QoS feature enhancement method and computer program product

Abstract

The invention discloses a QoS (Quality of Service) feature enhancement method and a computer program product, the method is executed by a core network, and the method comprises the following steps: receiving a computing type QoS flow application message sent by a functional network element; according to own network resource data and load data, determining configuration information for calculating the QoS flow on the basis of a suggested value of the service quality configuration information carried by the calculation type QoS flow application message; and establishing an enhanced QoS flow according to the configuration information. The QoS features are enhanced with reference to the network resource data and the internal computing power load data, and the cloud service quality is ensured.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a QoS feature enhancement method and a computer program product. Background Art

[0002] In 5G networks, QoS (Quality of Service) defines the rules and requirements for packet forwarding between terminals and services, and is a critical foundation for wireless access network operations. The core network uses QoS identifiers to describe the quality of service for service flows. Base stations ensure transmission quality based on these QoS requirements. Base station signaling configuration, scheduling strategies, resource allocation, and other processes all reference QoS definitions.

[0003] With the introduction of wireless computing services, communication services have expanded from single-user service data transmission to a comprehensive service that integrates open RAN internal data transmission, wireless computing resource support, and edge computing task services. Traditional 5QI-based QoS systems fail to reference network resource data and internal computing load data. This can lead to wasted transmission resources due to over-guaranteed channels, or untimely data transmission that impacts cloud service quality. Summary of the Invention

[0004] In order to solve the above problems, the present invention proposes a QoS feature enhancement method and computer program product, which enhances QoS features with reference to network resource data and internal computing load data to ensure the quality of cloud services.

[0005] An embodiment of the present invention provides a method for enhancing QoS features, the method being performed by a core network and comprising:

[0006] Receive the calculation type QoS flow application message sent by the functional network element;

[0007] Determine the configuration information of the calculated QoS flow based on the recommended value of the quality of service configuration information carried in the calculated QoS flow application message according to its own network resource data and load data;

[0008] An enhanced QoS flow is established according to the configuration information.

[0009] Preferably, the computing QoS flow application message carries the established PDU Session ID, or the IP to which the computing task is assigned, or indication information for initiating a new PDU Session.

[0010] Preferably, the configuration information includes the maximum service response time, service bandwidth, service processing resource information, service redundancy information, service elasticity information and service load balancing information of wireless edge cloud computing.

[0011] Preferably, the configuration information includes a maximum service response time;

[0012] The maximum service response time is the maximum delay that the computing task can accept;

[0013] The difference between the maximum service response time and the cloud computing service response time of the cloud base station is used to indicate the deployment of computing tasks in the cloud base station;

[0014] The cloud computing service response time is the sum of the cloud computing delay of the cloud base station and the data packet delay of the cloud base station.

[0015] Preferably, the configuration information includes service bandwidth;

[0016] The service bandwidth is the data transmission bandwidth requirement between the terminal, edge device or application device and the cloud base station;

[0017] The service bandwidth includes the bandwidth of the wireless air interface link and the bandwidth of the non-user plane cloud platform transmission link.

[0018] Preferably, the configuration information includes service processing resource information;

[0019] The service processing resource information is used to instruct the cloud base station to allocate dedicated computing resources for computing tasks in real time or evaluate the current shared computing resources;

[0020] The service processing resource information includes but is not limited to the number of CPU cores, main frequency, architecture type, memory capacity and storage capacity.

[0021] Preferably, the configuration information includes service redundancy information;

[0022] The service redundancy information includes the computing power backup capabilities of other computing power neighboring areas within the service area where the cloud base station is located;

[0023] The service redundancy information is used to instruct the cloud base station to apply for computing resources from other computing power neighboring cells within the service area and to deploy service capabilities.

[0024] Preferably, the configuration information includes service resilience information;

[0025] The service elasticity information is the ratio of the resource occupancy required for the maximum service processing capacity of the computing task to the resource occupancy required for the minimum service processing capacity, and is used to instruct the cloud base station to dynamically allocate or release computing resources according to the load requirements of the received computing power task.

[0026] Preferably, the configuration information includes service load balancing information;

[0027] The service load balancing information is the capability requirement for distributing computing tasks on multiple cloud base stations and deploying them in parallel, and is used to instruct the cloud base station to split the computing tasks.

[0028] An embodiment of the present invention further provides a method for enhancing QoS features, the method being performed by a functional network element of a core network, the method comprising:

[0029] Sending a computational QoS flow application message to the core network, and receiving configuration information determined by the core network based on the computational QoS flow application message;

[0030] Perform capability collection and measurement on terminals and cloud base stations to obtain measurement data;

[0031] An optimal wireless computing and data transmission scheme is formulated according to the measurement data and the configuration information.

