CN111836291A - Slice resource scheduling method and network element - Google Patents

Abstract

The invention provides a slice resource scheduling method and a network element. The method includes: acquiring slice analysis data; analyzing and updating the analysis data to obtain a data analysis result; sending the data analysis result to a network slice selection function NSSF or policy control Functional PCF. NSSF obtains slice data analysis results from the network analysis logic function NWDAF; and adjusts slice-level KPIs and network performance according to the data analysis results. PCF obtains the data analysis results of slices from the network analysis logic function NWDAF; and adjusts the policy configuration for different slices according to the data analysis results. The NWDAF in the embodiment of the present invention can obtain the data analysis result according to the analysis data of the current network, distribute the data analysis result to the NSSF or PCF, and the NSSF or PCF can dynamically adjust the slice resources, improve the resource utilization rate, and meet the needs of more slice users. SLA requirements.

Description

Slice resource scheduling method and network element

technical field

The present invention relates to the field of communication technologies, and in particular, to a method for scheduling slice resources and a network element.

Background technique

The 5th Generation (5G) mobile communication system supports a network architecture including the following network nodes (or network elements): Network Data Analytics Function ( ^NWDAF ), Access and Mobility Management Function (AccessManagement) Function, AMF), Policy Control Function (Policy Control Function, PCF), Network Slice Selection Function (NSSF), Radio Access Network (Radio Access Network, RAN) and Network Functions (Network Functions, NFs), etc. The NWDAF sends the specific network information status analysis of the subscribed user to other relevant network nodes according to the slice granularity, that is, sends the network information status analysis divided by slice with the subscription information to other network elements. In addition, NWDAF can also send specific network data analysis to other network elements by slice. In addition, other network elements can also directly collect the required network status analysis information from the NWDAF. Among them, the PCF can use the data obtained from the NWDAF in policy decisions, and the NSSF can select slices according to the load level information (load level information) provided by the NWDAF.

When there are multiple slices in the network, slice users are concerned about the service-level agreement (SLA) guarantee of slices. Currently, the following methods are often used to ensure users' SLA, such as sharing RAN and using RAN resource scheduling to ensure slice SLA , such as the transmission time interval

It is not mandatory to allocate part of the frequency to any slice within the Transmission Timing Interval (TTI); share the transmission network, use the Flex-E interface and tunneling technology to reduce delay and improve isolation and reliability; use dual connections with dedicated or shared (Dual Connectivity, DC) resources dedicated core network (Core Network, CN), the user plane function (User Plane Function, UPF) is deployed to the edge to reduce latency. However, the above methods are all implemented based on network resources and network capabilities. The network resources and capabilities are limited and cannot meet the SLA requirements of more slicing users.

SUMMARY OF THE INVENTION

The present invention provides a slice resource scheduling method and a network element, and solves the problem that the prior art cannot meet the SLA requirements of slice users.

The embodiment of the present invention provides a slice resource scheduling method, which is applied to the network analysis logic function NWDAF, including:

Obtain the analysis data of the slice;

Analyze and update the analysis data to obtain data analysis results;

The data analysis result is sent to the network slice selection function NSSF or the policy control function PCF.

An embodiment of the present invention also provides a network element of network analysis logic function NWDAF, including: a processor; a memory connected to the processor, and a transceiver connected to the processor; wherein,

The transceiver is used to: obtain the analysis data of the slice;

The processor is used to: analyze and update the analysis data to obtain the data analysis result;

The transceiver is also used for: sending the data analysis result to the network slice selection function NSSF or the policy control function PCF.

The embodiment of the present invention also provides a network element, the network element is a network analysis logic function NWDAF, including:

The acquisition module is used to acquire the analysis data of the slice;

The analysis module is used to analyze and update the analysis data to obtain data analysis results;

The first sending module is configured to send the data analysis result to the network slice selection function NSSF or the policy control function PCF.

