CN117119510A - Resource monitoring method and related equipment - Google Patents

Abstract

The embodiment of the disclosure provides a resource monitoring method and related equipment, and relates to the technical field of communication. The resource monitoring method executed by the PCF network element comprises the following steps: determining QoS parameters of AI and ML services, wherein the QoS parameters of the AI and ML services comprise AMBR for the AI and ML services; and transmitting QoS parameters of the AI and ML services to a UPF network element through an SMF network element, so that the UPF network element monitors traffic of the AI and ML services according to the AMBR aiming at the AI and ML services. The method determines the QoS parameters of AMBR for AI and ML services through the PCF network element, and can issue the QoS parameters to the UPF network element through the SMF network element, so as to monitor the traffic of the AI and ML services through the UPF network element, thereby realizing the monitoring of the network resource use condition of the AI and ML services in 5GS, and avoiding the AI and ML services from occupying excessive network resources.

Description

Resource monitoring method and related equipment

Technical Field

The present disclosure relates to the field of communications technologies, and in particular, 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

After implementing centralized network intelligence, 3GPP (3 rd Generation Partnership Project, third generation partnership project) SA2 has opened AI (Artificial Intelligence ) and ML (Machine Learning) projects in the study of 3GPP r18, so that 5GS (5 g system) can support distributed network intelligence and federal Learning.

In the current 3GPP network monitoring method, the network resource use condition of the AI and ML service in 5GS is not monitored, and the condition that the AI and ML service occupy excessive network resources exists to influence other services.

It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

The embodiment of the disclosure provides a resource monitoring method, a PCF network element, a UPF network element, an AF network element, electronic equipment and a computer readable storage medium, which can monitor the network resource use condition of AI and ML services in 5 GS.

Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.

According to one aspect of the present disclosure, there is provided a resource monitoring method, the method being performed by a PCF network element, comprising: determining QoS parameters of AI and ML services, wherein the QoS parameters of the AI and ML services comprise AMBR for the AI and ML services; and transmitting QoS parameters of the AI and ML services to a UPF network element through an SMF network element, so that the UPF network element monitors traffic of the AI and ML services according to the AMBR aiming at the AI and ML services.

In some embodiments of the present disclosure, the QoS parameters of the AI and ML traffic are determined by the PCF network element according to at least one of the following information: the method comprises the steps of User Equipment (UE) subscription information participating in an AI and ML service, capability information reported by the UE, resource information requested by the UE to execute the AI and ML service, quality of service (QoS) information requested by an AF network element to execute the AI and ML service, resource information requested by the AF network element to execute the AI and ML service and service flow information of a network.

In some embodiments of the present disclosure, the method further comprises: receiving network resource adjustment information sent by an AF network element through a NEF network element, wherein the network resource adjustment information is determined by the AF network element according to a reported monitoring event, the monitoring event is triggered to be reported when the UPF network element monitors that the traffic of AI and ML services exceeds the AMBR for the AI and ML services, and the monitoring event is reported to the AF network element through the NEF network element; and adjusting QoS parameters of the AI and ML services according to the network resource adjustment information.

According to yet another aspect of the present disclosure, there is provided a resource monitoring method, the method being performed by a UPF network element, comprising: receiving QoS parameters of AI and ML services issued by a PCF network element through an SMF network element, wherein the QoS parameters of the AI and ML services comprise AMBR for the AI and ML services; and monitoring the traffic of the AI and ML services through the application related information of the data packet according to the AMBR aiming at the AI and ML services.

In some embodiments of the present disclosure, the method further comprises: if the traffic of the AI and ML service is monitored to exceed the AMBR for the AI and ML service, triggering a monitoring event to be reported to an AF network element through a NEF network element, so that the AF network element determines network resource adjustment information according to the reported monitoring event, and sends the network resource adjustment information to the PCF network element through the NEF network element, and then the PCF network element adjusts QoS parameters of the AI and ML service according to the network resource adjustment information.

In some embodiments of the present disclosure, the application related information of the data packet includes at least one of the following information: application identity, DNN, APN, and quintuple; wherein, the monitoring the traffic of the AI and ML services according to the AMBR for the AI and ML services through the application related information of the data packet includes: and when the UE performs the AI and ML services, determining the traffic of the AI and ML services according to the application related information of the data packet, and monitoring the traffic of the AI and ML services by using the AMBR for the AI and ML services.

According to yet another aspect of the present disclosure, there is provided a resource monitoring method, the method being performed by an AF network element, comprising: receiving a monitoring event reported by an NEF network element, wherein the monitoring event is triggered to report when the UPF network element monitors that the traffic of the AI and ML service exceeds the AMBR aiming at the AI and ML service, and the AMBR aiming at the AI and ML service is information in QoS parameters of the AI and ML service; according to the reported monitoring event, determining network resource adjustment information; and sending the network resource adjustment information to a PCF (physical state communication) network element through the NEF network element, so that the PCF network element adjusts QoS parameters of the AI and ML services according to the network resource adjustment information.