[0032] Preferably, the configuration information includes the maximum service response time, service bandwidth, service processing resource information, service redundancy information, service elasticity information and service load balancing information of wireless edge cloud computing.

[0033] Preferably, the measurement data includes the link status between the terminal and the cloud base station, and the computing resource load and idle computing power of the cloud base station;

[0034] The link status includes connectivity, bandwidth, delay, packet loss rate and delay jitter.

[0035] Preferably, the method further comprises:

[0036] Configure the optimal wireless computing data transmission solution to the cloud base station, perform data routing configuration at the base station node level, and configure the RAN protocol stack to complete data openness and routing configuration;

[0037] The optimal wireless computing data transmission solution is synchronized to the core network and computing power network.

[0038] Preferably, the optimal wireless computing data transmission scheme includes a transmission mode and an optimal routing strategy;

[0039] The transmission mode is one of traditional UPF forwarding, direct transmission of computing data within RAN, and computing task migration.

[0040] Preferably, before sending the computational QoS flow application message to the core network, the method further includes:

[0041] Receive computing task scheduling instructions from the computing network through the extended interface;

[0042] Generate a computing task deployment plan for deploying different computing tasks on different cloud base stations based on the computing task scheduling instruction message and combining it with locally available wireless edge computing resources;

[0043] Use the cloud management interface to send computing task deployment messages to the corresponding cloud base station;

[0044] After determining that the computing task has run successfully based on the message fed back by the cloud base station, a computing QoS flow application message is sent to the core network.

[0045] Furthermore, the computing task scheduling instruction message includes task type, task scale, task requirements and task time limit.

[0046] An embodiment of the present invention further provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the steps of implementing the method in the core network in the above embodiment.

[0047] An embodiment of the present invention further provides a computer program product, comprising a computer program / instruction, wherein when the computer program / instruction is executed by a processor, a functional network element implements the steps of the method.

[0048] The present invention provides a method and computer program product for enhancing QoS features, executed by a core network. The method comprises: receiving a computational QoS flow request message sent by a functional network element; determining, based on its own network resource data and load data, configuration information for the computational QoS flow based on recommended values ​​of the quality of service configuration information carried in the computational QoS flow request message; and establishing an enhanced QoS flow based on the configuration information. This application enhances QoS features by referencing network resource data and internal computing load data to ensure cloud service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of a QoS feature enhancement method provided by an embodiment of the present invention;

[0050] Figure 2 Schematic diagram of the principle of feature enhancement of the QoS feature enhancement method provided by an embodiment of the present invention;

[0051] Figure 3 1 is another flow chart of the QoS feature enhancement method provided by an embodiment of the present invention;

[0052] Figure 4 This is another flow chart of a QoS feature enhancement method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0054] The embodiment of the present invention provides a QoS feature enhancement method, see Figure 1 , is a flow chart of a QoS feature enhancement method provided by an embodiment of the present invention, the method comprising the following steps:

[0055] Step S1, receiving a calculation-type QoS flow application message sent by a functional network element;

[0056] Step S2, determining the configuration information of the calculated QoS flow based on the recommended value of the quality of service configuration information carried in the calculated QoS flow application message according to its own network resource data and load data;

[0057] Step S3: Establish an enhanced QoS flow according to the configuration information.

[0058] During the specific implementation of this embodiment, the SMO (Service Management and Orchestration) network element of the access network sends a computing QoS flow application message to the core network, and the wireless side corresponds to a computing bearer.

[0059] It should be noted that, in this embodiment, the SMO network element of the core network is used as an example to illustrate the specific process of implementing this solution. In other embodiments, other functional network elements can also be used to perform corresponding steps or functions.

[0060] The calculation-based QoS flow application message contains the recommended value of the service quality configuration information, which is used to support the decision-making basis of the core network to initiate the enhanced QoS flow.

[0061] Existing 5QI-based QoS flow definitions primarily focus on transmission quality, defining aspects such as resource type (GBR, non-GBR, or Delaycritical GBR), priority, packet delay budget, packet error rate, average window (applicable only to GBR and Delay-critical GBR), maximum data burst size (applicable only to Delay-critical GBR), and packet delay budget (PDB). However, they fail to reference network resource data or internal computing load data, potentially leading to wasted transmission resources due to over-guaranteed channels or untimely data transmission that impacts cloud service quality.

