to another embodiment of the method for analyzing a core system of the present invention, The analysis function server is NWDAF (Network Data Analytics Function), The learning model of the analysis function server is trained by MTLF (Model Training Logical Function), The inference process of the analytical function server is performed by AnLF (Analytics Logical Function), The consumer server queries the NRF (Network Repository Function) for the address of the analysis control function server that can control the analysis item in question. It is also preferable to perform the process as follows.

[0032] According to the present invention, there is provided an analysis control function server that communicates with a plurality of analysis function servers that analyze predetermined analysis items using a learning model from network data, and functions as a producer server in a core system, an inference time table that stores, for each analysis function server, an inference time corresponding to the length of the analysis request period; analysis request receiving means for receiving an analysis request including an analysis item, an analysis request period, and a desired waiting time from a consumer server; an analysis function server search means for searching for a first analysis function server whose inference time corresponding to the length of the analysis request period is equal to or shorter than the desired waiting time, using an inference time table; an analysis request transfer means for transferring the analysis request to the searched analysis function server; and The first analysis function server executes inference processing for the analysis item using a learning model from the network data, and transmits the analysis results to the consumer server. It is characterized by:

[0033] According to the present invention, a program that communicates with a plurality of analysis function servers that analyze predetermined analysis items using a learning model from network data and causes a computer to function as a producer server in a core system, an inference time table that stores, for each analysis function server, an inference time corresponding to the length of the analysis request period; analysis request receiving means for receiving an analysis request including an analysis item, an analysis request period, and a desired waiting time from a consumer server; a first analysis function server search means for searching for a first analysis function server whose inference time corresponding to the length of the analysis request period is equal to or shorter than a desired waiting time, using an inference time table; an analysis request transfer means for transferring the analysis request to the first analysis function server; to make the computer function, The first analysis function server executes inference processing for the analysis item using a learning model from the network data, and transmits the analysis results to the consumer server. It is characterized by:

[0034] According to the present invention, there is provided a core system having a plurality of analysis function servers that analyzes predetermined analysis items using a learning model from network data, an analysis control function server as a producer server having an inference time table that stores inference times corresponding to the length of an analysis request period for each analysis function server; The analysis control function server an inference time table that stores, for each analysis function server, an inference time corresponding to the length of the analysis request period; analysis request receiving means for receiving an analysis request including an analysis item, an analysis request period, and a desired waiting time from a consumer server; a first analysis function server search means for searching for a first analysis function server whose inference time corresponding to the length of the analysis request period is equal to or shorter than a desired waiting time using an inference time table; an analysis request transfer means for transferring the analysis request to the first analysis function server; and The first analysis function server executes inference processing for the analysis item using a learning model from the network data, and transmits the analysis results to the consumer server. It is characterized by: [Effects of the Invention]

[0035] According to the analysis method, analysis control function server, program, and core system using the analysis control server of the present invention, the processing load on the repository function server can be reduced, and the desired waiting time for analysis requests from network function consumers can be met as much as possible. [Brief explanation of the drawings]

[0036] [Figure 1] FIG. 1 is a diagram illustrating a system configuration according to a conventional technique. [Figure 2] FIG. 1 is a sequence diagram according to the prior art. [Figure 3] 1 is a system configuration diagram according to the present invention. [Figure 4] 3 is an explanatory diagram of a table held by the analysis control function server of the present invention. FIG. [Figure 5] FIG. 2 is a sequence diagram according to the present invention. [Figure 6] FIG. 2 is a functional configuration diagram of an analysis control function server according to the present invention. [Figure 7] FIG. 10 is an explanatory diagram of the scheduling of each analysis control server in the analysis control function server of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0037] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0038] FIG. 3 is a diagram showing the system configuration according to the present invention.

[0039] The core system of Fig. 3 has multiple NWDAFs (analysis function servers) 12 that analyze predetermined analysis items using learning models from network data, similar to Fig. 1. The NWDAFs 12 are existing systems and have the following two functions: AnLF(Analytics Logical Function) MTLF (Model Training Logical Function) The learning model of NWDAF12 is trained by MTLF, which retrains the learning model using log data obtained from the data source and makes the learning model available to AnLF. The inference process of the NWDAF 12 is executed by the AnLF, which receives analysis requests and returns the analysis results.