According to yet another aspect of the present disclosure, there is provided a PCF network element, comprising: a parameter determining unit for determining QoS parameters of AI and ML services, wherein the QoS parameters of the AI and ML services comprise AMBR for the AI and ML services; and the parameter sending unit is used for sending QoS parameters of the AI and ML services to a UPF network element through an SMF network element, so that the UPF network element monitors the traffic of the AI and ML services according to the AMBR aiming at the AI and ML services.

According to yet another aspect of the present disclosure, there is provided a UPF network element, including: 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, where the QoS parameters of the AI and ML services include AMBR for the AI and ML services; and the monitoring unit is used for monitoring the traffic of the AI and the ML service through the application related information of the data packet according to the AMBR aiming at the AI and the ML service.

According to yet another aspect of the present disclosure, there is provided an AF network element, including: an event receiving unit, configured to receive a monitoring event reported by a NEF network element, where the monitoring event is triggered and reported when a UPF network element monitors that traffic of an AI and ML service exceeds an AMBR for the AI and ML service, and the AMBR for the AI and ML service is information in QoS parameters of the AI and ML service; the information adjustment unit is used for determining network resource adjustment information according to the reported monitoring event; and the information sending unit is used for sending the network resource adjustment information to the PCF network element through the NEF network element, so that the PCF network element adjusts QoS parameters of the AI and ML services according to the network resource adjustment information.

According to still another aspect of the present disclosure, there is provided an electronic apparatus including: one or more processors; and a storage configured to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method as described in the above embodiments.

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

According to the resource monitoring method provided by the embodiment of the disclosure, the QoS parameters of the AI and ML services are determined through the PCF network element, wherein the QoS parameters of the AI and ML services comprise AMBR aiming at the AI and ML services; sending QoS parameters of AI and ML business to UPF network element via SMF network element; when the UE performs the AI and ML traffic, the UPF network element may monitor traffic for the AI and ML traffic according to AMBR for the AI and ML traffic among QoS parameters of the AI and ML traffic. Therefore, the resource monitoring method provided by the embodiment of the disclosure does not need to add additional network elements in the 5GS or newly add interfaces between network elements, can enhance the functions of PCF network elements, determine QoS parameters of AI and ML services through the PCF network elements, and enhance the functions of UPF network elements, and monitor traffic for AI and ML services according to AMBR for the AI and ML services through the UPF network elements, thereby realizing monitoring of network resource use conditions of AI and ML services in the 5GS and avoiding the situation that the AI and ML services occupy excessive network resources.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure and do not constitute an undue limitation on the disclosure.

Fig. 1 illustrates an architectural diagram of a communication system in an embodiment of the present disclosure;

fig. 2 shows a flowchart of a resource monitoring method applied to a PCF network element in an embodiment of the disclosure;

fig. 3 is a flowchart illustrating a resource monitoring method applied to a UPF network element in an embodiment of the present disclosure;

fig. 4 is a flowchart illustrating a resource monitoring method applied to an AF network element in an embodiment of the present disclosure;

FIG. 5 illustrates a flow chart of a method of resource monitoring in an embodiment of the present disclosure;

fig. 6 shows a schematic structural diagram of a PCF network element in an embodiment of the disclosure;

fig. 7 is a schematic structural diagram of a UPF network element in an embodiment of the disclosure;

fig. 8 is a schematic structural diagram of an AF network element in an embodiment of the present disclosure;

fig. 9 shows a block diagram of an electronic device in an embodiment of the disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example 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.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.

Fig. 1 illustrates an architecture diagram of a communication system in an embodiment of the disclosure, where the communication system may support the resource monitoring method provided by the embodiment of the disclosure. As shown in fig. 1, the communication system may include: UE (User Equipment), RAN (Radio Access Network ) Equipment, AMF (Access and Mobility Mangement Function, access and mobility management function element) network elements, SMF (Session Management function ) network elements, UPF (User Plane Function, user plane function) network elements, PCF (Policy Control Function ) network elements, NEF (Network Exposure Function, network opening function) network elements, and AF (Application Function ) network elements.

The UE may be a variety of electronic devices including, but not limited to, smartphones, tablets, laptop portable computers, desktop computers, wearable devices, augmented reality devices, virtual reality devices, and the like. Alternatively, the clients of the applications installed in different UEs are the same, or clients of the same type of application based on different operating systems. The specific form of the application client may also be different based on the different terminal platforms, for example, the application client may be a mobile phone client, a PC client, etc.