Embodiments of the present invention also provide a method for scheduling slice resources, which is applied to the network slice selection function NSSF, including:

Obtain slice data analysis results from the network analysis logic function NWDAF;

Slice-level KPIs and network performance adjustments are made based on data analysis results.

An embodiment of the present invention also provides a network slice selection function NSSF network element, including: a processor; a memory connected to the processor, and a transceiver connected to the processor; wherein,

The transceiver is used to: obtain the data analysis result of the slice from the network analysis logic function NWDAF;

The processor is used to: perform slice-level KPIs and network performance adjustment according to the data analysis results.

The embodiment of the present invention also provides a network element, the network element is a network slice selection function NSSF, including:

The first acquisition module is used to acquire the data analysis result of the slice from the network analysis logic function NWDAF;

The first adjustment module is used to adjust slice-level KPIs and network performance according to the data analysis result.

Embodiments of the present invention also provide a method for scheduling slice resources, which is applied to the policy control function PCF, including:

Obtain slice data analysis results from the network analysis logic function NWDAF;

According to the data analysis results, adjust the policy configuration for different slices.

An embodiment of the present invention further provides a policy control function PCF network element, including: a processor; a memory connected to the processor, and a transceiver connected to the processor; wherein,

The transceiver is used to: receive the data analysis result of the slice from the NWDAF side of the network analysis logic function;

The processor is used for: adjusting the policy configuration for different slices according to the data analysis result.

Embodiments of the present invention also provide a method for scheduling slice resources, which is applied to the policy control function PCF, including:

The second acquisition module is used to acquire the data analysis result of the slice from the network analysis logic function NWDAF;

The second adjustment module is used to adjust the policy configuration for different slices according to the data analysis result.

Embodiments of the present invention further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method for scheduling slice resources on the NWDAF side, the NSSF side, or the PCF side are implemented. .

The beneficial effects of the above technical solutions of the present invention are as follows: NWDAF can obtain data analysis results of slice resources of the current network according to the analysis data of the current network, distribute the data analysis results to the NSSF or PCF, and dynamically adjust the slice resources by the NSSF or PCF , improve resource utilization and meet the SLA requirements of more slice users.

Description of drawings

1 shows a schematic flowchart of a method for scheduling slice resources on the NWDAF side according to an embodiment of the present invention;

FIG. 2 shows a schematic flowchart of a method for scheduling slice resources on an NSSF side according to an embodiment of the present invention;

3 is a schematic flowchart of a method for scheduling slice resources on the PCF side according to an embodiment of the present invention;

FIG. 4 shows a schematic flowchart of a method for scheduling slice resources according to an embodiment of the present invention;

FIG. 5 is a schematic structural diagram of a network element module on the NWDAF side according to an embodiment of the present invention;

FIG. 6 is a schematic structural diagram of a network element module on the NSSF side according to an embodiment of the present invention;

FIG. 7 is a schematic structural diagram of a network element module on the PCF side according to an embodiment of the present invention;

FIG. 8 shows a structural block diagram of a network element according to an embodiment of the present invention.

Detailed ways

In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, the following will be described in detail with reference to the accompanying drawings and specific embodiments. In the following description, specific details such as specific configurations and components are provided merely to assist in a comprehensive understanding of embodiments of the present invention. Accordingly, it should be apparent to those skilled in the art that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted for clarity and conciseness.

It is to be understood that reference throughout the specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic associated with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily necessarily referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments.

In various embodiments of the present invention, it should be understood that the size of the sequence numbers of the following processes does not mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, rather than the implementation of the present invention The implementation of the examples constitutes no limitation.

Additionally, the terms "system" and "network" are often used interchangeably herein.

In the embodiments provided in this application, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean that B is only determined according to A, and B may also be determined according to A and/or other information.

As shown in FIG. 1, an embodiment of the present invention provides a slice resource scheduling method, which is applied to the network analysis logic function NWDAF, and specifically includes the following steps:

Step 11: Obtain analysis data for slices.