[0062] Therefore, upon receiving a Computed QoS Flow Request message, the core network begins the Computed QoS Flow Establishment process. Based on the PDUSession Establishment process, the quality requirements for the Computed Service dimension are supplemented and enhanced. Based on its own network resources and load, the core network formulates the Computed QoS Flow configuration information based on the recommended QoS configuration values ​​in the Computed QoS Flow Request message.

[0063] An enhanced QoS flow is established based on the configuration information and sent to the cloud base station and the terminal.

[0064] The enhanced definition of wireless computing data transmission service quality effectively supplements the deficiencies in the service quality characteristics in computing services. It can provide a comprehensive description of service quality requirements for future wireless computing services and support base stations in formulating efficient computing and data transmission plans and making routing and forwarding decisions.

[0065] In another embodiment of the present invention, a computing QoS flow application message can be initiated for an existing PDU Session, carrying the PDU Session ID or the IP address assigned to the computing task. The application can also initiate a new PDU Session.

[0066] By carrying the established Session ID or the IP assigned to the computing task, or initiating instruction information for a new PDU Session, the core network can be instructed to enter the computing QoS flow establishment process based on the PDU Session establishment process, supplement and enhance the quality requirements of the computing service dimension, and enhance the QoS features.

[0067] In another embodiment provided by the present invention, the configuration information includes the maximum service response time, service bandwidth, service processing resource information, service redundancy information, service elasticity information and service load balancing information of wireless edge cloud computing.

[0068] The recommended values ​​of the service quality configuration information correspond to the recommended values ​​of the maximum service response time, service bandwidth, service processing resource information, service redundancy information, service elasticity information and service load balancing information of wireless edge cloud computing, which support the decision-making basis for the core network to initiate enhanced QoS flows.

[0069] To enhance transmission quality of service (QoS), the description of transmission quality of service (QoS) must evolve from a traditional focus on transmission capabilities to one that prioritizes transmission quality while also considering the capabilities and effectiveness of wireless edge cloud computing services. The description should expand from bearers to include computational bearers with collaborative computing tasks. With enhanced QoS features, the new transmission QoS definition should include a definition of wireless edge cloud computing QoS to ensure cloud service quality.

[0070] In another embodiment of the present invention, the configuration information includes a maximum service response time.

[0071] The maximum service response time for wireless edge cloud computing describes the time required for a wireless edge cloud platform (cloud base station) to respond to service requests from terminals, edge devices, or applications. This represents the maximum acceptable latency for computing tasks and is a key indicator for determining whether computing tasks can be deployed in a wireless edge cloud.

[0072] The difference between the maximum service response time and the cloud computing service response time of the cloud base station is used to indicate the deployment of computing tasks in the cloud base station.

[0073] The response time of cloud computing services is the sum of cloud computing latency and the packet delay budget (PDB). Response time is highly correlated with the type of computing task. For example, the response time for AI training is 200ms, for AR it is 50ms, for XR services it is 20ms, and for high-precision industrial control it is 10ms. Response time is also affected by different computing task deployment and data transmission schemes. For example, computing on a remote, high-power node can achieve short computation time and long transmission latency, while computing on a local, low-power node can achieve long computation time and short transmission latency.

[0074] The deployment of computing tasks in the cloud base station is indicated by the maximum service response time, taking into account the service capabilities and effects of wireless edge cloud computing.

[0075] In another embodiment provided by the present invention, the configuration information includes service bandwidth;

[0076] Wireless edge cloud computing service bandwidth describes the available data transmission bandwidth requirements between terminals, edge devices, or applications and the wireless edge cloud platform, specifically the cloud base station. Data transmission bandwidth has a direct impact on cloud computing performance. The data transmission bandwidth deployed on the wireless edge cloud platform is primarily constrained by the wireless air interface link, and factors such as wireless channel quality and multi-user multiplexing must be considered. Unlike service bandwidth in traditional cloud computing, the wireless edge cloud computing service bandwidth of a single device node (cloud base station) is a dynamic, non-committed indicator. The system must monitor this indicator in real time to ensure overall cloud service performance.

[0077] The service bandwidth includes two parts: the wireless air interface link and the non-user-plane cloud platform transmission link. This is also the main difference from the traditional QoS bandwidth definition.

[0078] Service bandwidth is the actual transmission bandwidth capability of the wireless edge cloud platform (cloud base station) for the multiple users, their associated bearers, and QoS flows involved in a computing task, as assessed in real time by the wireless edge cloud platform (cloud base station) within the statistical window (real-time performance is reflected in the statistical window generally being less than the upper limit of the service key frame delay jitter requirement, such as less than 20ms for VR services). Note: Real-time bandwidth is more accurate and real-time than 5QI planned bandwidth, effectively reducing the service response time jitter of computing tasks.

