A resource monitoring method and related device

Abstract

This disclosure provides a resource monitoring method and related equipment, relating to the field of communication technology. The resource monitoring method, executed by a PCF network element, includes: determining QoS parameters for AI and ML services, wherein the QoS parameters for AI and ML services include an AMBR (Advanced Management Principle) for AI and ML services; and transmitting the QoS parameters for AI and ML services to a UPF network element via an SMF network element, enabling the UPF network element to monitor the traffic of AI and ML services based on the AMBR for AI and ML services. This method, by determining QoS parameters including an AMBR for AI and ML services through a PCF network element and transmitting these QoS parameters to a UPF network element via an SMF network element, and then monitoring the traffic of AI and ML services through the UPF network element, achieves monitoring of network resource usage for AI and ML services in 5GS, and can prevent AI and ML services from consuming excessive network resources.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and more specifically, to a resource monitoring method, a PCF network element, a UPF network element, an AF network element, an electronic device, and a computer-readable storage medium. Background Technology

[0002] After achieving centralized network intelligence in 3GPP (3rd Generation Partnership Project) SA2, 3GPP R18 research launched AI (Artificial Intelligence) and ML (Machine Learning) projects, enabling 5GS (5G system) to support distributed network intelligence and federated learning.

[0003] Current 3GPP network monitoring methods do not monitor network resource usage for AI and ML services in 5GS, which may lead to AI and ML services consuming excessive network resources and affecting other services.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] This disclosure provides a resource monitoring method, a PCF network element, a UPF network element, an AF network element, an electronic device, and a computer-readable storage medium, which can monitor the network resource usage of AI and ML services in 5GS.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] According to one aspect of this disclosure, a resource monitoring method is provided, the method being executed by a PCF network element, comprising: determining QoS parameters for AI and ML services, the QoS parameters for AI and ML services including an AMBR for AI and ML services; and distributing the QoS parameters for AI and ML services to a UPF network element via an SMF network element, such that the UPF network element monitors the traffic of AI and ML services according to the AMBR for AI and ML services.

[0008] In some embodiments of this disclosure, the QoS parameters of the AI ​​and ML services are determined by the PCF network element based on at least one of the following: UE subscription information for participating in the AI ​​and ML services, capability information reported by the UE, resource information requested by the UE to perform the AI ​​and ML services, QoS information requested by the AF network element to perform the AI ​​and ML services, resource information requested by the AF network element to perform the AI ​​and ML services, and network service traffic information.

[0009] In some embodiments of this disclosure, the method further includes: receiving network resource adjustment information sent by the AF network element via the NEF network element, wherein the network resource adjustment information is determined by the AF network element based on a reported monitoring event, the monitoring event being triggered by the UPF network element detecting that the traffic of AI and ML services exceeds the AMBR for AI and ML services, and the monitoring event being reported to the AF network element via the NEF network element; and adjusting the QoS parameters of the AI ​​and ML services according to the network resource adjustment information.

[0010] According to another aspect of this disclosure, a resource monitoring method is provided, the method being executed by a UPF network element, comprising: receiving QoS parameters of AI and ML services issued by a PCF network element via an SMF network element, wherein the QoS parameters of the AI ​​and ML services include an AMBR for the AI ​​and ML services; and monitoring the traffic of the AI ​​and ML services based on the AMBR for the AI ​​and ML services and application-related information of data packets.

[0011] In some embodiments of this disclosure, the method further includes: if the traffic of the AI ​​and ML services is detected to exceed the AMBR for the AI ​​and ML services, a monitoring event is triggered and reported to the AF network element via the NEF network element, so that the AF network element determines network resource adjustment information based on the reported monitoring event, sends the network resource adjustment information to the PCF network element via the NEF network element, and then the PCF network element adjusts the QoS parameters of the AI ​​and ML services according to the network resource adjustment information.

[0012] In some embodiments of this disclosure, the application-related information of the data packet includes at least one of the following: application identifier, DNN, APN, and quintuple; wherein, the step of monitoring the traffic of AI and ML services based on the application-related information of the data packet according to the AMBR for AI and ML services includes: when the UE performs AI and ML services, determining the traffic of AI and ML services according to the application-related information of the data packet, and monitoring the traffic of AI and ML services using the AMBR for AI and ML services.

[0013] According to another aspect of this disclosure, a resource monitoring method is provided, the method being executed by an AF network element, comprising: receiving a monitoring event reported via a NEF network element, the monitoring event being triggered by a UPF network element detecting that the traffic of AI and ML services exceeds the AMBR for AI and ML services, the AMBR for AI and ML services being information in the QoS parameters of AI and ML services; determining network resource adjustment information based on the reported monitoring event; and sending the network resource adjustment information to a PCF network element via the NEF network element, so that the PCF network element adjusts the QoS parameters of the AI ​​and ML services according to the network resource adjustment information.

[0014] According to another aspect of this disclosure, a PCF network element is provided, comprising: a parameter determination unit, configured to determine QoS parameters for AI and ML services, wherein the QoS parameters for AI and ML services include an AMBR for AI and ML services; and a parameter transmission unit, configured to transmit the QoS parameters for AI and ML services to a UPF network element via an SMF network element, such that the UPF network element monitors the traffic of AI and ML services according to the AMBR for AI and ML services.

