Method of pipeline contributor selection, method for pipeline contributor selection assistance and related apparatuses

Abstract

A method of pipeline contributor selection, a method for pipeline contributor selection assistance, and related apparatuses are provided. The method of pipeline contributor selection includes sending a candidate data pipeline topology map to a computing resource agent, wherein the candidate data pipeline topology map indicates candidate data pipeline contributors to support data pipeline operation according to data plane network data service request; receiving from the computing resource agent one or more recommended data pipeline paths that are derived based on computing resource consumption and / or performance information associated with the candidate data pipeline contributors; and based on the one or more recommended data pipeline paths, determining a group of data pipeline contributors to be selected for the data pipeline operation. This can facilitate the DPAC to select suitable data pipeline contributors.

Description

Atty. Dkt. No. 10085-01-0181-PCTMETHOD OF PIPELINE CONTRIBUTOR SELECTION, METHOD FOR PIPELINE CONTRIBUTOR SELECTION ASSISTANCE AND RELATED APPARATUSESCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 63 / 719,009, entitled “METHOD AND APPARATUS OF DATA PIPELINE CONTRIBUTOR DISCOVERY AND SELECTION,” filed on November 11, 2024, which is hereby incorporated in its entirety by this reference.TECHNICAL FIELD

[0002] The present application relates to wireless communication, and more particularly, to a method of pipeline contributor selection, a method for pipeline contributor selection assistance, and related apparatuses.BACKGROUND ART

[0001] In cellular wireless communication systems developed by the Third Generation Partnership Project (3GPP), user equipment (UE) is connected by a wireless link to a radio access network (RAN). The RAN includes a set of base stations (BSs) which provide wireless links to UEs located in cells covered by the base station and an interface to a core network (CN) which provides overall network control. The RAN and CN each conduct respective functions in relation to the overall network. The so-called 4G Long Term Evolution (LTE) system, namely, an Evolved Universal Mobile Telecommunication System Territorial Radio Access Network (E-UTRAN) has been developed for a mobile access network where one or more macro-cells are supported by a base station known as an eNodeB or eNB (evolved NodeB). Evolved from LTE, the so-called 5G or new radio (NR) systems where one or more cells are supported by a base station known as a gNB. Envisioned to succeed the current 5G networks, the 6G cellular system is the forthcoming generation of wireless communication technology.

[0002] The 3GPP 6G network is enhanced with a new type of communication plane, that is, the Data Plane. The Data Plane enables native Artificial Intelligence (Al) and native sensing demand to be collaborative “on-path processing” between different network entities (i.e., not only between point-to-point, but also between any-to-any) over “indiscriminate network topology” to support metadata carriage and processing, in which the metadata are forwarded based on data services and data pipeline identifiers.

[0003] The system architecture of the Data Pipeline support in Data Plane has been specified. The Data Pipeline is defined as a composite chain of activities which may cross the mobile network functional domains when manipulating the metadata that are collected from one or more data sources for supporting a data plane service. The Data Pipeline is formed by a group of Data Pipeline Contributors (DPCs) which support data plane functionalities such as Data Collection, Data Pre-processing, Data Labelling, ML Model training etc. In the context of the 3GPP mobile system, a Data Pipeline Contributor itself is a system functional entity that supports system operation by leveraging the metadata and ML model to navigate its local operation which is part of the data pipeline operation to support network service.

[0004] In the existing Data Pipeline system, the Data Plane Access Controller (DPAC) needs to select appropriate pipeline contributors to participate in the pipeline operation. However, the DPAC lacks sufficient information about the available computing resources and performance across different network entities. As a result, the DPAC cannot efficiently determine which contributors are suitable for a given pipeline operation, leading to suboptimal contributor selection, increased resource consumption, and degraded system performance. Therefore, there is a need for a mechanism that enables the DPAC to select a proper group of contributors for the data pipeline operation.SUMMARY

[0005] An object of the present application is to propose a method of pipeline contributor selection, a method for pipeline contributor selection assistance, and related apparatus, which can solve issues in theAtty. Dkt. No. 10085-01-0181-PCT relevant art, select suitable data pipeline contributors, enhance overall system performance, reduce wasted resource consumption, and / or provide high reliability.