[0079] In yet another embodiment provided by the present invention, the configuration information includes service processing resource information;

[0080] The wireless edge cloud computing service processing resource information is used to describe the computing resource requirements of the wireless edge cloud platform, that is, the cloud base station, for processing the computing tasks corresponding to this QoS flow.

[0081] Because wireless edge cloud base stations integrate 5G base stations, AI computing tasks, and industry application services, resources are fully shared in the cloud. Base stations should allocate the required dedicated computing resources to wireless computing tasks in real time based on QoS instructions, or evaluate and commit to currently available shared computing resources.

[0082] The service processing capability part of QoS should include but not be limited to: number of CPU cores (unit: piece), main frequency (unit: GHz), architecture type (unit: enumeration value, x86, ARM), memory capacity (unit: Gbyte), and storage capacity (unit: Gbyte).

[0083] The service processing resource information is used to instruct the cloud base station to allocate exclusive computing resources or evaluate the current shared computing resources for computing tasks in real time to ensure the quality of wireless edge cloud computing services.

[0084] In yet another embodiment provided by the present invention, the configuration information includes service redundancy information;

[0085] The service redundancy information of wireless edge cloud computing, namely service reliability information, is used to describe the computing power backup capability requirements between each computing power neighborhood within the same transmission ring within the service area, to ensure that service continuity can be maintained in the event of a single computing node failure or wireless connection interruption.

[0086] For high-value businesses such as industrial production and the Internet of Vehicles, the deployment of computing tasks requires a certain degree of service redundancy, which allows for rapid switching of tasks to other computing nodes. For example, neighboring base stations that are in the same transmission ring as the serving base station and have a reliable connection between the two should have a high idle "service processing capacity" (such as CPU, memory, etc.) to ensure that computing tasks can be switched to neighboring cells at any time. The service redundancy indicator can be understood as the "service processing capacity" requirement of the neighboring cells of the serving cell. Based on the resource requirements of the service redundancy part in the QoS indication, the base station should apply for corresponding resources from the neighboring computing cells (such as base stations, MEC, etc.) near the base station and deploy corresponding service capabilities to form service redundancy with the computing tasks of the base station and improve service reliability.

[0087] In another embodiment provided by the present invention, the configuration information includes service elasticity information;

[0088] Service elasticity information is the ratio of the resource occupation required for the maximum service processing capacity of the computing task to the resource occupation required for the minimum service processing capacity.

[0089] The service elasticity information of wireless edge cloud computing is used to describe the ability to dynamically allocate or release computing resources based on the ever-changing computing task load demands issued by the computing power network to the wireless access network.

[0090] Because resources in wireless edge cloud base stations are fully cloud-based and shared, the computing and transmission resources required by new computing tasks must be obtained through scaling other services. For example, can a base station with eight 5G cell capacity be dynamically adjusted to four at night, freeing up computing and transmission resources for deploying security visual recognition tasks? This service elasticity should be clearly defined and communicated to the base station through QoS at the beginning of service establishment, allowing the cloud base station to assess how much computing resources will be released or occupied by adjusting the workload of a particular computing task. For example, a fully configured CPU requires 12 cores and 20GB of memory; the minimum scaling configuration requires 6 cores and 4GB of memory. The service elasticity is: CPU: 2, Memory: 5.

[0091] Through service elasticity information, the description object is expanded from bearer to computing bearer with computing power task collaboration, that is, bearer + computing task combination.

[0092] In another embodiment provided by the present invention, the configuration information includes service load balancing information;

[0093] The service load balancing information of wireless edge cloud computing can describe the ability requirements of distributing the computing task load on multiple wireless edge cloud platforms (cloud base stations) for parallel deployment, so as to avoid the business bottleneck problem caused by the limited computing power of a single node for large computing tasks.

[0094] The service load balancing information is the ability requirement to distribute computing tasks on multiple cloud base stations and deploy them in parallel, which is used to instruct the cloud base station to split computing tasks.