[0015] According to another aspect of this disclosure, a UPF network element is provided, comprising: a parameter receiving unit, configured to receive QoS parameters of AI and ML services issued by a PCF network element via an SMF network element, wherein the QoS parameters of the AI ​​and ML services include an AMBR for the AI ​​and ML services; and a monitoring unit, configured to monitor the traffic of the AI ​​and ML services based on the AMBR for the AI ​​and ML services and application-related information of the data packets.

[0016] According to another aspect of this disclosure, an AF network element is provided, comprising: an event receiving unit, configured to receive a monitoring event reported via a NEF network element, wherein the monitoring event is triggered by a UPF network element detecting that the traffic of AI and ML services exceeds the AMBR for AI and ML services, and the AMBR for AI and ML services is information in the QoS parameters of AI and ML services; an information adjustment unit, configured to determine network resource adjustment information based on the reported monitoring event; and an information sending unit, configured to send the network resource adjustment information to a PCF network element via the NEF network element, so that the PCF network element adjusts the QoS parameters of the AI ​​and ML services according to the network resource adjustment information.

[0017] According to another aspect of this disclosure, an electronic device is provided, comprising: one or more processors; and a storage device configured to store one or more programs that, when executed by the one or more processors, cause the one or more processors to perform the method described in the above embodiments.

[0018] According to another aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described in the above embodiments.

[0019] The resource monitoring method provided in this disclosure determines the QoS parameters of AI and ML services through the PCF network element. The QoS parameters of AI and ML services include the AMBR specific to AI and ML services. The QoS parameters of AI and ML services are sent to the UPF network element via the SMF network element. When the UE performs AI and ML services, the UPF network element can monitor the traffic used for AI and ML services based on the AMBR specific to AI and ML services in the QoS parameters. Therefore, the resource monitoring method provided in this disclosure does not require adding additional network elements or interfaces between network elements in the 5GS. It enhances the functionality of the PCF network element by determining the QoS parameters of AI and ML services through the PCF network element, and also enhances the functionality of the UPF network element by monitoring the traffic used for AI and ML services based on the AMBR specific to AI and ML services through the UPF network element. This enables monitoring of network resource usage for AI and ML services in the 5GS, preventing AI and ML services from consuming excessive network resources.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0022] Figure 1 A schematic diagram of the architecture of a communication system according to an embodiment of this disclosure is shown;

[0023] Figure 2 A flowchart of a resource monitoring method applied to a PCF network element is shown in an embodiment of this disclosure;

[0024] Figure 3 A flowchart of a resource monitoring method applied to a UPF network element is shown in an embodiment of this disclosure;

[0025] Figure 4A flowchart of a resource monitoring method applied to an AF network element is shown in an embodiment of this disclosure;

[0026] Figure 5 A flowchart of a resource monitoring method according to an embodiment of this disclosure is shown;

[0027] Figure 6 A schematic diagram of the structure of a PCF network element in an embodiment of this disclosure is shown;

[0028] Figure 7 A schematic diagram of the structure of a UPF network element in an embodiment of this disclosure is shown;

[0029] Figure 8 A schematic diagram of the structure of an AF network element in an embodiment of this disclosure is shown;

[0030] Figure 9 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0031] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0032] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0033] Figure 1 A schematic diagram of the architecture of a communication system according to an embodiment of this disclosure is shown. This communication system can support the resource monitoring method provided in the embodiments of this disclosure. Figure 1As shown, the communication system may include: UE (User Equipment), RAN (Radio Access Network) equipment, AMF (Access and Mobility Management Function) network element, SMF (Session Management Function) network element, UPF (User Plane Function) network element, PCF (Policy Control Function) network element, NEF (Network Exposure Function) network element, and AF (Application Function) network element.

[0034] The UE can be various electronic devices, including but not limited to smartphones, tablets, laptops, desktop computers, wearable devices, augmented reality devices, and virtual reality devices. Optionally, the client of the application installed on different UEs can be the same, or the client of the same type of application based on different operating systems. Depending on the terminal platform, the specific form of the application client can also be different; for example, the application client can be a mobile client, a PC client, etc.

[0035] RAN equipment can include devices in the access network that communicate with wireless terminals via one or more sectors on the air interface. UEs can access AMF network elements through RAN equipment. Specifically, when a UE accesses an AMF network element through RAN equipment, it can do so via network-side equipment such as 5G and later versions of base stations (e.g., 5G NR NB) or base stations in other communication systems (e.g., eNB base stations). AMF network elements are primarily used for UE mobility management, access authentication / authorization, and also responsible for transmitting user policies between the UE and PCF network elements. SMF network elements are mainly used for session management, UE Internet Protocol address allocation and management, selection of manageable user plane functions, policy control, or termination points for charging function interfaces, and downlink data notification. PCF network elements provide a unified policy framework to guide network behavior and offer policy rule information to control plane function network elements (e.g., AMF and SMF network elements). UPF network elements can be used for packet routing and forwarding, or QoS processing of user plane data. User data can be accessed to the DN (data network) through this network element. The NEF (Network Element) is located between the 5G core network and external third-party application functions, and may also have some internal application functions. It is responsible for managing publicly accessible network data. All external applications wishing to access internal 5G core network data must go through the NEF. The NEF provides corresponding security guarantees to ensure the security of external applications accessing the network, offering features such as open QoS customization capabilities for external applications, mobility state event subscription, and application function request distribution. The AF (Application Frame) is mainly used to transmit application-side requirements to the network side, such as QoS requirements and user state event subscriptions. The AF can be a third-party functional entity or an application service deployed by the operator. For third-party application functional entities, authorization processing can also be performed through the NEF when interacting with the core network. For example, if a third-party application function directly sends a request to the NEF, the NEF determines whether the AF is allowed to send the request. If the verification is successful, the request is forwarded to the corresponding PCF (Physical Processing Element) or UDM (User Device Element).