[0006] In a first aspect of the present application, provided is a method of pipeline contributor selection, performed by a first network node, the method including sending, by the first network node, a candidate data pipeline topology map to a computing resource agent, wherein the candidate data pipeline topology map indicates candidate data pipeline contributors to support data pipeline operation according to data plane network data service request; receiving from the computing resource agent one or more recommended data pipeline paths that are derived based on computing resource consumption and / or performance information associated with the candidate data pipeline contributors; and based on the one or more recommended data pipeline paths, determining a group of data pipeline contributors to be selected for the data pipeline operation.

[0007] In a second aspect of the present application, provided is a method for pipeline contributor selection assistance, performed by a computing resource agent, the method including receiving, by the computing resource agent, a candidate data pipeline topology map from a first network node, wherein the candidate data pipeline topology map indicates candidate data pipeline contributors to support data pipeline operation according to data plane network data service request; estimating computing resource consumption and / or performance information associated with the candidate data pipeline contributors to derive one or more recommended data pipeline paths; and sending the one or more recommended data pipeline paths to the first network node.

[0008] In a third aspect of the present application, provided is a first network node, including: a first sending module, configured to send a candidate data pipeline topology map to a computing resource agent, wherein the candidate data pipeline topology map indicates candidate data pipeline contributors to support data pipeline operation according to data plane network data service request; a first receiving module, configured to receive from the computing resource agent one or more recommended data pipeline paths that are derived based on computing resource consumption and / or performance information associated with the candidate data pipeline contributors; and a determination module, configured to determine, based on the one or more recommended data pipeline paths, a group of data pipeline contributors to be selected for the data pipeline operation.

[0009] In a fourth aspect of the present application, provided is a computing resource agent, including a second receiving module, configured to receive a candidate data pipeline topology map from a first network node, wherein the candidate data pipeline topology map indicates candidate data pipeline contributors to support data pipeline operation according to data plane network data service request; an estimation module, configured to estimate computing resource consumption and / or performance information associated with the candidate data pipeline contributors to derive one or more recommended data pipeline paths; and a second sending module, configured to send the one or more recommended data pipeline paths to the first network node.

[0010] In a fifth aspect of the present application, provided is a first network node, including at least one memory configured to store program instructions; and at least one processor configured to execute the program instructions, which cause the at least one processor to execute the method according to the first aspect described above.

[0011] In a sixth aspect of the present application, provided is a computing resource agent, including at least one memory configured to store program instructions; and at least one processor configured to execute the program instructions, which cause the at least one processor to execute the method according to the second aspect described above.

[0012] In a seventh aspect of the present application, a non-transitory machine-readable storage medium has stored thereon instructions that, when executed by a computer, cause the computer to perform the above method.

[0013] In an eighth aspect of the present application, a chip includes a processor, configured to call and run a computer program stored in a memory, to cause a device in which the chip is installed to execute the above method.Atty. Dkt. No. 10085-01-0181-PCT

[0014] In a ninth aspect of the present application, a computer readable storage medium, in which a computer program is stored, causes a computer to execute the above method.

[0015] In a tenth aspect of the present application, a computer program product includes a computer program, and the computer program causes a computer to execute the above method.

[0016] In an eleventh aspect of the present application, a computer program causes a computer to execute the above method.DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or related art, the following figures that will be described in the embodiments are briefly introduced. It is obvious that the drawings are merely some embodiments of the present application, a person having ordinary skill in this field can obtain other figures according to these figures without paying the premise.

[0018] FIG. 1 is a schematic diagram illustrating a high-level architecture of the Data Plane.

[0019] FIG. 2 is a schematic diagram illustrating Data Pipeline Communication Flows.

[0020] FIG. 3 is a flowchart of information exchange between DPAC and Computing Resource Agent according to an embodiment of the present application.

[0021] FIG. 4 is a flowchart of a method of pipeline contributor selection implemented by a first network node according to an embodiment of the present application.