[0040] 1, the core system of FIG. 3 further includes an analysis control function server (NWDAF controller) 13.

[0041] [Analysis and Control Function Server (NWDAF Controller) 13] The NWDAF controller 13 functions as a producer server for the NF consumer 10.

[0042] FIG. 4 is an explanatory diagram of a table held by the analysis control function server of the present invention.

[0043] According to FIG. 4, the NWDAF controller 13 has the following tables: <Inference time table> <Collection time table> <Model Acquisition Table> It should be noted that although the inference time table is required, the collection time table and the model acquisition table may be optional depending on the embodiment.

[0044] <Inference time table> The inference time table stores inference times corresponding to the length of the analysis request period for each NWDAF 12. As shown in Fig. 4, this may be represented by a graph (approximate line or approximate curve) with the length of the analysis request period on the horizontal axis and the inference time on the vertical axis. The inference times in the inference time table are statistically estimated from inference times corresponding to the length of past analysis request periods. Specifically, they may be trained by a supervised machine learning engine using training data in which the length of past analysis request periods is used as an explanatory variable and the past inference time at that time is used as a target variable. The inference time can be predicted by inputting the length of the analysis request period at the time of estimation.

[0045] <Collection time table> The collection time table stores data collection times for acquiring network data from data sources according to the length of the analysis request period. As shown in Fig. 4, the collection time table may be represented by a graph (approximate line or approximate curve) with the length of the analysis request period on the horizontal axis and the data collection time on the vertical axis. The data collection time in the collection time table is statistically estimated from the data collection time corresponding to the length of the past analysis request period. Specifically, it may be trained by a supervised machine learning engine using training data in which the length of the past analysis request period is used as the explanatory variable and the past data collection time at that time is used as the objective variable. The data collection time can be predicted by entering the length of the analysis request period at the time of estimation.

[0046] <Model Acquisition Table> The model acquisition table stores, for each NWDAF, the model acquisition time required to acquire the learning model held by that NWDAF. As shown in Figure 4, the model acquisition table records, for each NWDAF, the "model ID" held by that NWDAF, the "model acquisition time" required to acquire that model, and the "update date and time" when that model was last trained using network data. The update date and time is recorded to determine the need for updating and training the learning model in relation to the analysis request period. The model acquisition time in the model acquisition table is a statistical estimate based on the model acquisition times corresponding to past learning models. Specifically, it may be the average or median of the past model acquisition times.

[0047] FIG. 5 is a sequence diagram according to the present invention.

[0048] (S0) The NRF 11 discovers the various NF servers required by the NF consumer 10, as in the prior art. First, the NF consumer 10 sends a discovery request to discover the NWDAF 12 based on the analysis item α to the NRF 11. In response, the NRF 11 returns the address (URL (Uniform Resource Locator)) of the NWDAF controller 13 to the NF consumer 10. That is, the NF consumer 10 inquires of the NRF 11 about the address of the NWDAF controller 13 that can control the analysis item α. Of course, if the NF consumer 10 already knows the address of the NWDAF controller 13 to which the analysis request is to be sent, there is no need to make an inquiry to the NRF 11.

[0049] Alternatively, multiple analytical function control servers 13 may be provided for each type of analysis item. In such a case, the NRF 11 selects an analytical function control server 13 according to the analysis item in the discovery request received from the NF consumer 10, and returns the address of the selected analytical function control server 13 to the NF consumer 10.

[0050] According to the present invention, the NRF 11 does not need to discover the NWDAF 12 according to the analysis items of the analysis request, and does not need to manage the Supported Analytics Delay for each NWDAF 12. The NRF 11 can reduce the processing load in managing the NWDAF 12 and can allocate that processing load to discovering various other NF servers.