[0036] Those skilled in the art will know that Figure 1 The number of UE, RAN equipment, AMF network elements, SMF network elements, UPF network elements, PCF network elements, NEF network elements, and AF network elements is merely illustrative; any number of network elements and terminals can be included depending on actual needs. This disclosure does not limit the number of network elements and terminals.

[0037] In the above Figure 1In the illustrated communication system architecture, the QoS (Quality of Service) parameters for AI and ML services are determined through the PCF network element. These QoS parameters include the AMBR (Aggregate Maximum Bit Rate) specific to AI and ML services. These QoS parameters can be sent to the UPF network element via the SMF network element. Thus, when the UE performs AI and ML services, the UPF network element can monitor the traffic used for AI and ML services based on the AMBR specific to AI and ML services within the QoS parameters. This enables monitoring of network resource usage for AI and ML services in 5GS, preventing excessive network resource consumption by AI and ML services.

[0038] In specific implementation, the main process includes the following steps: (1) The PCF network element determines the QoS parameters of AI and ML services, which include the AMBR for AI and ML services, and sends the QoS parameters of AI and ML services to the SMF network element; (2) The SMF network element sends the QoS parameters of AI and ML services to the UPF network element, so that when the UE performs AI and ML services, the UPF network element can use the AMBR for AI and ML services in the parameters to monitor the traffic used for AI and ML services; (3) If U The PF network element detects that the traffic used for AI and ML services exceeds the AMBR for AI and ML services and triggers a monitoring event report; (4) the reported monitoring event is reported to the AF network element via the NEF network element; (5) after receiving the reported monitoring event, the AF network element adjusts and reduces the demand for network resources, determines the network resource adjustment information, and feeds back the network resource adjustment information; (6) the network resource adjustment information is sent to the PCF network element via the NEF network element, so that the PCF network element can refer to the network resource adjustment information when determining the QoS parameters of AI and ML services.

[0039] exist Figure 1 Under the communication system architecture shown, this disclosure provides a resource monitoring method, which can be applied to, but is not limited to, [various applications]. Figure 1 As shown in the PCF network element, in principle, this method can be executed by any electronic device with computing capabilities.

[0040] Figure 2 A flowchart of a resource monitoring method applied to a PCF network element is shown in an embodiment of this disclosure, as follows: Figure 2 As shown, the method may include the following steps.

[0041] Step S210: Determine the QoS parameters for AI and ML services, wherein the QoS parameters for AI and ML services include the AMBR for AI and ML services.

[0042] QoS refers to a network's ability to provide better service for specified network communications using various underlying technologies. It is a network security mechanism and a technology used to solve problems such as network latency and congestion. QoS is not necessary if the network is only used for specific, time-unrestricted applications, such as web applications, but it is essential for critical and multimedia applications. When the network is overloaded or congested, QoS ensures that important traffic is not delayed or dropped, while guaranteeing efficient network operation.

[0043] AMBR defines the upper limit of the total bit rate of all GBR (non-Guaranteed Bit Rate) bearers of a UE. AMBR for AI and ML services can be used to limit the maximum transmission rate of AI and ML services.

[0044] In addition, the QoS parameters for AI and ML services may include, but are not limited to: ARP (Allocation and Retension Priority) for resource allocation, priority processing level of service data streams, average window, maximum data burst size, uplink guaranteed bandwidth, downlink guaranteed bandwidth, maximum uplink bandwidth, maximum downlink bandwidth, maximum downlink packet loss rate, maximum uplink packet loss rate, packet delay budget, and packet error rate.

[0045] In some embodiments of this disclosure, the QoS parameters of AI and ML services are determined by the PCF network element based on at least one of the following: UE subscription information for participating in AI and ML services, capability information reported by the UE, resource information requested by the UE to perform AI and ML services, QoS information requested by the AF network element to perform AI and ML services, resource information requested by the AF network element to perform AI and ML services, and network service traffic information.

[0046] The UE subscription information for participating in AI and ML services refers to the subscription information of UEs that are eligible to perform AI and ML services. The capability information reported by the UE may include, but is not limited to, wireless / network capabilities, intelligent capabilities, and computing capabilities. The resource information requested by the UE to perform AI and ML services refers to the resource information requested by the UE to perform AI and ML services. The QoS information requested by the AF network element to perform AI and ML services refers to the QoS requested by the AF network element to perform AI and ML services. The network service traffic information may include, but is not limited to: real-time usage statistics collected by UPF or SMF network elements, and historical usage statistics obtained through OAM (network management).