[0022] FIG. 5 is a schematic diagram illustrating an example of Candidate Data Pipeline Topology Map according to an embodiment of the present application.

[0023] FIG. 6 is a schematic diagram illustrating an example of Recommended Data Pipeline Path according to an embodiment of the present application.

[0024] FIG. 7 is a flowchart of data pipeline contributor discovery and selection performed by DPAC according to an embodiment of the present application.

[0025] FIG. 8 is a flowchart of a method for pipeline contributor selection assistance implemented by a Computing Resource Agent according to an embodiment of the present application.

[0026] FIG. 9 is a block diagram of a first network node according to an embodiment of the present application.

[0027] FIG. 10 is a block diagram of a Computing Resource Agent according to an embodiment of the present application.DETAILED DESCRIPTION OF EMBODIMENTS

[0028] Embodiments of the disclosure are described in detail with the technical matters, structural features, achieved objects, and effects with reference to the accompanying drawings as follows. Specifically, the terminologies in the embodiments of the present application are merely for describing the purpose of the certain embodiment, but not to limit the disclosure.

[0029] In this document, the term “ / ” should be interpreted to indicate “and / or.” A combination such as “at least one of A, B, or C,” “one or more of A, B, or C,” “at least one of A, B, and C,” “one or more of A,B, and C,” or “A, B, and / or C” may be A only, B only, C only, A and B, A and C, B and C, or A and B andC, where any combination may contain one or more members of A, B, or C.

[0030] FIG. 1 illustrates an example of Data Plane architecture applicable to 3GPP 6G network system. The architecture is generally divided into a Control Plane 10, a User Plane 20 and a Data Plane 30, and includes a User Equipment (UE) 40 and multiple functional entities including Radio Access Network (RAN) / Transport Network (TN) functions 50, Application Function 60, Management Functions 70, and other core functions 80.

[0031] The Control Plane 10, shown at the upper portion of FIG. 1, is responsible for the signaling and control interactions among different functional entities. In particular, the Control Plane 10 connects to the Application Function 60 and the Management Functions 70, and interfaces with the Data Plane 30 via a Data Plane Access Controller (DPAC) 310. Through the Control Plane 10, network policies, orchestrationAtty. Dkt. No. 10085-01-0181-PCT commands, and management instructions can be transmitted to configure and control the operation of the Data Plane 30.

[0032] The User Plane 20, depicted at the lower portion of FIG. 1 , is responsible for the actual data traffic delivery between the UE 40 and the Network Functions. The User Plane 20 connects the UE 40 to the RAN / TN functions 50 and the core functions 80, enabling end-to-end data transmission.

[0033] The Data Plane 30 is illustrated as a functional block positioned between the Control Plane 10 and the User Plane 20. The Data Plane 30 enables efficient and intelligent handling of network data. In one implementation, the Data Plane 30 includes the DPAC 310 and a Data Plane Repository (DPR) 320. The DPAC 310 includes a Data Collection Function 311, a Data Processing Function 312, and a Data Service Orchestration and Management Function 313.

[0034] The Data Collection Function 311 is configured to collect various types of data from multiple sources, such as user plane traffic information, RAN performance data, and core network operational data. The Data Processing Function 312 is configured to perform processing, analysis, or transformation on the collected data, for example for the purpose of enabling intelligent network operation, service optimization, or data exposure to external applications. The Data Service Orchestration and Management Function 313 is responsible for managing data services, orchestrating data flows, and coordinating the operation of data collection and processing functions in accordance with network policies. The DPR 320 can aid the DPAC to select and compose appropriate data plane functions to form a data pipeline that meets service requirements.

[0035] This architecture supports data-centric network operation and enables advanced functions such as real-time analytics, AI / ML-based network optimization, and data exposure for third-party services.