The RAN equipment may include equipment in an access network that communicates over the air-interface, through one or more sectors, with wireless terminals. The UE may access the AMF network element through the RAN device. Specifically, when the UE accesses the AMF network element through the RAN device, the UE may access the AMF network element through a network-side device such as a base station of a 5G or later version (e.g., a 5G NR NB), or a base station in another communication system (e.g., an eNB base station). The AMF network element is mainly used for mobility management, access authentication/authorization for the UE and is also responsible for transferring user strategies between the UE and the PCF network element. The SMF network element is mainly used for session management, internet protocol address allocation and management of UE, terminal node of selecting manageable user plane function, strategy control or charging function interface, downlink data notification and the like. The PCF network element is used for guiding a unified policy framework of network behavior, and provides policy rule information for control plane function network elements (such as AMF network elements, SMF network elements), and the like. The UPF network element may be used for packet routing and forwarding, qoS handling of user plane data, etc. User data can be accessed to a DN (data network) through the network element. The NEF network element is located between the 5G core network and the external third party application function, and there may be some internal application functions that are responsible for managing the external open network data, and all external applications want to access the internal data of the 5G core network, and need to pass through the NEF network element. The NEF network element provides corresponding security assurance to ensure the security of external application to the network, and provides the functions of external application Qos customization capability opening, mobility state event subscription, application function request distribution and the like. The AF network element is mainly used for transmitting the demands of the application side on the network side, such as QoS demands and user state event subscription. The AF network element may be a third party functional entity, or may be an application service deployed by an operator. For the application function entity of the third party application, when the application function entity interacts with the core network, authorization processing can be performed through the NEF network element, for example, the third party application function directly sends a request to the NEF network element, the NEF network element judges whether the AF network element is allowed to send the request, and if the AF network element passes the verification, the request is forwarded to the corresponding PCF network element or the UDM network element.

Those skilled in the art will appreciate that 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 in fig. 1 is merely illustrative, and any number of network elements and terminals may be provided according to actual needs. The embodiments of the present disclosure are not limited in this regard.

Under the communication system architecture shown in fig. 1, qoS (Quality of Service ) parameters of AI and ML services are determined by the PCF network element, and include AMBR (Aggregate Maximum Bit Rate ) for the AI and ML services, which can be sent to the UPF network element via the SMF network element. Therefore, when the UE performs the AI and ML services, the UPF network element can monitor the traffic for the A and ML services according to the AMBR for the AI and ML services in the QoS parameters of the AI and ML services, thereby realizing the monitoring of the network resource use condition of the AI and ML services in 5GS and avoiding the situation that the AI and ML services occupy excessive network resources.

When in implementation, the method mainly comprises the following steps: (1) The PCF network element determines QoS parameters of AI and ML services, wherein the QoS parameters of the AI and ML services comprise AMBR aiming at the AI and ML services, and the QoS parameters of the AI and ML services are sent to the SMF network element; (2) The SMF network element sends QoS parameters comprising the AI and ML service to the UPF network element, so that when the UE performs the AI and ML service, the UPF network element can monitor the traffic for the AI and ML service by using the AMBR for the AI and ML service in the parameters; (3) Triggering a monitoring event to report if the UPF network element monitors that the traffic for the AI and ML services exceeds the AMBR for the AI and ML services; (4) Reporting the reported monitoring event to an AF network element through a NEF network element; (5) The AF network element adjusts and reduces the requirement on network resources after receiving the reported monitoring event, determines 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 QoS parameters of the AI and ML services.

Under the architecture of the communication system shown in fig. 1, the embodiment of the present disclosure provides a resource monitoring method, which can be applied to, but not limited to, the PCF network element shown in fig. 1, and in principle, the method can be executed by any electronic device with computing processing capability.

Fig. 2 shows a flowchart of a resource monitoring method applied to a PCF network element in an embodiment of the disclosure, as shown in fig. 2, the method may include the following steps.

Step S210: qoS parameters for AI and ML traffic are determined, wherein the QoS parameters for the AI and ML traffic comprise AMBR for the AI and ML traffic.

QoS refers to a network that can provide better service capability for specified network communication by using various basic technologies, and is a security mechanism of the network, which is a technology for solving the problems of network delay and blocking. QoS is not required if the network is only used for a specific time-free application system, such as a Web application, but is necessary for critical applications and multimedia applications. QoS ensures that important traffic is not delayed or dropped when the network is overloaded or congested, while ensuring efficient operation of the network.

AMBR defines the upper limit of the sum of the bit rates of all GBR (non-Guaranteed Bit Rate ) bearers of one UE. AMBR for AI and ML traffic may be used to define the maximum transmission rates of AI and ML traffic.

In addition, qoS parameters for AI and ML traffic may also include, but are not limited to: ARP (Allocation and Retension Priority, allocation and reservation priority) for resource allocation, priority handling level for traffic data flows, average window, maximum data burst, uplink guaranteed bandwidth, downlink guaranteed bandwidth, uplink maximum bandwidth, downlink maximum packet loss, uplink maximum packet loss, message delay budget, and packet error rate.

In some embodiments of the present disclosure, the QoS parameters of the AI and ML traffic are determined by the PCF network element based on at least one of the following information: the method comprises the steps of User Equipment (UE) subscription information participating in an AI and ML service, capability information reported by the UE, resource information requested by the UE to execute the AI and ML service, quality of service (QoS) information requested by an AF network element to execute the AI and ML service, resource information requested by the AF network element to execute the AI and ML service and service flow information of a network.