[0095] Through QoS service load balancing, it is clearly defined and informed to the base station whether the computing task can be split, as well as the requirements for the number of computing nodes (such as base stations, MEC, etc.). This capability indicator describes the requirements for the system in the following two aspects: whether the computing task can be split (if this is not supported, it means that load balancing capability is not required), and the minimum resource requirement granularity after splitting is a few CPU cores (if it is 0, it means that no independent CPU core requirement is required, and one core can be reused with other tasks) and memory capacity requirements. Under the premise that the computing task supports splitting, the system is required to have N wireless edge cloud platforms (cloud base stations) whose "service processing capabilities" all meet the "minimum resource requirement granularity" requirements. Ultimately, it can ensure that a large computing task can be distributed and deployed on N cloud base stations to achieve service load balancing.

[0096] See also Figure 2 , is a schematic diagram of the feature enhancement principle of the QoS feature enhancement method provided in an embodiment of the present invention.

[0097] Enhanced wireless computing network QoS features include computing service load balancing, computing service elasticity, computing service redundancy, computing service processing capacity, computing service bandwidth, and computing service response time. These enhanced QoS features can determine resource type (GRB, non-GRB, or delay-critical GRB); transmission priority, packet delay budget (PDB), packet error rate, average window, and maximum data burst size.

[0098] The enhanced definition of wireless computing data transmission service quality effectively supplements the deficiencies in the service quality characteristics in computing services. It can provide a comprehensive description of service quality requirements for future wireless computing services and support base stations in formulating efficient computing and data transmission plans and making routing and forwarding decisions.

[0099] Existing QoS features are no longer sufficient to guide base stations in developing efficient computing and data transmission plans. The purpose of data transmission within the RAN is evolving from connecting terminals and the core network to meet user internet access needs to supporting cloud, edge, and end computing tasks, providing multimodal cloud computing services directly to users. The quality of cloud computing services is determined by multiple factors, including transmission (data) quality, computing power, and software algorithms. Taking edge VR services as an example, potential problems include: 1) Although the network can transmit data to the destination in a timely manner, the service node may lack sufficient CPU and GPU computing resources for high-definition rendering due to preemptive multi-service tasks, resulting in reduced image frame rates; 2) Although the average rate of the QoS flow (or transport bearer) meets the GBR rate requirement, the transmission delay of a key service control frame may exceed 50ms due to packet loss, causing rendering service lag. Therefore, data transmission strategies, computing resource orchestration, and software algorithms need to be coordinated at the foundational level (within the base station), and multi-objective joint optimization of transmission plans and computing resources must be achieved to ensure efficient and high-quality wireless edge cloud services. Existing QoS flow transmission quality descriptions evaluate transmission performance based on a single CT metric, such as link bandwidth, latency, and packet loss rate, and use this information to determine transmission solutions. This data transmission performance (QoS) evaluation and transmission strategy formulation fail to consider the RAN's internal computing power and application status, leading to wasted transmission resources due to over-guaranteed channels, or delayed data transmission that impacts cloud service quality.

[0100] Targeting wireless computing scenarios, the definition of 5G QoS features has been enhanced. With the introduction of computing service quality descriptions, it now encompasses multiple capabilities, performance descriptions, and control measures, including cloud resources and computing tasks. Traditionally, the core network specifies 5QIs, and base stations agree on a best-effort QoS approach based on their QoS capabilities. However, due to base stations' lack of cloud resources and computing task orchestration data, a single network element cannot independently guarantee the comprehensive cloud, network, and industry service quality requirements outlined by the enhanced 5G QoS features. Therefore, a collaborative service quality assurance process for 5G networks, cloud platforms, and computing tasks is needed.

[0101] In another embodiment provided by the present invention, see Figure 3 , is another flow chart of a method for enhancing QoS features provided by an embodiment of the present invention. The method is executed by a functional network element of a core network and includes the following steps:

[0102] Step S301, sending a calculation type QoS flow application message to the core network, and receiving configuration information determined by the core network according to the calculation type QoS flow application message;

[0103] Step S302: Capacity collection and measurement are performed on the terminal and the cloud base station to obtain measurement data;

[0104] Step S303: formulating an optimal wireless computing and data transmission solution based on the measurement data and the configuration information.

[0105] When implementing this embodiment, see Figure 4 , is another flowchart of a QoS feature enhancement method provided by an embodiment of the present invention. After the SMO network element sends a calculation data flow application to the core network, the core network calculates the QoS data flow establishment based on the calculation QoS flow application message, that is, determines the configuration information of the enhanced QoS flow configuration and feeds it back to the terminal and cloud base station.

[0106] It should be noted that, in this embodiment, the SMO network element of the core network is used as an example to illustrate the specific process of implementing this solution. In other embodiments, other functional network elements can also be used to perform corresponding steps or functions.

[0107] After completing the calculation of the QOS flow establishment, the SMO network element uses the O2 and A1 interfaces to perform capability collection tests on the cloud base station and terminal respectively to obtain measurement data.