[0036] FIG. 2 illustrates Data Pipeline Communication Flows, which enables closed-loop Data Pipeline operations. As shown in FIG. 2, various Network Data Service Applications (e.g., Al, autonomous driving, smart energy) rely on the Data Plane Network Data Service. The distributed Data Plane Orchestration / Control (DPAC) and Repository (DPR) is responsible for orchestrating data flows, managing network data services, and interacting with distributed computing resources. The data pipeline includes several processing entities, including Data Source, Machine Learning (ML) Training Host, and Data Sink. The closed-loop operation allows feedback from the Data Sink or application layer to be used to adjust the data collection or processing parameters dynamically, ensuring real-time optimization and adaptive learning.

[0037] The present application defines how the Data Plane Access Controller (DPAC) orchestrates the discovery and selection of the Data Pipeline Contributor to participate in the end-to-end data pipeline operation.

[0038] An overview on the data pipeline operation for the Pipeline Contributor Discovery and Selection is provided as follows.

[0039] To enable the DPAC to select the proper pipeline contributors to support the pipeline operation, the DPAC uses the assistance of the Computing Resource Agent which is the main contact for the network entities which inquire the computing resource consumption and performance information for the system operation. Such information is necessary for Computing Resource Agent to assist the DPAC to determine the proper group of contributors to be selected to support the pipeline operation.

[0040] In order to allow the DPAC to provide sufficient information for Computing Resource Agent to provide the assistance, the present application introduces two following descriptors to support the information exchange between the DPAC and the Computing Resource Agent and they are:Candidate Data Pipeline Topology Map, andRecommended Data Pipeline Path

[0041] As shown in FIG. 3, the DPAC provides the Candidate Data Pipeline Topology Map to the Computing Resource Agent in Step SI, and the Computing Resource Agent derives the Recommended Data Pipeline Path based on computing resource information considerations and returns the Recommended Data Pipeline Path to the DPAC in Step S2.Atty. Dkt. No. 10085-01-0181-PCT

[0042] Note that, the internal logic of the Computing Resource Agent and how Computing Resource Agent obtains the computing resources information may or may not be within the scope of the present application.

[0043] FIG. 4 is a flowchart of a method of pipeline contributor selection 100 implemented by a first network node according to an embodiment of the present application. Referring to FIG. 4, the method of pipeline contributor selection 100 includes the following steps.

[0044] In Step S102, the first network node (e.g., a data control network node, or DPAC in particular) sends a Candidate Data Pipeline Topology Map to a Computing Resource Agent. The Candidate Data Pipeline Topology Map indicates candidate data pipeline contributors to support data pipeline operation according to Data Plane Network Data Service request. The Candidate Data Pipeline Topology Map can enable the DPAC to communicate its computing-resource requirements to the Computing Resource Agent. Based on these requirements, the Computing Resource Agent can recommend which data pipeline contributors should be selected to support the pipeline operation.

[0045] Before sending the Candidate Data Pipeline Topology Map to the Computing Resource Agent, the DPAC may orchestrate the data pipeline operation based on Data Plane Service parameters, which are derived from the Data Plane Network Data Service request. The DPAC obtains these parameters by referring to the Data Plane Network Data Service Descriptor in the request message sent by a Network Data Plane Service Supplicant (DPSS). The derived Data Plane Service parameters provide essential information by which the DPAC controls and coordinates the data pipeline operation.

[0046] Before sending the Candidate Data Pipeline Topology Map to the Computing Resource Agent, the DPAC may discover and identify the candidate data pipeline contributors, which are capable of supporting Network Data Service as per requested in the Data Plane Network Data Service request, via at least one of a Network Repository Function (NRF) and a Service Communication Proxy (SCP) to verify their legitimate access to the meta dataset and / or ML model(s), for example.

[0047] FIG. 5 illustrates an example of information provided in the Candidate Data Pipeline Topology Map. The purpose of the Candidate Data Pipeline Topology Map is to allow the DPAC to provide the computing resources related requirements to Computing Resource Agent so that it can offer recommendation to DPAC to select the proper data pipeline contributors to support pipeline operation while complying to the KPIs of the target network data service.

[0048] The information in the Candidate Data Pipeline Topology Map by DPAC is mainly extracted and / or derived from the Data Plane Network Data Service Descriptor. The Data Plane Network Data Service Descriptor is provided by the supplicant (e.g., the DPSS) which initiates the Data Plane Network Data Service request.