The UE subscription information participating in the AI and ML services refers to subscription information of UEs capable of performing the AI and ML services. The capability information reported by the UE may include, but is not limited to, wireless/network capabilities, intelligent capabilities, computing capabilities. The resource information requested by the UE to perform the AI and ML services refers to 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 refers to QoS requested by the AF network element to perform the AI and ML services. The resource information requested by the AF network element to execute the AI and ML services refers to the resource information requested by the AF network element to execute the AI and ML services. Traffic information for the network 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 (administration and management).

Step S220: and transmitting QoS parameters of the AI and ML services to the UPF network element via the SMF network element, so that the UPF network element monitors traffic of the AI and ML services according to AMBR for the AI and ML services.

In the embodiment of the disclosure, the PCF network element may determine QoS parameters of the AI and ML services by using one or more of UE subscription information participating in the AI and ML services, capability information reported by the UE, resource information requested by the UE to execute the AI and ML services, qoS information requested by the AF network element to execute the AI and ML services, resource information requested by the AF network element to execute the AI and ML services, and service flow information of the network in combination with an actual application scenario, where the QoS parameters of the AI and ML services include AMBR for the AI and ML services, and the QoS parameters of the AI and ML services are sent to the UPF network element via the SMF network element. Therefore, when the UE performs the AI and ML services, the UPF network element can monitor the traffic for the AI and ML services according to the AMBR for the AI and ML services, thereby realizing the monitoring of the network resource use condition of the AI and ML services in 5GS and avoiding the situation that the AI and ML services occupy excessive network resources.

In some embodiments of the present disclosure, the resource monitoring method may further include: the PCF network element receives network resource adjustment information sent by the AF network element through the NEF network element; and adjusting QoS parameters of the AI and ML services according to the network resource adjustment information. The network resource adjustment information is determined by the AF network element according to the reported monitoring event, the monitoring event is triggered and reported under the condition that the UPF network element monitors that the traffic of the AI and ML service exceeds the AMBR aiming at the AI and ML service, and the monitoring event is reported to the AF network element through the NEF network element.

The UPF network element acquires QoS parameters of AI and ML services sent by the PCF network element via the SMF network element, wherein the QoS parameters of the AI and ML services comprise AMBR for the AI and ML services. When the UE performs the AI and ML services, the UPF network element may monitor traffic for the AI and ML services using AMBR for the AI and ML services according to application related information of the data packet. Wherein, the application related information of the data packet can be used for identifying the service application performed by the UE. The application related information of the data packet includes one or more of application identification, DNN (Data Network Name ), APN (Access Point Name, access point name), and five-tuple information. The UPF network element can identify AI and ML services performed by the UE according to the application related information of the data packet, and count the traffic for performing the AI and ML services. If the UPF network element judges that the traffic for carrying out AI and ML services exceeds the AMBR for AI and ML services, the monitoring event reporting is triggered, and the monitoring event can be reported to the AF network element through the NEF network element.

After receiving the monitoring event, the AF network element can determine the network resource adjustment information, and then the network resource adjustment information is sent to the PCF network element through the NEF network element, so that the PCF network element can adjust QoS parameters of the AI and ML services according to the network resource adjustment information. Specifically, the network resource adjustment information may reduce the requirement for network resources, thereby reducing the guarantee requirement for network QoS.

In the embodiment of the disclosure, when the UPF network element monitors that the traffic for the AI and ML services exceeds the AMBR for the AI and ML services, the monitoring event may be triggered to be reported to the AF network element via the NEF network element, then the AF network element may determine network resource adjustment information according to the monitoring event, and send the network resource adjustment information to the PAC network element via the NEF network element, so that the PCF network element may adjust QoS parameters of the AI and ML services according to the network resource adjustment information sent by the AF network element, and further the SMF network element may notify the QoS parameters of the AI and ML services adjusted by the UPF network element, and may support flexible adjustment of QoS parameters according to service requirements.

Based on the same inventive concept, under the communication system architecture shown in fig. 1, the embodiments of the present disclosure provide a resource monitoring method, which can be applied to, but not limited to, the UPF network element shown in fig. 1, and in principle, the method can be executed by any electronic device with computing processing capability.

Fig. 3 shows a flowchart of a resource monitoring method applied to a UPF network element in an embodiment of the present disclosure, and as shown in fig. 3, the method may include the following steps.

Step S310: and receiving QoS parameters of the AI and ML services issued by the PCF network element through the SMF network element, wherein the QoS parameters of the AI and ML services comprise AMBR for the AI and ML services.

The QoS parameters of the AI and ML services have been described above, and are determined by the PCF network element according to one or more of the UE subscription information participating in the AI and ML services, 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 traffic information of the network, which are not described herein.

Step S320: and monitoring the traffic of the AI and ML services through application related information of the data packet according to the AMBR for the AI and ML services.