Among them, NWDAF is a service interface, which can collect and analyze data from the network regularly, such as obtaining information from NFs, Operation Administration Maintenance (OAM) entities or application functions (Application Function, AF), etc., and offline analysis of these information , which regularly updates network performance information. The NSSF may subscribe to the NWDAF for network performance information, or the NWDAF may notify the NSSF of the network performance information.

Wherein, step 11 may include at least one of the following:

Obtain the first analysis data from the network function NFs and/or the operation management and maintenance OAM; wherein the first analysis data includes: key performance indicators (Key Performance Indicators, KPIs) and performance measurement indicators (Performance Measurements) of the stored slices at least one of. The first analysis data may come from network elements such as NFs and OAM. The first analysis data may include KPIs and performance measurement indicators of the currently stored slices on the RAN side of the radio access network and the CN side of the core network.

Obtain second analysis data from the PCF; wherein the second analysis data includes: slice priority information. That is, the PCF reports the slice priority information of different slices configured by the policy to the NWDAF.

From the NSSF, the third analysis data is obtained. The third analysis data includes at least one item of slice congestion information, percentage information of user experience value, and service requirement information of newly added slices. The percentage information of the user experience value refers to the percentage of the user experience value that each slice can satisfy. That is to say, the NSSF simultaneously reports to the NWDAF at least one of the slice congestion information of the current network slice, the percentage of the user experience value that each slice can satisfy, and the service requirement information of the newly added slice.

Step 12: Analyze and update the analysis data to obtain a data analysis result.

Wherein, step 12 includes: using an analysis model to analyze and update the analysis data to obtain a data analysis result. Among them, the analysis model is trained by NWDAF through offline data analysis.

Correspondingly, before step 12, the method further includes: performing offline data analysis, and training an analysis model. That is to say, NWDAF performs offline data analysis to train an analysis model, and then performs online analysis and update through the input data.

Specifically, the NWDAF needs to take the user service experience as the goal, perform network service quality (Quality of Service, QoS) file (profile) conversion, and form a QoS profile data model.

Step 13: Send the data analysis result to the network slice selection function NSSF or the policy control function PCF.

Among them, when new slices are added to the network, NWDAF analyzes the network congestion status and sends the corresponding optimization indicators to the PCF and NSSF for reasonable resource scheduling, decomposes them into the access network, transmission network and core network, and dynamically adjusts existing Slice network resources and indicators of each domain to maximize the SLA of contracted users and provide SLA contract reliability guarantee for potential users.

Wherein, the data analysis result includes: QoS configuration file suggestion for the quality of service of the slice service and/or slice load information. Specifically, step 13 includes: the NWDAF delivers the data analysis result to the NSSF according to the slice priority information. For example, the QoS profile suggestion of each service of the slice and the slice load information of different slices are delivered to the NSSF. Accordingly, NSSF adjusts according to the current situation, whether to meet the SLA of the slice tenant and how many users it can meet. It is worth pointing out that, at this time, NSSF is partially adding users in the slice, and testing the SLA that can be satisfied, and finally obtains a data and signs a contract with the tenant, and then configures it. In addition, the data analysis result may further include at least one of the following information: spatial validity condition (spatial validity condition), such as Tracking Area Identity (TAI); spatial validity condition (time validity condition), such as time window (Time Window); NWDAF service identifier (transactionid); Single Network Slice Selection Assistance Information (S-NSSAI); S-NSSAI list (a list of S-NSSAI); Maximum Registration Users (MaximumRegistration Users) ); a list of application, etc. The relevant information of the reference list may include: application identifier (application id), maximum number of users of the application (Maximum Users for the application), average service (average service) average score (Mean Opinion Score, MOS), application user experience Satisfaction percentage (how many percentage UE's experience for the application satisfy), etc.