[0049] The Candidate Data Pipeline Topology Map may include at least one of the following parameters: Contributor Execution Order, Candidate for Data Pipeline Contributor, Previous Hop, Next Hop, Data Operation, and Data Type. In addition, the Candidate Data Pipeline Topology Map may further include at least one of the following parameters: Service Type, Service Initiation Trigger, Service Termination Trigger, Service Pipeline Type, Service Area, and Service Performance Requirements. For example, the descriptions of the information in the topology map are as follows:

[0050] Contributor Execution Order -It describes the order of the contributor operation being executed within the pipeline operation.

[0051] Note that, more than one contributor operation could be executed in parallel with the other.

[0052] Candidate for Data Pipeline Contributor - The network function that is selected by the DPAC which could be the candidate to support the data pipeline operation according to the target Data Plane Network Data Service request.

[0053] Note that, more than one candidate contributor can be considered for the same data pipeline operation.

[0054] Previous Hop - The contributor operation which happens prior to the current contributor operation.Atty. Dkt. No. 10085-01-0181-PCT

[0055] Note that, in the case of the cross domain scenario, the previous hop of the subsequent domain may include the gateway address of the preceding domain.

[0056] Next Hop - The contributor operation which happens after the current contributor operation.

[0057] Note that, in the case of the cross domain scenario, the next hop of the preceding domain may include the gateway address of the subsequent domain.

[0058] Data Operation - It describes type of data operation (e.g., data collection, ML training, Data Analytics etc.) and how the data is used and being preprocess according to instructions (e.g., Data Cleaning, Formatting, Normalizing, Aggregating, Filtering etc.) in the contributor operation.

[0059] Data Type - Forms of data - e.g., Numeric, Categorical, Time Series and Text.

[0060] Numeric data: This type of data consists of numbers and can include continuous values (such as prices or temperatures) or discrete values (such as counts or rankings). Numeric data can be used as input or output in machine learning algorithms

[0061] Categorical data: This type of data consists of categories or labels, such as names, types, or categories. Categorical data can be used as input or output in machine learning algorithms, but it may be converted into a numerical form in order to be used by certain algorithms.

[0062] Time series data: This type of data consists of measurements taken at regular intervals over a period of time. Time series data is often used in machine learning algorithms for tasks such as forecasting or trend analysis.

[0063] Text data: This type of data consists of written or spoken words and can include things like emails, social media posts, or customer reviews. Text data is often used in machine learning algorithms for tasks such as natural language processing or sentiment analysis.

[0064] Encrypted - It is an indication whether the data is encrypted or not.

[0065] Service Type - It describes the service profile of the Data Plane Network Data Services such as Autonomous Driving Congestion Avoidance, Smart Meter Agriculture Water irrigation, Network Congestion Avoidance etc.. For the given service profile, the set of network features, policies, data and workflows will be specified which dictates the operations of the data pipeline.

[0066] Service Initiation Trigger - It specifies trigger mechanism which is used to initiate the data pipeline operation, e.g., Time-of-Data, Sensor triggered (e.g., Mobility, Network Parameter Thresholds (e.g., Explicit congestion notification (ECN) etc.)

[0067] Service Termination Trigger: It specifies trigger mechanism which is used to terminate the data pipeline operation, e.g., Time-of-Data, Sensor triggered (e.g., UE proximity to target location etc. ), ML Training duration etc.

[0068] Service Pipeline Type: Type of Al-related service operation, e.g. ML training, ML inference, Data Analytics

[0069] Service Area: Topological Service Area (e.g., Cell ID, Tracking Area, PLMN ID etc.)

[0070] Service Performance Requirements: End-to-end (E2E) pipeline performance requirements, e.g., Per Round Trip, E2E Throughput, E2E Reliability, E2E Energy Consumption etc.

[0071] In Step S104, the first network node (e.g., a data control network node, or DPAC in particular) receives from the Computing Resource Agent one or more Recommended Data Pipeline Paths that are derived based on computing resource consumption and / or performance information associated with the candidate data pipeline contributors.