The UPF network element may receive QoS parameters of AI and ML traffic sent by the PCF network element via the SMF network element, the QoS parameters of the AI and ML traffic including AMBR for the AI and ML traffic. When the UE performs the AI and ML services, the UPF network element may monitor traffic of the AI and ML services according to AMBR for the AI and ML services through application related information of the data packet.

The application related information of the data packet comprises one or more of application identification, DNN, APN and quintuple. The application related information of the data packet may be used to identify the service application performed by the UE. Further, according to AMBR for AI and ML services, monitoring traffic of AI and ML services through application related information of data packets may include: when the UE performs the AI and ML services, traffic of the AI and ML services is determined according to application related information of the data packet, and monitored by AMBR for the AI and ML services.

The UPF network element can identify AI and ML services performed by the UE according to the application related information of the data packet, and count the traffic for performing the AI and ML services. And, the UPF network element may monitor traffic for AI and ML traffic using AMBR for AI and ML traffic.

In some embodiments of the present disclosure, the resource monitoring method may further include: if the UPF network element monitors that the traffic of the AI and ML service exceeds the AMBR aiming at the AI and ML service, triggering a monitoring event to be reported to the AF network element through the NEF network element, so that the AF network element determines network resource adjustment information according to the reported monitoring event, and sends the network resource adjustment information to the PCF network element through the NEF network element, and then the PCF network element adjusts QoS parameters of the AI and ML service according to the network resource adjustment information.

If the UPF network element judges that the traffic for carrying out AI and ML services exceeds the AMBR for AI and ML services, the monitoring event reporting is triggered, and the monitoring event can be reported to the AF network element through the NEF network element. After receiving the monitoring event, the AF network element can determine the network resource adjustment information, and then the network resource adjustment information is sent to the PCF network element through the NEF network element, so that the PCF network element can adjust QoS parameters of the AI and ML services according to the network resource adjustment information.

Based on the same inventive concept, under the communication system architecture shown in fig. 1, the embodiments of the present disclosure provide a resource monitoring method, which can be applied to, but not limited to, the AF network element shown in fig. 1, and in principle, the method can be executed by any electronic device having computing processing capability.

Fig. 4 shows a flowchart of a resource monitoring method applied to an AF network element in an embodiment of the present disclosure, and as shown in fig. 4, the method may include the following steps.

Step S410: and receiving a monitoring event reported by the NEF network element, wherein the monitoring event is information in QoS parameters of the AI and the ML service when the UPF network element monitors that the traffic of the AI and the ML service exceeds the AMBR for the AI and the ML service and the AMBR for the AI and the ML service is triggered and reported.

The PCF network element may determine QoS parameters of the AI and ML services according to one or more of 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 traffic flow information of the network, and the QoS parameters of the AI and ML services include AMBR for the AI and ML services. After determining the QoS parameters of the AI and ML services, the PCF network element may 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 may monitor traffic for the AI and ML services according to AMBR for the AI and ML services in the QoS parameters of the AI and ML services. If the UPF network element judges that the traffic for carrying out AI and ML services exceeds AMBR for AI and ML services, the monitoring event reporting is triggered, and the monitoring event can be reported to the AF network element through the NEF network element, namely the AF network element receives the monitoring event reported through the NEF network element.

Step S420: and determining network resource adjustment information according to the reported monitoring event. Specifically, the AF network element may reduce the demands on network resources, thereby reducing the guarantee demands on network QoS.

Step S430: and the network resource adjustment information is sent to the PCF network element through the NEF network element, so that the PCF network element adjusts QoS parameters of the AI and ML services according to the network resource adjustment information.

The AMF network element may send the network resource adjustment information to the NEF network element, and then send the network resource adjustment information to the PCF network element through the NEF network element, so that the PCF network element may adjust QoS parameters of the AI and ML services according to the network resource adjustment information.

The resource monitoring method provided by the embodiment of the disclosure is described below through specific embodiments. Fig. 5 shows a flowchart of a resource monitoring method in an embodiment of the present disclosure, which may include the following steps, as shown in fig. 5.

In step S510, the PCF network element determines QoS parameters of the AI and ML services according to one or more of the UE subscription information participating in the AI and ML services, capability information reported by the UE, resource information requested by the UE to execute the AI and ML services, qoS information requested by the AF network element to execute the AI and ML services, resource information requested by the AF network element to execute the AI and ML services, and service traffic information of the network, wherein the QoS parameters of the AI and ML services include AMBR for the AI and ML services.

The PCF network element determines QoS parameters of AI and ML traffic, wherein the QoS parameters of the AI and ML traffic include AMBR for the AI and ML traffic

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

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

In step S540, when the UE performs the AI and ML services, the UPF network element counts the traffic for the AI and ML services through the application related information of the data packet, and monitors the traffic for the AI and ML services according to AMBR for the AI and ML services in QoS parameters of the AI and ML services. The application related information of the data packet comprises one or more of application identification, DNN, APN and quintuple information.