[0051] (S1) The NF consumer 10 sends an “analysis request” including the following information to the NWDAF controller 13. This is the same as the prior art described above. ·Analysis items ·Analysis request period Desired waiting time

[0052] (S21) The NWDAF controller 13 manages the current computational resource utilization rate for each NWDAF 12. The NWDAF controller 13 may periodically receive computational resource utilization rates from multiple NWDAFs 12 under its management. The NWDAF controller 13 selects, as search candidates, multiple NWDAFs 12 that satisfy a predetermined condition in descending order of current computational resource utilization rate (lowest computational resource efficiency). Here, the "predetermined condition" refers to a predetermined rate or less. In other words, if there are only NWDAFs 12 with computational resource utilization rates higher than the predetermined rate, no search candidates are selected. On the other hand, the NWDAF with the highest computational resource utilization rate and a rate less than the predetermined rate may be selected as a search candidate.

[0053] (S22) Next, the NWDAF controller 13 selects, from among the NWDAFs 12 that are search candidates, an NWDAF 12 that satisfies the expected waiting time of the analysis request using the inference time table, collection time table, and model acquisition table described above in Figure 4.

[0054] The NWDAF controller 13 uses the inference time table to search for an NWDAF 12 whose inference time according to the length of the analysis request period (Analytics target period) is equal to or shorter than the desired waiting time. Inference time ≦ desired waiting time If the NWDAF 12 cannot be found, it returns an error response to the NF consumer 10. At this time, the NWDAF controller 13 sets the inference time as the revised waiting time in the error response. This allows the NF consumer 10 to send the analysis request again, taking the revised waiting time into consideration.

[0055] (S23) Next, the NWDAF controller 13 selects NWDAFs 12 as search candidates for which the sum of the inference time and the data collection time according to the length of the analysis request period (Analytics target period) using the collection time table is less than the desired waiting time. Inference time + data collection time ≦ desired latency If the learning model held by NWDAF12 has not been trained with network data based on the requested analysis period, the network data must be obtained from the data source.

[0056] (S24) Next, the NWDAF controller 13 uses the model acquisition table to determine whether the NWDAF 12 that is a search candidate holds a learning model (learning model) based on the analysis item. For example, according to the above-mentioned FIG. 4, it is assumed that the NWDAF 121 in the model acquisition table does not hold a learning model C that can analyze the analysis item α. If it is determined that the learning model C is not held in this manner, the NWDAF controller 13 selects an NWDAF from among multiple other NWDAFs 12 that hold the learning model C, whose sum of the inference time, data collection time, and model acquisition time required to acquire the learning model is less than the desired waiting time as a search candidate. Inference time + data collection time + model acquisition time ≦ desired latency Here, it is assumed that the NWDAF 123 (second analysis function server) holds the learning model C. If the NWDAF 121 already holds the learning model C, the model acquisition time is zero. If the learning model held by NWDAF12 has not been trained with network data based on the requested analysis period, that network data must be obtained.

[0057] If there are multiple NWDAFs 12 found through S22 to S24, the NWDAF 12 with the lowest computational resource usage rate (worst computational resource efficiency) in S21 is selected. On the other hand, if there is no NWDAF 12 found by S22 to S24, the processing time (= inference time + data collection time + model acquisition time) required by the NWDAF 12 with the lowest computational resource usage rate in S21 is set as the revised waiting time in the error response.The NWDAF controller 13 then returns the error response to the NF consumer 10.

[0058] (S25) The NWDAF controller 13 schedules and manages the time slots of inference times for analysis items to be executed for each NWDAF 12. That is, it schedules the processing timings of multiple analysis requests in the same NWDAF 12. The NWDAF controller 13 may divide and schedule the time slots of the inference time to be executed by each NWDAF 12 so that the waiting time for each analysis item is equal to or less than the desired waiting time. Specific scheduling will be described later with reference to FIG. 7.

[0059] (S26) The NWDAF controller 13 transmits a "model acquisition request" to the NWDAF 123 (second analysis function server) requesting that the learning model C be transmitted to the NWDAF 121 (first analysis function server). In response, the NWDAF 123 transmits the learning model C that it holds to the NWDAF 121.

[0060] (S27) The NWDAF controller 13 also transmits a "network data request" to the data source, requesting that network data for the analysis request period be transmitted to the NWDAF 121 (first analysis function server). In response, the data source transmits network data corresponding to the analysis request period to the NWDAF 121. At this time, the NWDAF controller 13 may transmit a network data request to the data source requesting that network data for the analysis request period be transmitted to the NWDAF 123 (second analysis function server). In response, the data source transmits network data corresponding to the analysis request period to the NWDAF 123.