[0108] The SMO network element formulates a wireless computing data transmission plan, that is, formulates an optimal wireless computing data transmission plan based on the measurement data and the configuration information.

[0109] Based on the established enhanced QoS flow, the SMO network element can use the O2 interface to collect the status of the wireless cloud platform and use the A1 interface to obtain the wireless network operation status through the RIC. Based on the enhanced QoS indicators, it can maintain the optimal wireless computing data transmission plan in real time to ensure data transmission quality.

[0110] In another embodiment provided by the present invention, the configuration information includes the maximum service response time, service bandwidth, service processing resource information, service redundancy information, service elasticity information and service load balancing information of wireless edge cloud computing.

[0111] The enhanced definition of wireless computing data transmission service quality effectively supplements the deficiencies in the service quality characteristics in computing services. It can provide a comprehensive description of service quality requirements for future wireless computing services and support base stations in formulating efficient computing and data transmission plans and making routing and forwarding decisions.

[0112] In another embodiment provided by the present invention, the measurement data includes the link status between the terminal and the cloud base station, and the computing resource load and idle computing power of the cloud base station;

[0113] Link status includes connectivity, bandwidth, delay, packet loss rate, and delay jitter.

[0114] The computing power resource load includes the load percentage, and the idle computing power includes the number of idle CPU cores.

[0115] The computing service elasticity of the cloud base station application is determined through the obtained measurement data, and the optimal data transmission strategy is determined based on the computing service elasticity.

[0116] In another embodiment provided by the present invention, see Figure 4 After obtaining the optimal wireless computing data transmission solution, the SMO network element also configures the transmission solution to the cloud base station via the O2 interface, implementing data routing configuration at the base station node level. Simultaneously, the RIC can be used to configure the RAN protocol stack via the E2 interface, enabling data openness and routing configuration for "direct transmission of computing data within the RAN."

[0117] The SMO network element will coordinate the transmission plan with the core network and computing power network. That is, the SMO network element will synchronize the generated optimal wireless computing data transmission plan to the core network and computing power network to support the subsequent expansion of business.

[0118] In another embodiment provided by the present invention, the optimal wireless computing data transmission solution includes a transmission mode and an optimal routing strategy;

[0119] Existing QoS features can no longer guide base stations to formulate efficient computing data routing strategies or computing task deployment plans. After the introduction of wireless computing power services, UPF is no longer the only data aggregation point, and data packet forwarding requires a routing strategy that can adapt to the wireless network structure. For data transmission services between extended applications dynamically distributed and loaded in base stations and mobile terminals, the system cannot provide similar fixed UPF network elements as data anchor points. Data is transmitted between fixed points and mobile points, and has evolved into transmission between mobile points. Both extended applications and terminals have the possibility of quickly switching base stations within tens of milliseconds. Through traditional SMF signaling configuration, UPF serves as a unified data anchor point for data distribution, which will lead to data distribution delays. A large amount of data needs to be forwarded between stations, resulting in transmission delay jitter and waste of transmission network resources, so data forwarding forms need to be more diversified. For example, when a terminal initiates a service request to the wireless edge cloud, after the service cell where the terminal is located receives the wireless computing data, the system has multiple possible data transmission solutions:

[0120] Traditional UPF forwarding means that the serving cell does not identify or differentiate the data in the bearer. SDAP maps the data to a QoS flow and then transmits it back to the UPF. The data is then routed and forwarded to the target base station via the UPF, ultimately offloading the data to the service application. This solution has the advantages of wide routing range and high accessibility, but the disadvantage is that the data is forwarded through the central UPF, resulting in a long data transmission path.

[0121] Direct transmission of computing data within the RAN: The serving cell identifies and extracts computing data from the bearer and attempts to send data directly to destinations with a network connection, without forwarding it through the UPF. This solution offers the advantage of a short transmission path and low latency, but suffers from a short routing range.

[0122] Computing task migration leverages computing orchestration technology to migrate service applications to the base station where the serving cell is located, enabling local offload and processing of wireless computing data. This solution avoids the transmission of computing data, offering high reliability and latency advantages, but it does incur computing task migration costs and deployment resource costs.

[0123] The transmission mode needs to be selected from traditional UPF forwarding, direct transmission of computing data within RAN, computing power task migration and other modes.