That is to say, when a new slice is added to the network, NWDAF can measure the KPIs of the current network slice level (slice level) and the RAN/CN performance measurement indicators, such as what percentage of users (such as slice users/ Stream QoS) service experience, slice priority, uplink/downlink bandwidth/bit rate of each slice of each RAN node, etc., perform statistical calculation, and then send the statistical results to NSSF or PCF, NSSF or PCF Perform reasonable slice resource planning and scheduling.

The slice resource scheduling method on the NWDAF side has been described above, and the following embodiments of the present invention will further introduce the slice resource scheduling method on the NSSF side.

As shown in FIG. 2, an embodiment of the present invention provides a slice resource scheduling method, which is applied to a network slice selection function NSSF, and specifically includes the following steps:

Step 21: Obtain the data analysis result of the slice from the network analysis logic function NWDAF.

The data analysis result is that when a new slice is added to the network, the NWDAF sends the corresponding optimization indicators to the PCF and NSSF after analyzing the network congestion status through NWDAF. The data analysis results include: QoS profile recommendations for the slice service and/or slice load information.

Step 22: Adjust slice-level KPIs and network performance according to the data analysis results.

Among them, NSSF can perform slice-level KPIs and network performance adjustment for RAN and CN according to data analysis results. Specifically, the NSSF can directly request or subscribe to network status information from the NWDAF through the serviced interface. NSSF can determine slice-level quality information and achievable tenant SLA slice satisfaction based on slice-level data analysis results and current network policies provided by NWDAF.

Wherein, before step 21, the method further includes: sending at least one item of slice congestion information of different slices, percentage information of user experience value, and service requirement information of newly added slices to the NWDAF. The percentage information of the user experience value refers to the percentage of the user experience value that each slice can satisfy. That is to say, the NSSF simultaneously reports to the NWDAF at least one of the slice congestion information of the current network slice, the percentage of the user experience value that each slice can satisfy, and the service requirement information of the newly added slice.

Specifically, the operator can divide the network load degree into different levels through testing, quantify the SLA of the slice tenant into a specific value range according to the network load degree, that is, the user experience value, and then according to the input of each type of slice required Items of network indicators, such as: cell coverage in the service area, current network load information, etc., are reported to NWDAF for analysis to output the corresponding user experience value.

The slice resource scheduling method on the NWDAF and NSSF sides has been described above, and the following embodiments of the present invention will further introduce the slice resource scheduling method on the PCF side.

As shown in FIG. 3 , an embodiment of the present invention provides a method for scheduling slice resources, which is applied to, and specifically includes the following steps:

Step 31: Obtain the data analysis result of the slice from the network analysis logic function NWDAF.

The data analysis result is that when a new slice is added to the network, the NWDAF sends the corresponding optimization indicators to the PCF and NSSF after analyzing the network congestion status through NWDAF. Wherein, the data analysis result includes: QoS configuration file suggestion for the quality of service of the slice service and/or slice load information.

Step 32: According to the data analysis result, adjust the policy configuration for different slices.

Among them, the PCF re-adjusts the policy configuration according to the data analysis result obtained from the NWDAF and the slice priority information of each slice.

Wherein, before step 31, the method further includes: sending slice priority information of different slices to the NWDAF. That is, the PCF reports the slice priority information of different slices configured by the policy to the NWDAF.

The above describes the slice resource scheduling method according to the embodiment of the present invention from the NWDAF, NSSF, and PCF sides, respectively. The following describes the slice resource scheduling method in conjunction with the accompanying drawings in the implementation of the present invention.

As shown in FIG. 4 , the slice resource scheduling method is applied to a slice network, and the slice network includes, but is not limited to, network elements such as NWDAF, NFs/OAM, NSSF, and PCF. Wherein, the method may include the following processes:

Step 41: Acquire analysis data.

Wherein, step 41 includes:

Step 41a: The NFs/OAM sends at least one of the KPIs and performance measurement indicators of the slices stored on the RAN/CN side to the NWDAF.

Step 41b: The NSSF sends at least one item of slice congestion information, user experience value percentage information, and service requirement information of the newly added slice to the NWDAF.