[0072] If more than one Recommended Data Pipeline Paths are available, the Recommended Data Pipeline Paths may be prioritized.

[0073] FIG. 6 illustrates an example of the Recommended Data Pipeline Path. The purpose of the Recommended Data Pipeline Path is for the Computing Resource Agent to provide the suggestion to DPAC for the best topology communication path for the pipeline contributors to support the data pipeline operation according to the computing requirements that were provided by the DPAC. The one or more Recommended Data Pipeline Paths may include at least one of the following parameters:

[0074] Data Pipeline Path Recommendation - It presents the topology communication path among the data pipeline contributors. If there is more than one alternative path, they may be prioritized.Atty. Dkt. No. 10085-01-0181-PCT

[0075] Data Pipeline Computing Performance - It presents the estimated computing performance according to the requirements that were specified in the Candidate Data Pipeline Topology Map.

[0076] For example, as shown in FIG. 6, the Data Pipeline Computing Performance parameter may include at least one of the following parameters: end-to-end (E2E) latency, E2E throughput, E2E reliability, and E2E energy consumption.

[0077] In Step S 106, based on the one or more Recommended Data Pipeline Paths, the first network node (e.g., a data control network node, or DPAC in particular) determines a group of data pipeline contributors to be selected for the data pipeline operation.

[0078] In order to select the data pipeline contributors for the data pipeline operation, the DPAC may first determine whether a target data pipeline contributor in a recommended path is willing or is available to participate in the data pipeline operation. To achieve this, the DPAC may send a request message to each of target data pipeline contributors associated with the one or more Recommended Data Pipeline Paths for seeking commitment to participate in the data pipeline operation. Then, based on a response message from each of the target data pipeline contributors, the DPAC can determine or select a Data Pipeline Path for the data pipeline operation from the one or more Recommended Data Pipeline Paths. Note that, the data pipeline contributors eventually determined or selected by the DPAC are included in the selected Data Pipeline Path.

[0079] The DPAC may send a notification to the Computing Resource Agent to indicate the determined or selected data pipeline contributors as a feedback for the Computing Resource Agent to take this into considerations in generating the Recommended Data Pipeline Path next time.

[0080] In the method of pipeline contributor selection 100 of the present application, the DPAC sends a Candidate Data Pipeline Topology Map to a Computing Resource Agent, receives from the Computing Resource Agent one or more Recommended Data Pipeline Paths that are derived based on computing resource consumption and performance information associated with the candidate data pipeline contributors, and determines, based on the one or more Recommended Data Pipeline Paths, a group of data pipeline contributors to be selected for the data pipeline operation. This facilitates the DPAC to select suitable data pipeline contributors, overall system performance is enhanced, and wasted resource consumption is reduced.

[0081] FIG. 7 is a flowchart of data pipeline contributor discovery and selection performed by DPAC according to an embodiment of the present application. Referring to FIG. 7, further details on how the DPAC performs the discovery and selection of the data pipeline contributors with the assistance of the Computing Resource Agent are described in more details below:

[0082] In Step S10, when a network data service application (e.g., V2X server) initiates Al-related service application, for example, Al -assisted Sensing support for V2X auto-driving navigation, Al -assisted data analytics for RAN base station optimization for automatic inter-band load balancing, automatic cell switch-off for power saving etc., which eventually invokes the Data Plane Network Service request message, DPAC refers to the Data Plane Network Data Service Descriptor provided by the service application to perform the service mapping and to derive the Data Plane Service parameters.

[0083] The Data Plane Service parameter is necessary information to be used by DPAC to orchestrate the data pipeline operation.

[0084] In Step S20, DPAC verifies the identity of the supplicant of the Data Plane Network Service request against the serving MNO’s (i.e., Mobile Network Operator’s) network capability and policy as well as the local regional data protection regulation and local environment.