[0124] Faced with diverse wireless edge cloud computing scenarios, the RAN needs to adjust its internal computing data forwarding and routing strategies in real time based on the service operation status. This includes determining whether insufficient computing resources at the local base station are impacting the user experience and whether computing data needs to be forwarded to adjacent base stations for collaborative computing. However, current QoS features are unable to guide base stations in formulating efficient computing data routing strategies.

[0125] By enhancing the QoS feature definition, the optimal wireless computing data transmission solution including transmission mode and optimal routing strategy is determined to support the RAN system to effectively carry out the wireless computing data transmission solution formulation and routing decision-making required for wireless edge cloud computing services.

[0126] In another embodiment of the present invention, before sending the computational QoS flow application message to the core network, the method further includes:

[0127] Receive computing task scheduling instructions from the computing network through the extended interface;

[0128] Generate a computing task deployment plan for deploying different computing tasks on different cloud base stations based on the computing task scheduling instruction message and combining it with locally available wireless edge computing resources;

[0129] Use the cloud management interface to send computing task deployment messages to the corresponding cloud base station;

[0130] After determining that the computing task has been successfully executed based on the message fed back by the cloud base station, a computing QoS flow application message is sent to the core network.

[0131] When implementing this embodiment, see Figure 4After the cloud base station is started, the 5G cell service is normal, the computing power network can perceive the computing power node and issue wireless computing power tasks, the computing power network will issue computing power task scheduling instructions. That is, the computing power network will make a comprehensive decision based on the user's business needs and the edge computing resource situation in the area. Through the extended interface with the SMO, it will issue computing power task scheduling instruction messages for SMO's subsequent computing power task deployment decisions.

[0132] The SMO network element deploys computing tasks, that is, based on the received computing task scheduling instruction message and referring to the local available wireless edge computing resources, it decides which computing nodes (cloud base stations) to deploy computing tasks.

[0133] Use the existing cloud management interface, namely the O2 interface, to send computing task deployment messages to the corresponding nodes.

[0134] When the computing task is completed, the cloud base station will feedback the message to the SMO network element. The SMO network element will determine that the computing task has been successfully completed. After the computing task has been successfully completed, it will be able to provide it with better data transmission services.

[0135] The SMO network element receives computing task requests from the computing network and deploys these wireless computing tasks across various cloud base stations. Based on the computing service requirements corresponding to the computing tasks, the SMO collaborates with the core network to establish enhanced QoS data flows, decomposing the computing task requirements and collaborating with the core network to establish enhanced QoS data flows with computing service quality assurance capabilities. Furthermore, the SMO leverages its near-real-time data collection and control capabilities for the RAN to maintain the optimal wireless computing data transmission solution in real time.

[0136] In another embodiment provided by the present invention, the computing task scheduling indication message includes task type, task scale, task requirements and task time limit.

[0137] The task type is an enumerated field that describes the computing operation or computing target required for the wireless computing task. Examples include visual recognition, image rendering, wireless data analysis, machine learning model training, and password cracking.

[0138] The task size is the floating-point computational power (FLOPs), which can be measured in millions of floating-point operations per second (MFLOPs), gigabytes of floating-point operations per second (GFLOPs), or terabytes of floating-point operations per second (TFLOPs). It describes the complexity or computational power of the task. The computing network evaluates and issues the task based on computation time, number of computational steps, and input data size.

[0139] Task requirements is an enumerated field that describes the hardware or computing capability requirements of the wireless computing task on the wireless computing platform. For example, a specific wireless protocol accelerator, GPU, or DPU may be required.

[0140] The task deadline, expressed in milliseconds or seconds, describes the requirement for the wireless computing platform to complete the task within a specified timeframe, or requires the wireless computing platform to reserve computing and transmission resources within a specified timeframe. This helps ensure that wireless computing tasks are completed on time and delivered to the computing network or task requester.

[0141] The task scheduling indication message including task type, task scale, task requirements and task time limit computing power can accurately describe the computing power requirements of the task, and thus accurately complete the establishment of the enhanced QoS data flow.

[0142] An embodiment of the present invention further provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the steps of implementing the method in the core network in the above embodiment.

[0143] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of implementing the method by the functional network element in the above embodiment.

[0144] It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A QoS feature enhancement method, characterized in that: The method is performed by a core network, and includes: Receive the calculation type QoS flow application message sent by the functional network element; Determine the configuration information of the calculated QoS flow based on the recommended value of the quality of service configuration information carried in the calculated QoS flow application message according to its own network resource data and load data; An enhanced QoS flow is established according to the configuration information.

2. The QoS feature enhancement method according to claim 1, characterized in that: The computing QoS flow application message carries the established PDU Session ID, or the IP to which the computing task is assigned, or indication information for initiating a new PDU Session.