[0047] Step S220: The QoS parameters of AI and ML services are sent from the SMF network element to the UPF network element, so that the UPF network element can monitor the traffic of AI and ML services according to the AMBR for AI and ML services.

[0048] In this embodiment, the PCF network element can determine the QoS parameters of AI and ML services by combining one or more of the following based on actual application scenarios: UE subscription information, UE-reported capability information, resource information requested by the UE to perform AI and ML services, QoS information requested by the AF network element to perform AI and ML services, and network service traffic information. These QoS parameters include an AMBR (Advanced Management Principle) for AI and ML services. The QoS parameters are then sent to the UPF network element via the SMF network element. Thus, when the UE performs AI and ML services, the UPF network element can monitor the traffic used for AI and ML services based on the AMBR, enabling monitoring of network resource usage for AI and ML services in 5GS and preventing excessive network resource consumption by AI and ML services.

[0049] In some embodiments of this disclosure, the resource monitoring method may further include: the PCF network element receiving network resource adjustment information sent by the AF network element via the NEF network element; and adjusting the QoS parameters of AI and ML services according to the network resource adjustment information. The network resource adjustment information is determined by the AF network element based on reported monitoring events. These monitoring events are triggered by the UPF network element detecting that the traffic of AI and ML services exceeds the AMBR for AI and ML services, and the monitoring events are reported to the AF network element via the NEF network element.

[0050] The UPF network element obtains the QoS parameters of AI and ML services sent by the PCF network element via the SMF network element. These QoS parameters include the AMBR (Advanced Management Principle) for AI and ML services. When the UE performs AI and ML services, the UPF network element can monitor the traffic used for AI and ML services based on the application-related information of the data packets and the AMBR. The application-related information of the data packets can be used to identify the service applications performed by the UE. This information includes the application identifier, DNN (Data Network Name), APN (Access Point Name), and one or more pieces of information from the five-tuple. The UPF network element can identify the AI ​​and ML services performed by the UE based on the application-related information of the data packets and count the traffic used for these services. If the UPF network element determines that the traffic used for AI and ML services exceeds the AMBR for AI and ML services, it triggers a monitoring event report, which can be reported to the AF network element via the NEF network element.

[0051] After receiving a monitoring event, the AF (Analog-Forwarding) network element can determine network resource adjustment information and then send this information to the PCF (Network-Content-Forwarding) network element via the NEF (Network-Forwarding) network element. The PCF network element can then adjust the QoS parameters of AI and ML services based on this information. Specifically, the network resource adjustment information can reduce the demand on network resources, thereby reducing the need to guarantee network QoS.

[0052] In this embodiment, when the UPF network element detects that the traffic used for AI and ML services exceeds the AMBR for AI and ML services, it can trigger a monitoring event to be reported to the AF network element via the NEF network element. Then, the AF network element can determine network resource adjustment information based on the monitoring event and send the network resource adjustment information to the PAC network element via the NEF network element. In this way, the PCF network element can adjust the QoS parameters of AI and ML services based on the network resource adjustment information sent by the AF network element. Subsequently, the SMF network element can notify the UPF network element of the adjusted QoS parameters of AI and ML services, which can support flexible adjustment of QoS parameters according to service requirements.

[0053] Based on the same inventive concept, Figure 1 Under the communication system architecture shown, this disclosure provides a resource monitoring method, which can be applied to, but is not limited to, [various applications]. Figure 1 As shown in the UPF network element, in principle, this method can be executed by any electronic device with computing capabilities.

[0054] Figure 3 A flowchart of a resource monitoring method applied to a UPF network element is shown in an embodiment of this disclosure, as follows: Figure 3 As shown, the method may include the following steps.

[0055] Step S310: Receive the QoS parameters of AI and ML services sent by the PCF network element through the SMF network element, wherein the QoS parameters of AI and ML services include the AMBR for AI and ML services.

[0056] As explained above, the QoS parameters for AI and ML services are determined by the PCF network element based on one or more of the following: the UE subscription information for participating in AI and ML services, the capability information reported by the UE, the resource information requested by the UE to perform AI and ML services, the QoS information requested by the AF network element to perform AI and ML services, the resource information requested by the AF network element to perform AI and ML services, and the network's service traffic information. This will not be repeated here.

[0057] Step S320: Based on the AMBR for AI and ML services, monitor the traffic of AI and ML services using application-related information in the data packets.

[0058] The UPF network element can receive QoS parameters for AI and ML services sent by the PCF network element via the SMF network element. These QoS parameters for AI and ML services include the AMBR for AI and ML services. When the UE is performing AI and ML services, the UPF network element can monitor the traffic of AI and ML services based on the AMBR for AI and ML services and application-related information in the data packets.

[0059] The application-related information in the data packet includes the application identifier, DNN, APN, and one or more pieces of information from the 5-tuple. This application-related information can be used to identify the service applications performed by the UE. Furthermore, based on the AMBR for AI and ML services, monitoring the traffic of AI and ML services using the application-related information in the data packet can include: determining the traffic of AI and ML services based on the application-related information in the data packet when the UE performs AI and ML services, and monitoring the traffic of AI and ML services using the AMBR for AI and ML services.