[0061] (S3) The NWDAF controller 13 transfers the "analysis request" to the NWDAF 121 (first analysis function server). Note that when the NWDAF controller 13 sends multiple analysis requests to the same NWDAF 12, the timing of the transmission is the time scheduled in S25. The NWDAF 121 inputs network data into a learning model to perform inference processing, and the learning model may be retrained using the network data.

[0062] Regarding S26, S27, and S3, the NWDAF controller 13 may transmit the following three request messages simultaneously in parallel. Requesting model acquisition from other NWDAF12 Network data requests to the data source Analysis request for NWDAF12

[0063] (S4) The NWDAF 121 (first analysis function server) that has received the analysis request executes inference processing for the analysis item using a learning model from the network data, and transmits the analysis results to the consumer. It is also preferable that the NWDAF 121 transmits an analysis completion notification to the NWDAF controller 13. The analysis completion notification includes processing times, which are the inference time, the data acquisition time, and the model acquisition time. In response to this, the NWDAF controller 13 updates each table.

[0064] (S5) The NWDAF controller 13 may also transmit a "learning request" to the NWDAF 123 (second analysis function server) to update its own learning model. In response to this, the NWDAF 123 receives network data for the analysis request period from the data source and trains to update its own learning model. The NWDAF 123 then sends a training completion notification to the NWDAF controller 13. The analysis completion notification includes the inference time for training as processing time. This causes the NWDAF controller 13 to update the inference time table.

[0065] FIG. 6 is a functional configuration diagram of the analysis control function server in the present invention.

[0066] 6, the NWDAF controller 13 includes an analysis request receiving unit 131, a computational resource usage rate determining unit 132, an analysis function server searching unit 133, a scheduling unit 134, a model acquisition request unit 135, a network data request unit 136, and an analysis request forwarding unit 137. These functional components are realized by executing a program that causes a computer installed in the NWDAF controller to function. The processing flow of these functional components can also be understood as an analysis control method.

[0067] The analysis request receiving unit 131 functions in the same manner as S1 described above. The computational resource usage rate determination unit 132 functions in the same manner as in S21 described above. The analytical function server search unit 133 functions in the same manner as in S22 to S24 described above. The scheduling unit 134 functions in the same manner as in S25 described above. The model acquisition request unit 135 functions in the same manner as in S26 described above. The network data request unit 136 functions in the same manner as the above-mentioned S3.

[0068] FIG. 7 is an explanatory diagram of the scheduling of each analysis control server in the analysis control function server of the present invention.

[0069] (0 seconds) Assume that NWDAF12 receives the following two analysis requests. Analysis request 1: Analysis item 1 Analysis request period Desired waiting time: 50 seconds Processing time: 20 seconds Analysis request 2: Analysis item 2 Analysis request period Desired waiting time: 70 seconds Processing time: 30 seconds Note that "processing time" refers to the inference time + data collection time + model acquisition time. At time 0 seconds, the analysis process for analysis item 1 is scheduled to start first, and then the analysis process for analysis item 2 is scheduled to start.

[0070] According to the 3GPP standard, the NWDAF processes analysis requests received from NF consumers in a first-in, first-out (FIFO) order. Therefore, if the NWDAF 12 receives analysis requests for analysis items 1 and 2 at approximately the same time, the NWDAF 12 follows the FIFO order.

[0071] (20 seconds) Analysis processing for analysis item 1 is completed. Then, analysis processing for analysis item 2 begins.

[0072] (30 seconds) Assume that NWDAF12 receives the following further analysis request: Analysis request 3: Analysis item 3 Analysis request period Desired waiting time: 30 seconds Processing time: 20 seconds In this case, if analysis processing for analysis item 3 is started after analysis processing for analysis item 2 (at 50 seconds), the analysis processing for analysis item 3 will end at 80 seconds, and the desired waiting time of 30 seconds for analysis item 3 will not be met. Therefore, analysis item 2 is scheduled to be processed in parts to satisfy the desired waiting time of 30 seconds for analysis item 3. Specifically, the analysis process for analysis item 2 is scheduled to be interrupted once at the 40-second mark, and the analysis process for analysis item 3 is scheduled to start.