3. The QoS feature enhancement method according to claim 1, wherein: The configuration information includes the maximum service response time, service bandwidth, service processing resource information, service redundancy information, service elasticity information and service load balancing information of wireless edge cloud computing.

4. The QoS feature enhancement method according to claim 1, wherein: The configuration information includes a maximum service response time; The maximum service response time is the maximum delay that the computing task can accept; The difference between the maximum service response time and the cloud computing service response time of the cloud base station is used to indicate the deployment of computing tasks in the cloud base station; The cloud computing service response time is the sum of the cloud computing delay of the cloud base station and the data packet delay of the cloud base station.

5. The QoS feature enhancement method according to claim 1, wherein: The configuration information includes service bandwidth; The service bandwidth is the data transmission bandwidth requirement between the terminal, edge device or application device and the cloud base station; The service bandwidth includes the bandwidth of the wireless air interface link and the bandwidth of the non-user plane cloud platform transmission link.

6. The QoS feature enhancement method according to claim 1, characterized in that: The configuration information includes service processing resource information; Service processing resource information is used to instruct the cloud base station to allocate dedicated computing resources for computing tasks in real time or evaluate the current shared computing resources; The service processing resource information includes but is not limited to the number of CPU cores, main frequency, architecture type, memory capacity and storage capacity.

7. The QoS feature enhancement method according to claim 1, characterized in that: The configuration information includes service redundancy information; The service redundancy information includes the computing power backup capabilities of other neighboring computing power areas within the service area where the cloud base station is located; The service redundancy information is used to instruct the cloud base station to apply for computing resources from other computing power neighboring cells within the service area and to deploy service capabilities.

8. The QoS feature enhancement method according to claim 1, wherein: The configuration information includes service resilience information; The service elasticity information is the ratio of the resource occupancy required for the maximum service processing capacity of the computing task to the resource occupancy required for the minimum service processing capacity, and is used to instruct the cloud base station to dynamically allocate or release computing resources according to the load requirements of the received computing power task.

9. The QoS feature enhancement method according to claim 1, wherein: The configuration information includes service load balancing information; The service load balancing information is the capability requirement for distributing computing tasks on multiple cloud base stations and deploying them in parallel, and is used to instruct the cloud base station to split the computing tasks.

10. A QoS feature enhancement method, characterized in that: The method is performed by a functional network element of a core network, and the method includes: Sending a computational QoS flow application message to the core network, and receiving configuration information determined by the core network based on the computational QoS flow application message; Perform capability collection and measurement on terminals and cloud base stations to obtain measurement data; An optimal wireless computing and data transmission scheme is formulated according to the measurement data and the configuration information.

11. The QoS feature enhancement method according to claim 10, characterized in that: The configuration information includes the maximum service response time, service bandwidth, service processing resource information, service redundancy information, service elasticity information and service load balancing information of wireless edge cloud computing.

12. The QoS feature enhancement method according to claim 10, characterized in that: The measurement data includes the link status between the terminal and the cloud base station, and the computing resource load and idle computing power of the cloud base station; The link status includes connectivity, bandwidth, delay, packet loss rate and delay jitter.

13. The QoS feature enhancement method according to claim 10, characterized in that: The method further comprises: Configure the optimal wireless computing data transmission solution to the cloud base station, perform data routing configuration at the base station node level, and configure the RAN protocol stack to complete data openness and routing configuration; The optimal wireless computing data transmission solution is synchronized to the core network and computing power network.

14. The QoS feature enhancement method according to claim 10, characterized in that: The optimal wireless computing data transmission solution includes a transmission mode and an optimal routing strategy; The transmission mode is one of traditional UPF forwarding, direct transmission of computing data within RAN, and computing task migration.

15. The QoS feature enhancement method according to claim 10, characterized in that: Before sending the computational QoS flow application message to the core network, the method further includes: Receive computing task scheduling instructions from the computing network through the extended interface; Generate a computing task deployment plan for deploying different computing tasks on different cloud base stations based on the computing task scheduling instruction message and combining it with locally available wireless edge computing resources; Use the cloud management interface to send computing task deployment messages to the corresponding cloud base station; After determining that the computing task has been successfully executed based on the message fed back by the cloud base station, a computing QoS flow application message is sent to the core network.

16. The QoS feature enhancement method according to claim 15, characterized in that: The computing task scheduling instruction message includes task type, task scale, task requirements and task time limit.

17. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to claims 1 to 9 are implemented.

18. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to claims 10 to 16 are implemented.

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