[0060] UPF network elements can identify AI and ML services performed by the UE based on application-related information in data packets, and count the traffic used for AI and ML services. Furthermore, UPF network elements can utilize AMBRs (Advanced Management Base) for AI and ML services to monitor the traffic used for these services.

[0061] In some embodiments of this disclosure, the resource monitoring method may further include: if the UPF network element detects that the traffic of AI and ML services exceeds the AMBR for AI and ML services, a monitoring event is triggered and reported to the AF network element via the NEF network element, so that the AF network element determines network resource adjustment information based on the reported monitoring event, sends the network resource adjustment information to the PCF network element via the NEF network element, and then the PCF network element adjusts the QoS parameters of AI and ML services based on the network resource adjustment information.

[0062] If the UPF network element determines that the traffic used for AI and ML services exceeds the AMBR (Advanced Management Flow Rate) for AI and ML services, it triggers a monitoring event report. This monitoring event can be reported to the AF (Agency Flow Rate) network element via the NEF (Network Flow Rate) network element. After receiving the monitoring event, the AF network element can determine the network resource adjustment information and then send the network resource adjustment information to the PCF (Public Flow Rate) network element via the NEF network element. In this way, the PCF network element can adjust the QoS parameters of AI and ML services according to the network resource adjustment information.

[0063] Based on the same inventive concept, Figure 1 Under the communication system architecture shown, this disclosure provides a resource monitoring method, which can be applied to, but is not limited to, [various applications]. Figure 1 The AF network element shown can, in principle, be executed by any electronic device with computing capabilities.

[0064] Figure 4 A flowchart of a resource monitoring method applied to an AF network element is shown in an embodiment of this disclosure, as follows: Figure 4 As shown, the method may include the following steps.

[0065] Step S410: Receive monitoring events reported by the NEF network element, wherein the monitoring events are triggered by the UPF network element detecting that the traffic of AI and ML services exceeds the AMBR for AI and ML services, and the AMBR for AI and ML services is the information in the QoS parameters of AI and ML services.

[0066] The PCF network element can determine the QoS parameters of the AI ​​and ML services based on one or more of the following: UE subscription information, UE-reported capability information, resource information requested by the UE to perform AI and ML services, QoS information requested by the AF network element to perform AI and ML services, and network service traffic information. These QoS parameters include the AMBR (Advanced Management Principle) for the AI ​​and ML services. After determining the QoS parameters, the PCF network element can send these parameters to the UPF network element via the SMF network element. The UPF network element can then monitor the traffic used for the AI ​​and ML services based on the AMBR within the QoS parameters. If the UPF network element determines that the traffic used for the AI ​​and ML services exceeds the AMBR, it triggers a monitoring event report. This monitoring event can be reported to the AF network element via the NEF network element, meaning the AF network element receives the monitoring event reported by the NEF network element.

[0067] Step S420: Based on the reported monitoring events, determine network resource adjustment information. Specifically, the AF network element can reduce its demand for network resources, thereby reducing the need to guarantee network QoS.

[0068] Step S430: Send the network resource adjustment information to the PCF network element via the NEF network element, so that the PCF network element can adjust the QoS parameters of AI and ML services according to the network resource adjustment information.

[0069] The AMF network element can send network resource adjustment information to the NEF network element, which then sends it to the PCF network element. In this way, the PCF network element can adjust the QoS parameters of AI and ML services according to the network resource adjustment information.

[0070] The resource monitoring method provided in this disclosure will be described below through specific embodiments. Figure 5 A flowchart of a resource monitoring method according to an embodiment of this disclosure is shown, such as... Figure 5 As shown, the method may include the following steps.

[0071] In step S510, the PCF network element determines the QoS parameters of the AI ​​and ML services based on one or more of the following information: the UE subscription information, the capability information reported by the UE, the resource information requested by the UE to perform the AI ​​and ML services, the QoS information requested by the AF network element to perform the AI ​​and ML services, the resource information requested by the AF network element to perform the AI ​​and ML services, and the network service traffic information. The QoS parameters of the AI ​​and ML services include the AMBR for the AI ​​and ML services.

[0072] The PCF network element determines the QoS parameters for AI and ML services, whereby the QoS parameters for AI and ML services include the AMBR for AI and ML services.

[0073] In step S520, the SMF network element obtains the QoS parameters of AI and ML services from the PCF network element.

[0074] In step S530, the SMF network element sends the QoS parameters of AI and ML services to the UPF network element.

[0075] In step S540, when the UE performs AI and ML services, the UPF network element counts the traffic used for AI and ML services by using application-related information of data packets, and monitors the traffic used for AI and ML services according to the AMBR for AI and ML services in the QoS parameters of AI and ML services. The application-related information of the data packets includes one or more of the following: application identifier, DNN, APN, and 5-tuple information.

[0076] Step S550: If the UPF network element detects that the traffic used for AI and ML services exceeds the AMBR for AI and ML services, it triggers a monitoring event report and reports the monitoring event to the AF network element via the NEF network element.

[0077] In step S560, after receiving the monitoring event, the AF network element determines the network resource adjustment information, that is, the AF network element can adjust the network resource requirements of AI and ML services, specifically reducing the network resource requirements, thereby reducing the requirements for network QoS guarantees.