[0073] (40 seconds) The analysis process for analysis item 2 is temporarily suspended. Then, the analysis process for analysis item 3 is started.

[0074] (60 seconds) Analysis processing ends for analysis item 3. Then, analysis processing resumes for analysis item 2.

[0075] (70 seconds) Analysis processing for analysis item 2 is completed.

[0076] By dividing and scheduling the analysis process as shown in Figure 7, it is possible to satisfy the expected waiting time for as many analysis requests as possible. Of course, the scheduling can also be performed according to a predetermined policy. The policy may be operated by the mobile core operator.

[0077] Furthermore, by having the NWDAF controller 13 perform scheduling for each NWDAF 12, there is no need to update the Supported Analytics Delay between the NRF 11 and the NWDAF 12, which further reduces the processing load on the NRF 11 and avoids network congestion.

[0078] As explained in detail above, the analysis method, analysis control function server, program, and core system using the analysis control server of the present invention can reduce the processing load on the repository function server and meet the desired waiting time for analysis requests from network function consumers as much as possible.

[0079] Furthermore, this will, for example, "reduce the processing load on core systems of mobile communication networks," which will contribute to Goal 9 of the United Nations-led Sustainable Development Goals (SDGs), which is to "build resilient infrastructure, promote sustainable industrialization and foster innovation."

[0080] With respect to the various embodiments of the present invention described above, various changes, modifications, and omissions that fall within the scope of the technical spirit and aspects of the present invention may be easily made by those skilled in the art. The above description is merely illustrative and is not intended to be limiting in any way. The present invention is limited only by the claims and their equivalents. [Explanation of symbols]

[0081] 10 NF Consumer, Application Function Server 11 NRF, Repository Function Server 12 NWDAF, Analysis Function Server 13 NWDAF Controller, Analysis and Control Function Server 131 Analysis request receiving unit 132 Computational resource usage rate determination section 133 Analysis Function Server Search Unit 134 Scheduling Department 135 Model acquisition request section 136 Network Data Request Section 137 Analysis Request Transfer Unit

Claims

1. An analysis method for a core system having a plurality of analysis function servers that analyzes predetermined analysis items using a learning model from network data, comprising: an analysis control function server as a producer server having an inference time table that stores inference times corresponding to the length of an analysis request period for each analysis function server; A first step in which an analysis control function server receives an analysis request including an analysis item, an analysis request period, and a desired waiting time from a consumer server; a second step in which the analysis control function server uses an inference time table to search for a first analysis function server whose inference time corresponding to the length of the analysis request period is equal to or shorter than the desired waiting time; a third step in which the analysis control function server transfers the analysis request to the first analysis function server; a fourth step in which the first analysis function server executes inference processing for the analysis item using a learning model from the network data and transmits the analysis results to the consumer server; A method for analyzing a core system, comprising:

2. The inference time in the inference time table is a statistical estimate based on the inference time corresponding to the length of the past analysis request period.

2. The method of claim 1, wherein the core system is analyzed.

3. the analysis control function server further includes a collection time table that stores, for each analysis function server, a data collection time for acquiring network data from a data source according to the length of the analysis request period; In the second step, the analysis control function server selects as a search candidate a first analysis function server whose sum of the inference time and the data collection time according to the length of the analysis request period using the collection time table is equal to or less than the desired waiting time, In the third step, the analysis control function server sends a network data request to the data source, requesting that the data source send network data for an analysis request period to the first analysis function server; In a fourth step, a first analytical function server receives network data from a data source.

2. The method of claim 1, wherein the core system is analyzed.

4. The data collection time in the collection time table is statistically estimated from the data collection time corresponding to the time length of the past analysis request period.

4. The method for analyzing a core system according to claim 3.

5. The analysis control function server further includes a model acquisition table that stores, for each analysis function server, a model acquisition time required to acquire the learning model held by the analysis control function server, and In the second step, if the analysis control function server determines using the model acquisition table that the first analysis function server does not hold a learning model based on the analysis item, it searches among multiple other analysis function servers that hold the learning model for a second analysis function server whose sum of the inference time, data collection time, and model acquisition time required to acquire the learning model is less than or equal to the desired waiting time, and sends a model acquisition request to the second analysis function server requesting that the learning model be sent to the first analysis function server.