Step 41c: PCF sends slice priority information to NWDAF.

It is worth noting that the embodiment of the present invention does not limit the time sequence between steps 41a, 41b, and 41c, and the above steps may be performed in parallel or serially performed in any order.

Step 42: Using the analysis model, analyze and update the collected analysis data to obtain a data analysis result.

Step 43a: NWDAF sends the data analysis result to NSSF.

Step 44a: NSSF adjusts slice-level KPIs and network performance according to the data analysis result.

Or, after step 42, it also includes:

Step 43b: NWDAF sends the data analysis result to PCF.

Step 44b: The PCF adjusts the policy configuration for different slices according to the data analysis result.

Wherein, in the embodiment of the present invention, the sequence sequence between steps 43a and 43b and the sequence sequence between steps 44a and 44b are not limited either.

In the slice resource scheduling method according to the embodiment of the present invention, the NWDAF can obtain the data analysis result of the slice resource of the current network according to the analysis data of the current network, distribute the data analysis result to the NSSF or the PCF, and the NSSF or the PCF can dynamically adjust the slice resource. , improve resource utilization and meet the SLA requirements of more slice users.

The above embodiments respectively introduce the slice resource scheduling method of the present invention, and the corresponding network elements thereof will be further described in this embodiment below with reference to the accompanying drawings.

Specifically, as shown in FIG. 5 , the network element 500 in the embodiment of the present invention is a network analysis logic function NWDAF, and includes the following functional modules:

an acquisition module 510, configured to acquire analysis data of the slice;

An analysis module 520, configured to analyze and update the analysis data to obtain a data analysis result;

The first sending module 530 is configured to send the data analysis result to the network slice selection function NSSF or the policy control function PCF.

Wherein, the obtaining module 510 includes at least one of the following:

a first acquisition sub-module, configured to acquire the first analysis data from the network function NFs and/or the operation management and maintenance OAM;

The second acquisition submodule is used to acquire the second analysis data from the PCF;

The third obtaining submodule is used for obtaining third analysis data from the NSSF.

Wherein, the first analysis data includes: at least one of key performance indicators KPIs and performance measurement indicators of the stored slices.

Wherein, the second analysis data includes: slice priority information.

The third analysis data includes at least one item of slice congestion information, percentage information of user experience value, and service requirement information of newly added slices.

Wherein, the network element 500 further includes:

The training module is used to perform offline data analysis and train an analysis model.

Wherein, the analysis module 520 includes:

The analysis sub-module is used to analyze and update the analysis data by using the analysis model to obtain the data analysis result.

Wherein, the data analysis result includes: QoS configuration file suggestion for the quality of service of the slice service and/or slice load information.

The embodiment of the network element on the NWDAF side of the present invention corresponds to the embodiment of the above method, and all implementation means in the embodiment of the above method are applicable to the embodiment of the network element, and the same technical effect can also be achieved.

As shown in FIG. 6 , a network element 600 according to an embodiment of the present invention, the network element is a network slice selection function NSSF, includes the following functional modules:

The first obtaining module 610 is configured to obtain the data analysis result of the slice from the network analysis logic function NWDAF;

The first adjustment module 620 is configured to perform slice-level KPIs and network performance adjustment according to the data analysis result.

Wherein, the network element 600 further includes:

The second sending module is configured to send the slice congestion information of different slices, the percentage information of the user experience value and the service requirement information of the newly added slices to the NWDAF.

Wherein, the data analysis result includes: QoS configuration file suggestion for the quality of service of the slice service and/or slice load information.

The embodiment of the network element on the NSSF side of the present invention corresponds to the embodiment of the foregoing method, and all implementation means in the foregoing method embodiment are applicable to the embodiment of the network element, and the same technical effect can also be achieved.