[0078] In step S570, the AF network element sends the network resource adjustment information to the PCF network element via the NEF network element.

[0079] In step S580, the PCF network element can adjust the QoS parameters of AI and ML services based on the network resource adjustment information. Specifically, when determining the QoS parameters of AI and ML services, the PCF network element can refer to the network resource adjustment information sent by the AF network element.

[0080] The resource monitoring method provided in this disclosure determines the QoS parameters of AI and ML services through the PCF network element. The QoS parameters of AI and ML services include the AMBR specific to AI and ML services. The QoS parameters of AI and ML services are sent to the UPF network element via the SMF network element. When the UE performs AI and ML services, the UPF network element can monitor the traffic used for AI and ML services based on the AMBR specific to AI and ML services in the QoS parameters. Therefore, the resource monitoring method provided in this disclosure does not require adding additional network elements or interfaces between network elements in the 5GS. It enhances the functionality of the PCF network element by determining the QoS parameters of AI and ML services through the PCF network element, and also enhances the functionality of the UPF network element by monitoring the traffic used for AI and ML services based on the AMBR specific to AI and ML services through the UPF network element. This enables monitoring of network resource usage for AI and ML services in the 5GS, preventing AI and ML services from consuming excessive network resources.

[0081] Furthermore, PCF network elements can combine actual application scenarios and utilize one or more of the following to determine the QoS parameters of AI and ML services: UE subscription information, UE-reported capability information, resource information requested by the UE to execute AI and ML services, QoS information requested by the AF network element to execute AI and ML services, resource information requested by the AF network element to execute AI and ML services, and network service traffic information. This ensures that the determined QoS parameters of AI and ML services correspond to the actual application scenarios. Consequently, when UPF network elements use the AMBR for AI and ML services in the QoS parameters of AI and ML services to monitor the traffic used for AI and ML services, monitoring efficiency can be improved, and the situation where AI and ML services consume excessive network resources can be avoided.

[0082] Furthermore, if the UPF network element detects that the traffic used for AI and ML services exceeds the AMBR for AI and ML services, it can trigger a monitoring event to be reported to the AF network element via the NEF network element. Then, the AF network element can determine network resource adjustment information based on the monitoring event and send the network resource adjustment information to the PAC network element via the NEF network element. In this way, the PCF network element can adjust the QoS parameters of AI and ML services based on the network resource adjustment information sent by the AF network element. Subsequently, the SMF network element can notify the UPF network element of the adjusted QoS parameters of AI and ML services, which can support flexible adjustment of QoS parameters according to service requirements.

[0083] Based on the same inventive concept, this disclosure provides a PCF network element, as described in the following embodiments. Since the principle by which the PCF network element embodiment solves the problem is similar to that of the above method embodiment, the implementation of the PCF network element embodiment can refer to the implementation of the above method embodiment, and repeated details will not be elaborated further.

[0084] Figure 6 A schematic diagram of the structure of a PCF network element in an embodiment of this disclosure is shown below, such as... Figure 6 As shown, the PCF network element 600 may include a parameter determination unit 610 and a parameter transmission unit 620.

[0085] The parameter determination unit 610 can be used to determine the QoS parameters of AI and ML services, which include the AMBR for AI and ML services. The parameter sending unit 620 can be used to send the QoS parameters of AI and ML services to the UPF network element via the SMF network element, so that the UPF network element can monitor the traffic of AI and ML services according to the AMBR for AI and ML services. The QoS parameters of AI and ML services are determined by the PCF network element based on at least one of the following: UE subscription information participating in AI and ML services, UE-reported capability information, resource information requested by the UE to perform AI and ML services, QoS information requested by the AF network element to perform AI and ML services, resource information requested by the AF network element to perform AI and ML services, and network service traffic information.

[0086] In some embodiments of this disclosure, the PCF network element 600 may further include a parameter adjustment unit 630. The parameter adjustment unit 630 is configured to: receive network resource adjustment information sent by the AF network element via the NEF network element; and adjust the QoS parameters of AI and ML services according to the network resource adjustment information. The network resource adjustment information is determined by the AF network element based on reported monitoring events. These monitoring events are triggered when the UPF network element detects that the traffic of AI and ML services exceeds the AMBR for AI and ML services, and are reported to the AF network element via the NEF network element.

[0087] Based on the same inventive concept, this disclosure provides a UPF network element, as described in the following embodiments. Since the principle by which the UPF network element embodiment solves the problem is similar to that of the above method embodiment, the implementation of the UPF network element embodiment can refer to the implementation of the above method embodiment, and repeated details will not be elaborated further.

[0088] Figure 7 A schematic diagram of the structure of a UPF network element in an embodiment of this disclosure is shown below. Figure 7 As shown, the UPF network element 700 may include a parameter receiving unit 710 and a monitoring unit 720.

[0089] The parameter receiving unit 710 can be used to: receive QoS parameters for AI and ML services sent by the PCF network element via the SMF network element, wherein the QoS parameters for AI and ML services include the AMBR for AI and ML services. The monitoring unit 702 can be used to: monitor the traffic of AI and ML services based on the AMBR for AI and ML services and application-related information of data packets.