[0085] In Step S30, if Step S20 above is verified, based on Network Data Service Operation Type and Data Workflow Scheme provided in the Data Plane Network Data Service Descriptors, DPAC discovers and identifies the candidate data pipeline contributors which may be capable of supporting the requested Network Data Service via the support of Network Repository Function (NRF) and / or Service Communication Proxy (SCP) to verify their legitimate access to the meta dataset and / or ML model(s) by sending and receiving the Nnrf_NFDiscovery_Request and Response, respectively.Atty. Dkt. No. 10085-01-0181-PCT

[0086] Note that, it is assumed all the network functions which are capable of supporting the network data service should have registered with NRF for its capability and it should have registered with the DPAC to certify its authenticity.

[0087] In Step S40, based on the candidate list of the data pipeline contributors, DPAC constructs the Candidate Data Pipeline Topology Map (see FIG. 5) to be sent to the Computing Resource Agent in Nx ComputingRsrcQueryRequest message to request for the Recommended Data Pipeline Path (see FIG. 6) to support the requested Data Plane Network Service request.

[0088] Note that, in case of the cross domain data pipeline operation, each domain is responsible for its own respective contributor discovery and selection. Furthermore, each domain is responsible for its aggregated computing performance determination. It is expecting that, there is pre-determined KPI for the cross domain connection. It is expecting the DPAC of the proceeding domain to indicate such cross domain connection in the “previous hop” of the first hop of the Candidate Data Pipeline Topology Map by including the gateway’s IP address. Based on such information, the Computing Resource Agent will take such information into consideration when estimating the aggregated computing resource consumption.

[0089] In Step S50, Computing Resource Agent provides DPAC the one or more alternatives of the Recommended Data Pipeline Path (see FIG. 6) in Nx ComputingRsrcQueryResponse message, if feasible, after it examines and evaluates the computing resource and performance requirements for the requested data pipeline operation. If more than one options are available, the options may be prioritized.

[0090] In Step S60, after receiving the Recommended Data Pipeline Path from the Computing Resource Agent, DPAC takes the recommendations into considerations and then decides on the Data Pipeline Id for the projected data pipeline operation. DPAC sends Data Pipeline Service subscription request captured in the DPAC DataPipelineServiceSubscribe Request message, which includes the respective Data Plane Service Request Parameters destined for the data pipeline contributor, seeks the commitment to participate in the projected data pipeline operation. More than one subscription request may be sent to the target data pipeline contributor for the given data pipeline operation.

[0091] In Step S70, after receiving all the responses captured in different DPAC_DataPipelineServiceSubscribe_Response messages from the target data pipeline contributors, DPAC determines the Selected Data Pipeline Path.

[0092] In Step S80, DPAC sends the notifications in DPAC DataPipelineServiceSubscribe Notify messages to all the target data pipeline contributors to inform them for which the contributors are being selected to participate in the target data pipeline operation.

[0093] In Step S90, the Computing Resource Agent will also be notified by receiving Nx_ComputingRsrcReservationNotify for the final decision of the list of selected data pipeline contributors in order to update the expected Computing Resource usage status.

[0094] FIG. 8 is a flowchart of a method for pipeline contributor selection assistance 200 implemented by a Computing Resource Agent according to an embodiment of the present application. Referring to FIG. 8, the method for pipeline contributor selection assistance 200 includes the following steps. In Step S202, the Computing Resource Agent receives a Candidate Data Pipeline Topology Map from a first network node (e.g., a data control network node, or DPAC in particular), wherein the Candidate Data Pipeline Topology Map indicates candidate data pipeline contributors to support data pipeline operation according to Data Plane Network Data Service request. In Step S204, the Computing Resource Agent estimates computing resource consumption and / or performance information associated with the candidate data pipeline contributors to derive one or more Recommended Data Pipeline Paths. In Step S206, the Computing Resource Agent sends the one or more Recommended Data Pipeline Paths to the DPAC. This can assist the DPAC to select suitable data pipeline contributors, overall system performance is enhanced, and wasted resource consumption is reduced. Other details of the method 200 may be referred to the method 100 described above and are not repeated herein.