As shown in FIG. 7 , a network element 700 according to an embodiment of the present invention is a policy control function PCF, and includes the following functional modules:

The second obtaining module 710 is configured to obtain the data analysis result of the slice from the network analysis logic function NWDAF;

The second adjustment module 720 is configured to adjust the policy configuration for different slices according to the data analysis result.

The network element 700 further includes:

The third sending module is configured to send slice priority information of different slices to the NWDAF.

Wherein, the data analysis result includes: QoS configuration file suggestion for the quality of service of the slice service and/or slice load information.

The PCF side network element embodiment of the present invention corresponds to the above method embodiment, and all implementation means in the above method embodiment are applicable to the network element embodiment, and the same technical effect can also be achieved.

In this embodiment of the present invention, NWDAF can obtain data analysis results of slice resources of the current network according to the analysis data of the current network, distribute the data analysis results to the NSSF or PCF, and the NSSF or PCF can dynamically adjust the slice resources to improve resource utilization. , to meet the SLA requirements of more slice users.

In order to better achieve the above purpose, as shown in FIG. 8 , a fourth embodiment of the present invention further provides a network element, the network element includes: a processor 800; A memory 820, and a transceiver 810 connected to the processor 800 through a bus interface; the memory 820 is used for storing programs and data used by the processor when performing operations; sending data information through the transceiver 810 or The pilot frequency also receives the uplink control channel through the transceiver 810; when the processor 800 calls and executes the program and data stored in the memory 820, the following functions are implemented:

The network element is the NWDAF network element of the network analysis logic function, and the transceiver 810 is used for: acquiring the analysis data of the slice;

The processor 800 is used to: analyze and update the analysis data to obtain a data analysis result;

The transceiver 810 is further configured to: send the data analysis result to the network slice selection function NSSF or the policy control function PCF.

The transceiver 810 is specifically used for at least one of the following:

Obtain the first analysis data from the network function NFs and/or the operation management and maintenance OAM;

From the PCF, obtain the second analysis data;

From the NSSF, the third analysis data is obtained.

Wherein, the first analysis data includes: at least one of key performance indicators KPIs and performance measurement indicators of the stored slices.

Wherein, the second analysis data includes: slice priority information.

The third analysis data includes at least one item of slice congestion information, percentage information of user experience value, and service requirement information of newly added slices.

The processor 800 is further configured to: perform offline data analysis and train an analysis model.

The processor 800 is specifically configured to: use the analysis model to analyze and update the analysis data to obtain the data analysis result.

The network element is the network slice selection function NSSF network element, and the transceiver 810 is used to: obtain the data analysis result of the slice from the network analysis logic function NWDAF;

The processor 800 is configured to: perform slice-level KPIs and network performance adjustment according to the data analysis result.

The transceiver 810 is further configured to: send the slice congestion information of different slices, the percentage information of the user experience value and the service requirement information of the newly added slices to the NWDAF.

Wherein, the data analysis result includes: QoS configuration file suggestion for the quality of service of the slice service and/or slice load information.

The network element is the PCF network element of the policy control function, and the transceiver 810 is used for: acquiring the data analysis result of the slice from the network analysis logic function NWDAF;

The processor 800 is configured to: adjust the policy configuration for different slices according to the data analysis result.

The transceiver 810 is further configured to: send slice priority information of different slices to the NWDAF.

Wherein, the data analysis result includes: QoS configuration file suggestion for the quality of service of the slice service and/or slice load information.

8, the bus architecture may include any number of interconnected buses and bridges, specifically one or more processors represented by processor 800 and various circuits of memory represented by memory 820 are linked together. The bus architecture may also link together various other circuits, such as peripherals, voltage regulators, and power management circuits, which are well known in the art and, therefore, will not be described further herein. The bus interface provides the interface. Transceiver 810 may be a number of elements, including a transmitter and a transceiver, providing a means for communicating with various other devices over a transmission medium. The processor 800 is responsible for managing the bus architecture and general processing, and the memory 820 may store data used by the processor 800 in performing operations.