[0090] In some embodiments of this disclosure, the UPF network element 700 may further include an event reporting unit 730. The event reporting unit 730 can be used to: if the traffic of AI and ML services is detected to exceed the AMBR for AI and ML services, trigger a monitoring event to be reported to the AF network element via the NEF network element, so that the AF network element determines network resource adjustment information based on the reported monitoring event, sends the network resource adjustment information to the PCF network element via the NEF network element, and then the PCF network element adjusts the QoS parameters of the AI ​​and ML services according to the network resource adjustment information.

[0091] In some embodiments of this disclosure, the application-related information of the data packet includes at least one of the following: application identifier, data network name (DNN), access point name (APN), and a 5-tuple. The monitoring unit 702 can also be used to: determine the traffic of AI and ML services based on the application-related information of the data packet when the UE is performing AI and ML services, and monitor the traffic of AI and ML services using an AMBR for AI and ML services.

[0092] Based on the same inventive concept, this disclosure provides an AF network element, as described in the following embodiments. Since the principle by which the AF network element embodiment solves the problem is similar to that of the above method embodiment, the implementation of the AF network element embodiment can refer to the implementation of the above method embodiment, and repeated details will not be elaborated further.

[0093] Figure 8 A schematic diagram of the structure of an AF network element in an embodiment of this disclosure is shown below, such as... Figure 8 As shown, the AF network element 800 may include: an event receiving unit 810, an information adjustment unit 820, and an information sending unit 830.

[0094] The event receiving unit 810 can be used to receive monitoring events reported via the NEF network element. These monitoring events are triggered by the UPF network element detecting that the traffic of AI and ML services exceeds the AMBR (Advanced Traffic Flow Rate) for AI and ML services. The AMBR for AI and ML services is information from the QoS parameters of the AI ​​and ML services. The information adjustment unit 820 can be used to determine network resource adjustment information based on the reported monitoring events. The information sending unit 830 can be used to send the network resource adjustment information to the PCF network element via the NEF network element, enabling the PCF network element to adjust the QoS parameters of the AI ​​and ML services according to the network resource adjustment information.

[0095] Figure 9 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. Referring below... Figure 9 To describe an electronic device 900 according to this embodiment of the present invention. Figure 9 The electronic device 900 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0096] like Figure 9 As shown, the electronic device 900 is presented in the form of a general-purpose computing device. The components of the electronic device 900 may include, but are not limited to: at least one processing unit 910, at least one storage unit 920, a bus 930 connecting different system components (including storage unit 920 and processing unit 910), and a display unit 940.

[0097] The storage unit stores program code that can be executed by the processing unit 910, causing the processing unit 910 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. The storage unit 920 may include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 9201 and / or a cache memory 9202, and may further include a read-only memory (ROM) 9203. The storage unit 920 may also include a program / utility 9204 having a set (at least one) of program modules 9205, including but not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0098] Bus 930 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0099] Electronic device 900 can also communicate with one or more external devices 970 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 900, and / or with any device that enables electronic device 900 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 950. Furthermore, electronic device 900 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 960. As shown, network adapter 960 communicates with other modules of electronic device 900 via bus 930. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0100] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section of this specification.

[0101] When the electronic device 900 provided in this embodiment is a PCF network element, the processing unit 910 can perform the following steps in the above embodiment: determine the QoS parameters of AI and ML services, the QoS parameters of AI and ML services include AMBR for AI and ML services; send the QoS parameters of AI and ML services to the UPF network element via the SMF network element, so that the UPF network element monitors the traffic of AI and ML services according to the AMBR for AI and ML services.

[0102] When the electronic device 900 provided in this embodiment is a UPF network element, the processing unit 910 can perform the following steps in the above embodiment: receive QoS parameters of AI and ML services issued by the PCF network element via the SMF network element, wherein the QoS parameters of AI and ML services include AMBR for AI and ML services; and monitor the traffic of AI and ML services based on the AMBR for AI and ML services and application-related information of data packets.

[0103] When the electronic device 900 provided in this embodiment is an AF network element, the processing unit 910 can perform the following steps in the above embodiment: receiving a monitoring event reported by the NEF network element, wherein the monitoring event is triggered by the UPF network element detecting that the traffic of AI and ML services exceeds the AMBR for AI and ML services, and the AMBR for AI and ML services is information in the QoS parameters of AI and ML services; determining network resource adjustment information based on the reported monitoring event; and sending the network resource adjustment information to the PCF network element via the NEF network element, so that the PCF network element adjusts the QoS parameters of AI and ML services according to the network resource adjustment information.

[0104] According to embodiments of the present invention, a program product for implementing the above-described method may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0105] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0106] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0107] The program code contained on the readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, fiber optic, RF, etc., or any suitable combination thereof. The program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages—such as Java, C++, etc.—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0108] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0109] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0110] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0111] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

[0112] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A resource monitoring method, characterized in that, The method is executed by the Policy Control Function (PCF) network element, including: Determine the Quality of Service (QoS) parameters for Artificial Intelligence (AI) and Machine Learning (ML) services, wherein the QoS parameters for AI and ML services include the Aggregated Maximum Bit Rate (AMBR) for AI and ML services; The Session Management Function (SMF) network element sends the QoS parameters of the AI ​​and ML services to the User Plane Function (UPF) network element, enabling the UPF network element to monitor the traffic of the AI ​​and ML services according to the AMBR for the AI ​​and ML services; and The application function (AF) network element receives network resource adjustment information sent by the network open function (NEF) network element. The network resource adjustment information is determined by the AF network element based on reported monitoring events. The monitoring events are triggered by the UPF network element detecting that the traffic of AI and ML services exceeds the AMBR for AI and ML services, and the monitoring events are reported to the AF network element via the NEF network element.