Step S550, if the UPF network element monitors that the traffic for the AI and ML service exceeds the AMBR for the AI and ML service, the monitoring event is triggered to report, and the monitoring event is reported to the AF network element through the NEF network element.

In step S560, after the AF network element receives the monitoring event, the AF network element determines network resource adjustment information, that is, the AF network element may adjust the requirements of AI and ML services for network resources, specifically may reduce the requirements for network resources, so as to reduce the requirements for guaranteeing network QoS.

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

In step S580, the PCF network element may adjust QoS parameters of the AI and ML services according to the network resource adjustment information. Specifically, when the PCF network element determines QoS parameters of the AI and ML services later, the PCF network element may refer to the network resource adjustment information sent by the AF network element.

According to the resource monitoring method provided by the embodiment of the disclosure, the QoS parameters of the AI and ML services are determined through the PCF network element, wherein the QoS parameters of the AI and ML services comprise AMBR aiming at the AI and ML services; sending QoS parameters of AI and ML business to UPF network element via SMF network element; when the UE performs the AI and ML traffic, the UPF network element may monitor traffic for the AI and ML traffic according to AMBR for the AI and ML traffic among QoS parameters of the AI and ML traffic. Therefore, the resource monitoring method provided by the embodiment of the disclosure does not need to add additional network elements in the 5GS or newly add interfaces between network elements, can enhance the functions of PCF network elements, determine QoS parameters of AI and ML services through the PCF network elements, and enhance the functions of UPF network elements, and monitor traffic for AI and ML services according to AMBR for the AI and ML services through the UPF network elements, thereby realizing monitoring of network resource use conditions of AI and ML services in the 5GS and avoiding the situation that the AI and ML services occupy excessive network resources.

Further, the PCF network element may determine QoS parameters of the AI and ML services by using one or more of UE subscription information participating in the AI and ML services, capability information reported by the UE, resource information requested by the UE to execute the AI and ML services, qoS information requested by the AF network element to execute the AI and ML services, and service traffic information of the network, so that the determined QoS parameters of the AI and ML services correspond to the actual application scenario, and then when the subsequent UPF network element monitors traffic for the AI and ML services by using AMBR for the AI and ML services in the QoS parameters of the AI and ML services, monitoring efficiency can be improved, and a situation that the AI and ML services occupy excessive network resources is avoided.

Further, when the UPF network element monitors that the traffic for the AI and ML services exceeds the AMBR for the AI and ML services, the monitoring event may be triggered to be reported to the AF network element via the NEF network element, then the AF network element may determine network resource adjustment information according to the monitoring event, and send the network resource adjustment information to the PAC network element via the NEF network element, so that the PCF network element may adjust QoS parameters of the AI and ML services according to the network resource adjustment information sent by the AF network element, and further the SMF network element may notify the QoS parameters of the AI and ML services adjusted by the UPF network element, and may support flexible adjustment of QoS parameters according to service requirements.

Based on the same inventive concept, a PCF network element is provided in the embodiments of the present disclosure, as described in the following embodiments. Since the principle of the PCF network element embodiment for solving the problem is similar to that of the method embodiment, the implementation of the PCF network element embodiment can be referred to the implementation of the method embodiment, and the repetition is not repeated.

Fig. 6 is a schematic structural diagram of a PCF network element in an embodiment of the disclosure, as shown in fig. 6, a PCF network element 600 may include: a parameter determination unit 610 and a parameter transmission unit 620.

The parameter determination unit 610 may be configured to: qoS parameters for AI and ML traffic are determined, including AMBR for AI and ML traffic. The parameter sending unit 620 may be configured to send QoS parameters of the AI and ML services to the UPF network element via the SMF network element, so that the UPF network element monitors traffic of the AI and ML services according to AMBR for the AI and ML services. Wherein the QoS parameters of the AI and ML services are determined by the PCF network element according to at least one of the following information: the method comprises the steps of User Equipment (UE) subscription information participating in an AI and ML service, capability information reported by the UE, resource information requested by the UE to execute the AI and ML service, quality of service (QoS) information requested by an AF network element to execute the AI and ML service, resource information requested by the AF network element to execute the AI and ML service and service flow information of a network.

In some embodiments of the present disclosure, PCF network element 600 may further comprise: a parameter adjustment unit 630. Wherein the parameter adjustment unit 630 is operable to: receiving network resource adjustment information sent by an AF network element through a NEF network element; and adjusting QoS parameters of the AI and ML services according to the network resource adjustment information. The network resource adjustment information is determined by the AF network element according to the reported monitoring event, the monitoring event is triggered and reported under the condition that the UPF network element monitors that the traffic of the AI and ML service exceeds the AMBR aiming at the AI and ML service, and the monitoring event is reported to the AF network element through the NEF network element.

Based on the same inventive concept, a UPF network element is provided in the embodiments of the present disclosure, as described in the following embodiments. Since the principle of the solution of the UPF network element embodiment is similar to that of the method embodiment, the implementation of the UPF network element embodiment can be referred to the implementation of the method embodiment, and the repetition is omitted.