Those skilled in the art can understand that all or part of the steps of implementing the above embodiments can be completed by hardware, or can be completed by instructing relevant hardware through a computer program, where the computer program includes instructions for executing part or all of the steps of the above method ; and the computer program can be stored in a readable storage medium, and the storage medium can be any form of storage medium.

Embodiments of the present invention further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, each process of the foregoing slice resource scheduling method embodiments can be implemented, and can achieve the same The technical effect, in order to avoid repetition, will not be repeated here. The computer-readable storage medium is, for example, a read-only memory (Read-Only Memory, ROM for short), a random access memory (Random Access Memory, RAM for short), a magnetic disk, or an optical disk.

In addition, it should be pointed out that, in the apparatus and method of the present invention, obviously, each component or each step can be decomposed and/or recombined. These disaggregations and/or recombinations should be considered as equivalents of the present invention. Also, the steps of performing the above-mentioned series of processes can naturally be performed in chronological order in the order described, but need not necessarily be performed in chronological order, and some steps can be performed in parallel or independently of each other. Those of ordinary skill in the art can understand that all or any steps or components of the method and device of the present invention can be implemented in any computing device (including a processor, storage medium, etc.) or a network of computing devices in hardware, firmware, etc. , software or a combination thereof, which can be realized by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

Accordingly, the objects of the present invention can also be achieved by running a program or set of programs on any computing device. The computing device may be a known general purpose device. Therefore, the object of the present invention can also be achieved only by providing a program product containing program code for implementing the method or apparatus. That is, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any known storage medium or any storage medium developed in the future. It should also be pointed out that, in the device and method of the present invention, obviously, each component or each step can be decomposed and/or recombined. These disaggregations and/or recombinations should be considered as equivalents of the present invention. Also, the steps of executing the above-described series of processes can naturally be executed in chronological order in the order described, but need not necessarily be executed in chronological order. Certain steps may be performed in parallel or independently of each other.

The above are the preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, without departing from the principles of the present invention, several improvements and modifications can be made. It should be regarded as the protection scope of the present invention.

Claims (26)

1. A slice resource scheduling method is applied to a network analysis logic function (NWDAF), and is characterized by comprising the following steps:

acquiring analysis data of the slice;

analyzing and updating the analysis data to obtain a data analysis result;

and sending the data analysis result to a network slice selection function NSSF or a policy control function PCF.

2. The slice resource scheduling method of claim 1, wherein the step of obtaining analysis data of the slice comprises at least one of:

obtaining first analysis data from network function NFs and/or an operation, administration, and maintenance, OAM;

acquiring second analysis data from the PCF;

from the NSSF, third analytical data is obtained.

3. The method for scheduling slice resources according to claim 2, wherein the first analysis data comprises: storing at least one of key performance indicators, KPIs, and performance measurement indicators for the slice.

4. The method for scheduling slice resources according to claim 2, wherein the second analysis data comprises: slice priority information.

5. The method of claim 2, wherein the third analysis data comprises: at least one of slice congestion information, percentage information of user experience values, and service demand information of newly added slices.

6. The slice resource scheduling method according to claim 1, wherein the step of analyzing and updating the analysis data to obtain a data analysis result further comprises:

and (5) performing off-line data analysis and training an analysis model.

7. The slice resource scheduling method of claim 6, wherein the step of analyzing and updating the analysis data to obtain a data analysis result comprises:

and analyzing and updating the analysis data by using the analysis model to obtain a data analysis result.

8. The method for scheduling slice resources according to claim 1, wherein the data analysis result comprises: quality of service QoS profile recommendations for a slice service and/or slice load information.

9. A network analysis logic function, NWDAF, network element, comprising: a processor; a memory coupled to the processor, and a transceiver coupled to the processor; wherein,

the transceiver is configured to: acquiring analysis data of the slice;

the processor is configured to: analyzing and updating the analysis data to obtain a data analysis result;

the transceiver is further configured to: and sending the data analysis result to a network slice selection function NSSF or a policy control function PCF.