2. The method for analyzing a core system according to claim 1, wherein the method is executed as follows:

6. The model acquisition time in the model acquisition table is a statistical estimate based on the model acquisition time for past training models.

6. The method for analyzing a core system according to claim 5.

7. Regarding the second step, The analysis control function server sends a network data request to the data source requesting that network data for an analysis request period be sent to the second analysis function server; The analysis control function server sends a learning request to the second analysis function server to update its own learning model; The second analysis function server receives network data for the analysis request period from the data source and trains its own learning model to update it.

6. The method for analyzing a core system according to claim 5, wherein the method is executed as follows:

8. The analysis control function server acquires the current computational resource utilization rate for each analysis function server, In the second step, the analysis control function server selects a plurality of analysis function servers that satisfy a predetermined condition in descending order of current computational resource utilization rate as search candidates for the first analysis function server.

8. The method for analyzing a core system according to claim 1, wherein the method is performed as follows:

9. In the second step, the analysis function server with the highest computational resource utilization rate is selected as the search candidate for the first analysis function server.

9. The method for analyzing a core system according to claim 8, wherein the method is performed as follows:

10. The analysis control function server schedules and manages the inference time slots for the analysis items to be executed for each analysis function server. In the second step, the analysis control function server divides the time slots of the inference time that each analysis function server should execute and schedules it so that the waiting time for each analysis item is equal to or less than the desired waiting time.

8. The method for analyzing a core system according to claim 1, wherein the method is performed as follows:

11. The analysis function server is NWDAF (Network Data Analytics Function), The learning model of the analysis function server is trained by MTLF (Model Training Logical Function), The inference process of the analytical function server is executed by AnLF (Analytics Logical Function), The consumer server queries the NRF (Network Repository Function) for the address of the analysis control function server that can control the analysis item.

4. The method for analyzing a core system according to claim 1, wherein the method is executed as follows:

12. An analysis control function server that communicates with a plurality of analysis function servers that analyze predetermined analysis items using a learning model from network data and functions as a producer server in a core system, an inference time table that stores, for each analysis function server, an inference time corresponding to the length of the analysis request period; analysis request receiving means for receiving an analysis request including an analysis item, an analysis request period, and a desired waiting time from a consumer server; an analysis function server search means for searching for a first analysis function server whose inference time corresponding to the length of the analysis request period is equal to or shorter than the desired waiting time, using an inference time table; an analysis request transfer means for transferring the analysis request to the searched analysis function server; and The first analysis function server executes inference processing for the analysis item using a learning model from the network data, and transmits the analysis results to the consumer server. An analysis control function server characterized by:

13. A program that communicates with a plurality of analysis function servers that analyze network data using a learning model for a predetermined analysis item, and causes a computer to function as a producer server in a core system, an inference time table that stores, for each analysis function server, an inference time corresponding to the length of the analysis request period; analysis request receiving means for receiving an analysis request including an analysis item, an analysis request period, and a desired waiting time from a consumer server; a first analysis function server search means for searching for a first analysis function server whose inference time corresponding to the length of the analysis request period is equal to or shorter than a desired waiting time, using an inference time table; an analysis request transfer means for transferring the analysis request to the first analysis function server; to make the computer function, The first analysis function server executes inference processing for the analysis item using a learning model from the network data, and transmits the analysis results to the consumer server. A program characterized by:

14. A core system having a plurality of analysis function servers that analyzes predetermined analysis items using a learning model from network data, an analysis control function server as a producer server having an inference time table that stores inference times corresponding to the length of an analysis request period for each analysis function server; The analysis control function server an inference time table that stores, for each analysis function server, an inference time corresponding to the length of the analysis request period; analysis request receiving means for receiving an analysis request including an analysis item, an analysis request period, and a desired waiting time from a consumer server; a first analysis function server search means for searching for a first analysis function server whose inference time corresponding to the length of the analysis request period is equal to or shorter than a desired waiting time using an inference time table; an analysis request transfer means for transferring the analysis request to the first analysis function server; and The first analysis function server executes inference processing for the analysis item using a learning model from the network data, and transmits the analysis results to the consumer server. A core system characterized by:

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