Fig. 7 is a schematic structural diagram of a UPF network element in an embodiment of the present disclosure, and as shown in fig. 7, a UPF network element 700 may include: a parameter receiving unit 710 and a monitoring unit 720.

The parameter receiving unit 710 may be configured to: and receiving the QoS parameters of the AI and ML services issued by the PCF network element via the SMF network element, wherein the QoS parameters of the AI and ML services comprise AMBR for the AI and ML services. The monitoring unit 702 may be configured to: and monitoring the traffic of the AI and ML services through application related information of the data packet according to the AMBR for the AI and ML services.

In some embodiments of the present disclosure, the UPF network element 700 may further include: event reporting unit 730. Wherein, the event reporting unit 730 may be configured to: if the traffic of the AI and ML service is monitored to exceed the AMBR for the AI and ML service, triggering a monitoring event to report to the AF network element through the NEF network element, so that the AF network element determines network resource adjustment information according to the reported monitoring event, and sends the network resource adjustment information to the PCF network element through the NEF network element, and the PCF network element adjusts QoS parameters of the AI and ML service according to the network resource adjustment information.

In some embodiments of the present disclosure, the application-related information of the data packet includes at least one of the following information: application identity, data network name DNN, access point name APN, and five tuples. Wherein the monitoring unit 702 is further operable to: when the UE performs the AI and ML services, traffic of the AI and ML services is determined according to application related information of the data packet, and monitored by AMBR for the AI and ML services.

Based on the same inventive concept, an AF network element is provided in the embodiments of the present disclosure, as described in the following embodiments. Since the principle of the AF network element embodiment for solving the problem is similar to that of the method embodiment described above, the implementation of the AF network element embodiment can be referred to the implementation of the method embodiment described above, and the repetition is not repeated.

Fig. 8 is a schematic structural diagram of an AF network element in an embodiment of the present disclosure, as shown in fig. 8, an AF network element 800 may include: an event receiving unit 810, an information adjusting unit 820, and an information transmitting unit 830.

The event receiving unit 810 may be configured to: a monitoring event reported via the NEF network element is received. The monitoring event is triggered and reported when the UPF network element monitors that the traffic of the AI and ML service exceeds the AMBR aiming at the AI and ML service, wherein the AMBR aiming at the AI and ML service is information in QoS parameters of the AI and ML service. The information adjustment unit 820 may be configured to: and determining network resource adjustment information according to the reported monitoring event. The information transmitting unit 830 may be configured to: and the network resource adjustment information is sent to the PCF network element through the NEF network element, so that the PCF network element adjusts QoS parameters of the AI and ML services according to the network resource adjustment information.

Fig. 9 shows a block diagram of an electronic device in an embodiment of the disclosure. An electronic device 900 according to such an embodiment of the invention is described below with reference to fig. 9. The electronic device 900 shown in fig. 9 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.

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

Wherein the storage unit stores program code that is executable by the processing unit 910 such that the processing unit 910 performs steps according to various exemplary embodiments of the present invention described in the above-described "exemplary methods" section of the present specification. The storage unit 920 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 9201 and/or cache memory 9202, and may further include 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, such program modules 9205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.

The bus 930 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 900 may also communicate with one or more external devices 970 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 900, and/or any device (e.g., router, modem, etc.) that enables the electronic device 900 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 950. Also, electronic device 900 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 960. As shown, the network adapter 960 communicates with other modules of the electronic device 900 over the bus 930. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 900, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

In an exemplary embodiment of the present disclosure, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention as described in the "exemplary methods" section of this specification, when said program product is run on the terminal device.

When the electronic device 900 provided in the embodiments of the present disclosure is a PCF network element, the processing unit 910 may execute the following steps in the foregoing embodiments: determining QoS parameters of AI and ML traffic, the QoS parameters of the AI and ML traffic including AMBR for the AI and ML traffic; and the QoS parameters of the AI and ML service are issued to the UPF network element by the SMF network element, so that the UPF network element monitors the traffic of the AI and ML service according to the AMBR aiming at the AI and ML service.

When the electronic device 900 provided in the embodiments of the present disclosure is a UPF network element, the processing unit 910 may perform the following steps in the foregoing embodiments: receiving QoS parameters of AI and ML services issued by the PCF network element via the SMF network element, wherein the QoS parameters of the AI and ML services comprise AMBR for the AI and ML services; and monitoring the traffic of the AI and ML services through application related information of the data packet according to the AMBR for the AI and ML services.

When the electronic device 900 provided in the embodiment of the present disclosure is an AF network element, the processing unit 910 may perform the following steps in the foregoing embodiment: receiving a monitoring event reported by an NEF network element, wherein the monitoring event is triggered to report when the UPF network element monitors that the traffic of the AI and ML service exceeds the AMBR aiming at the AI and ML service, and the AMBR aiming at the AI and ML service is information in QoS parameters of the AI and ML service; according to the reported monitoring event, determining network resource adjustment information; and the network resource adjustment information is sent to the PCF network element through the NEF network element, so that the PCF network element adjusts QoS parameters of the AI and ML services according to the network resource adjustment information.