10. The network analysis logic function, NWDAF, network element of claim 9, wherein the transceiver is specifically configured to at least one of:

obtaining first analysis data from network function NFs and/or an operation, administration, and maintenance, OAM;

acquiring second analysis data from the PCF;

from the NSSF, third analytical data is obtained.

11. The network analysis logic function, NWDAF, network element of claim 9, wherein the processor is further configured to:

and (5) performing off-line data analysis and training an analysis model.

12. The network analysis logic function, NWDAF, network element of claim 11, wherein the processor is specifically configured to:

and analyzing and updating the analysis data by using the analysis model to obtain a data analysis result.

13. A network element, the network element being a network analysis logic function, NWDAF, comprising:

the acquisition module is used for acquiring analysis data of the slice;

the analysis module is used for analyzing and updating the analysis data to obtain a data analysis result;

and the first sending module is used for sending the data analysis result to a network slice selection function NSSF or a policy control function PCF.

14. A slice resource scheduling method is applied to a network slice selection function NSSF, and is characterized by comprising the following steps:

acquiring a data analysis result of the slice from a network analysis logic function (NWDAF);

and adjusting the KPIs and the network performance at the slicing level according to the data analysis result.

15. The slice resource scheduling method of claim 14, wherein the step of obtaining the data analysis result of the slice from a network analysis logic function NWDAF further comprises, before the step of:

and sending at least one of slice congestion information of different slices, percentage information of user experience values and service requirement information of newly added slices to the NWDAF.

16. The method of claim 14, wherein the data analysis result comprises: quality of service QoS profile recommendations for a slice service and/or slice load information.

17. A network slice selection function, NSSF, network element, comprising: a processor; a memory coupled to the processor, and a transceiver coupled to the processor; wherein,

the transceiver is configured to: acquiring a data analysis result of the slice from a network analysis logic function (NWDAF);

the processor is configured to: and adjusting the KPIs and the network performance at the slicing level according to the data analysis result.

18. The network slice selection function, NSSF, network element of claim 17, wherein the transceiver is further configured to:

and sending slice congestion information of different slices, percentage information of user experience values and service requirement information of newly added slices to the NWDAF.

19. A network element, the network element being a network slice selection function, NSSF, comprising:

the first acquisition module is used for acquiring a data analysis result of the slice from a network analysis logic function NWDAF;

and the first adjusting module is used for adjusting the KPIs and the network performance at the slicing level according to the data analysis result.

20. A slice resource scheduling method is applied to a policy control function PCF, and is characterized by comprising the following steps:

acquiring a data analysis result of the slice from a network analysis logic function (NWDAF);

and according to the data analysis result, performing strategy configuration adjustment on different slices.

21. The slice resource scheduling method of claim 20, wherein the step of obtaining the data analysis result of the slice from a network analysis logic function NWDAF further comprises, before the step of:

transmitting slice priority information for different slices to the NWDAF.

22. The method for scheduling slicing resources of claim 20, wherein the data analysis result comprises: quality of service QoS profile recommendations for a slice service and/or slice load information.

23. A policy control function, PCF, network element, comprising: a processor; a memory coupled to the processor, and a transceiver coupled to the processor; wherein,

the transceiver is configured to: acquiring a data analysis result of the slice from a network analysis logic function (NWDAF);

the processor is configured to: and according to the data analysis result, performing strategy configuration adjustment on different slices.

24. The policy control function PCF network element of claim 23 wherein said transceiver is further configured to:

transmitting slice priority information for different slices to the NWDAF.

25. A slice resource scheduling method is applied to a policy control function PCF, and is characterized by comprising the following steps:

the second acquisition module is used for acquiring a data analysis result of the slice from a network analysis logic function NWDAF;

and the second adjusting module is used for performing strategy configuration adjustment on different slices according to the data analysis result.

26. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for slice resource scheduling according to any one of claims 1 to 8, 14 to 16, 20 to 22.

Images