2. The method according to claim 1, characterized in that, The QoS parameters of the AI ​​and ML services are determined by the PCF network element based on at least one of the following: user equipment (UE) subscription information participating in the AI ​​and ML services, capability information reported by the UE, resource information requested by the UE to perform the AI ​​and ML services, QoS information requested by the AF network element to perform the AI ​​and ML services, resource information requested by the AF network element to perform the AI ​​and ML services, and network service traffic information.

3. The method according to claim 1, characterized in that, The method further includes: Based on the network resource adjustment information, the QoS parameters of the AI ​​and ML services are adjusted.

4. A resource monitoring method, characterized in that, The method is executed by a UPF network element and includes: Receive QoS parameters for AI and ML services sent by PCF network element via SMF network element, wherein the QoS parameters for AI and ML services include AMBR for AI and ML services; Based on the AMBR for AI and ML services, traffic for AI and ML services is monitored using application-related information in data packets; and If the traffic of the AI ​​and ML services is detected to exceed the AMBR for the AI ​​and ML services, a monitoring event is triggered and reported to the AF network element via the NEF network element. The AF network element then determines network resource adjustment information based on the reported monitoring event and sends the network resource adjustment information to the PCF network element via the NEF network element.

5. The method according to claim 4, characterized in that, The network resource adjustment information is used by the PCF network element to adjust the QoS parameters of the AI ​​and ML services.

6. The method according to claim 4, characterized in that, The application-related information of the data packet includes at least one of the following: application identifier, data network name (DNN), access point name (APN), and a 5-tuple; The step of monitoring AI and ML service traffic based on the AMBR for AI and ML services and application-related information in data packets includes: When the UE performs AI and ML services, the traffic of AI and ML services is determined based on the application-related information of the data packets, and the traffic of AI and ML services is monitored using the AMBR for AI and ML services.

7. A resource monitoring method, characterized in that, The method is executed by the AF network element and includes: The system receives monitoring events reported by the NEF network element. These monitoring events are triggered when the UPF network element detects that the traffic of AI and ML services exceeds the AMBR for AI and ML services. The AMBR for AI and ML services is information in the QoS parameters of AI and ML services. The QoS parameters for AI and ML services are determined by the PCF network element and sent to the UPF network element via the SMF network element. Based on the reported monitoring events, determine the network resource adjustment information; The network resource adjustment information is sent to the PCF network element via the NEF network element, so that the PCF network element adjusts the QoS parameters of the AI ​​and ML services according to the network resource adjustment information.

8. A PCF network element, characterized in that, include: A parameter determination unit is used to determine the QoS parameters of AI and ML services, wherein the QoS parameters of AI and ML services include AMBR for AI and ML services; The parameter sending unit is used to send the QoS parameters of the AI ​​and ML services to the UPF network element via the SMF network element, so that the UPF network element can monitor the traffic of the AI ​​and ML services according to the AMBR for the AI ​​and ML services. as well as The parameter adjustment unit is used to receive network resource adjustment information sent by the AF network element via the NEF network element. The network resource adjustment information is determined by the AF network element based on reported monitoring events. The monitoring events are triggered by the UPF network element when it detects that the traffic of AI and ML services exceeds the AMBR for AI and ML services, and the monitoring events are reported to the AF network element via the NEF network element.

9. A UPF network element, characterized in that, include: The parameter receiving unit is used to receive QoS parameters of AI and ML services sent by the PCF network element through the SMF network element. The QoS parameters of AI and ML services include AMBR for AI and ML services. The monitoring unit is used to monitor the traffic of AI and ML services based on the AMBR for AI and ML services and application-related information of data packets; and The event reporting unit is used to trigger a monitoring event to be reported to the AF network element via the NEF network element if the traffic of the AI ​​and ML services exceeds the AMBR for the AI ​​and ML services. The AF network element then determines network resource adjustment information based on the reported monitoring event and sends the network resource adjustment information to the PCF network element via the NEF network element.

10. An AF network element, characterized in that, include: The event receiving unit is used to receive monitoring events reported by the NEF network element. The monitoring events are triggered by the UPF network element when it detects that the traffic of AI and ML services exceeds the AMBR for AI and ML services. The AMBR for AI and ML services is the information in the QoS parameters of AI and ML services. The QoS parameters of AI and ML services are determined by the PCF network element and sent to the UPF network element via the SMF network element. The information adjustment unit is used to determine network resource adjustment information based on the reported monitoring events; The information sending unit is used to send the network resource adjustment information to the PCF network element via the NEF network element, so that the PCF network element adjusts the QoS parameters of the AI ​​and ML services according to the network resource adjustment information.

11. An electronic device, characterized in that, include: One or more processors; A storage device configured to store one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

12. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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