A program product for implementing the above-described method according to an embodiment of the present invention may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: 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), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.

Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order or that all illustrated steps be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.

From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims (12)

1. A method for monitoring resources, the method being performed by a policy control function PCF network element, comprising:

determining quality of service, qoS, parameters for artificial intelligence, AI, and machine learning, ML, traffic, the QoS parameters for AI and ML traffic comprising an aggregate maximum bit rate, AMBR, for AI and ML traffic;

and transmitting QoS parameters of the AI and ML services to a user plane function UPF network element by a session management function SMF network element, so that the UPF network element monitors traffic of the AI and ML services according to the AMBR aiming at the AI and ML services.

2. The method of claim 1, wherein the QoS parameters of the AI and ML traffic are determined by the PCF network element based on at least one of: 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 execute the AI and ML services, qoS information requested by an Application Function (AF) network element to execute the AI and ML services, resource information requested by the AF network element to execute the AI and ML services, and service flow information of a network.

3. The method according to claim 1, wherein the method further comprises:

receiving network resource adjustment information sent by an AF (user interface) network element through a NEF (network opening function) network element, wherein the network resource adjustment information is determined by the AF network element according to a reported monitoring event, the monitoring event is triggered to be reported when the UPF network element monitors that the traffic of AI and ML (media access control) services exceeds the AMBR aiming at the AI and ML services, and the monitoring event is reported to the AF network element through the NEF network element;

and adjusting QoS parameters of the AI and ML services according to the network resource adjustment information.

4. A method of resource monitoring, the method performed by a UPF network element, comprising:

receiving QoS parameters of AI and ML services issued by a PCF network element through an SMF network element, wherein the QoS parameters of the AI and ML services comprise AMBR for the AI and ML services;

and monitoring the traffic of the AI and ML services through the application related information of the data packet according to the AMBR aiming at the AI and ML services.

5. The method according to claim 4, wherein the method further comprises:

if the traffic of the AI and ML service is monitored to exceed the AMBR for the AI and ML service, triggering a monitoring event to be reported to an AF network element through a NEF network element, so that the AF network element determines network resource adjustment information according to the reported monitoring event, and sends the network resource adjustment information to the PCF network element through the NEF network element, and then the PCF network element adjusts QoS parameters of the AI and ML service according to the network resource adjustment information.

6. The method of claim 4, wherein the application-related information of the data packet includes at least one of: application identity, data network name DNN, access point name APN, and quintuple;

wherein, the monitoring the traffic of the AI and ML services according to the AMBR for the AI and ML services through the application related information of the data packet includes:

and when the UE performs the AI and ML services, determining the traffic of the AI and ML services according to the application related information of the data packet, and monitoring the traffic of the AI and ML services by using the AMBR for the AI and ML services.

7. A method for resource monitoring, the method being performed by an AF network element and comprising:

receiving a monitoring event reported by an NEF network element, wherein the monitoring event is triggered to report when the UPF network element monitors that the traffic of the AI and ML service exceeds the AMBR aiming at the AI and ML service, and the AMBR aiming at the AI and ML service is information in QoS parameters of the AI and ML service;

according to the reported monitoring event, determining network resource adjustment information;

and sending the network resource adjustment information to a PCF (physical state communication) network element through the NEF network element, so that the PCF network element adjusts QoS parameters of the AI and ML services according to the network resource adjustment information.

8. A PCF network element, comprising:

a parameter determining unit for determining QoS parameters of AI and ML services, wherein the QoS parameters of the AI and ML services comprise AMBR for the AI and ML services;

and the parameter sending unit is used for sending QoS parameters of the AI and ML services to a UPF network element through an SMF network element, so that the UPF network element monitors the traffic of the AI and ML services according to the AMBR aiming at the AI and ML services.

9. A UPF network element, 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, where the QoS parameters of the AI and ML services include AMBR for the AI and ML services;

and the monitoring unit is used for monitoring the traffic of the AI and the ML service through the application related information of the data packet according to the AMBR aiming at the AI and the ML service.

10. An AF network element, comprising:

an event receiving unit, configured to receive a monitoring event reported by a NEF network element, where the monitoring event is triggered and reported when a UPF network element monitors that traffic of an AI and ML service exceeds an AMBR for the AI and ML service, and the AMBR for the AI and ML service is information in QoS parameters of the AI and ML service;

The information adjustment unit is used for determining network resource adjustment information according to the reported monitoring event;

and the information sending unit is used for sending the network resource adjustment information to the PCF network element through the NEF network element, so that the PCF network element adjusts QoS parameters of the AI and ML services according to the network resource adjustment information.

11. An electronic device, comprising:

one or more processors;

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 implement the method of any of claims 1-7.

12. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the method according to any one of claims 1 to 7.