[0095] FIG. 9 is a block diagram of a first network node 1000 according to an embodiment of the present application. As shown in FIG. 9, the first network node (e.g., a data control network node, or DPAC in particular) 1000 includes a first sending module 1001, a first receiving module 1002, and a determinationAtty. Dkt. No. 10085-01-0181-PCT module 1003. The first sending module 1001 is configured to send a Candidate Data Pipeline Topology Map to a Computing Resource Agent, wherein the Candidate Data Pipeline Topology Map indicates candidate data pipeline contributors to support data pipeline operation according to Data Plane Network Data Service request. The first receiving module 1002 is configured to receive from the Computing Resource Agent one or more Recommended Data Pipeline Paths that are derived based on computing resource consumption and / or performance information associated with the candidate data pipeline contributors. The determination module 1003 is configured to determine, based on the one or more Recommended Data Pipeline Paths, a group of data pipeline contributors to be selected for the data pipeline operation. This facilitates the first network node to select suitable data pipeline contributors, overall system performance is enhanced, and wasted resource consumption is reduced. Other details of the first network node 1000 may be referred to the method 100 described above and are not repeated herein.

[0096] In some embodiments of the first network node 1000, the first network node further includes an orchestrating module, configured to orchestrate the data pipeline operation according to Data Plane Service parameters derived from the Data Plane Network Data Service request.

[0097] In some embodiments of the first network node 1000, the first network node further includes a discovering and identifying module, configured to discover and identify the candidate data pipeline contributors, which are capable of supporting Network Data Service as per requested in the Data Plane Network Data Service request, via at least one of a Network Repository Function (NRF) and a Service Communication Proxy (SCP).

[0098] In some embodiments of the first network node 1000, the Candidate Data Pipeline Topology Map includes at least one of the following parameters: Contributor Execution Order, Candidate for Data Pipeline Contributor, Previous Hop, Next Hop, Data Operation, and Data Type.

[0099] In some embodiments of the first network node 1000, the Candidate Data Pipeline Topology Map further includes at least one of the following parameters: Service Type, Service Initiation Trigger, Service Termination Trigger, Service Pipeline Type, Service Area, and Service Performance Requirements.

[0100] In some embodiments of the first network node 1000, the one or more Recommended Data Pipeline Paths includes at least one of the following parameters:

[0101] Data Pipeline Path Recommendation presenting topology communication path among the data pipeline contributors; and

[0102] Data Pipeline Computing Performance presenting estimated computing performance.

[0103] In some embodiments of the first network node 1000, the Data Pipeline Computing Performance includes at least one of the following parameters: end-to-end (E2E) latency, E2E throughput, E2E reliability, and E2E energy consumption.

[0104] In some embodiments of the first network node 1000, the Recommended Data Pipeline Paths are prioritized.

[0105] In some embodiments of the first network node 1000, the data pipeline operation is a cross domain data pipeline operation, and each domain is responsible for its own respective contributor selection.

[0106] In some embodiments of the first network node 1000, each domain is responsible for its aggregated computing performance determination.

[0107] In some embodiments of the first network node 1000, the determination module is further configured to send a request message to each of target data pipeline contributors associated with the one or more Recommended Data Pipeline Paths for seeking commitment to participate in the data pipeline operation; and select a Data Pipeline Path from the one or more Recommended Data Pipeline Paths according to a response message from each of the target data pipeline contributors, wherein the determined group of data pipeline contributors is associated with the selected Data Pipeline Path.

[0108] In some embodiments of the first network node 1000, the first network node further includes a notification sending module, configured to notify the Computing Resource Agent of the determined group of data pipeline contributors as a feedback.

[0109] FIG. 10 is a block diagram of a Computing Resource Agent 2000 according to an embodiment of the present application. As shown in FIG. 10, the Computing Resource Agent 2000 includes a secondAtty. Dkt. No. 10085-01-0181-PCT receiving module 2001, an estimation module 2002, and a second sending module 2003. The second receiving module 2001 is configured to receive a Candidate Data Pipeline Topology Map from a first network node (e.g., a data control network node, or DPAC in particular), wherein the Candidate Data Pipeline Topology Map indicates candidate data pipeline contributors to support data pipeline operation according to Data Plane Network Data Service request. The estimation module 2002 is configured to estimate computing resource consumption and / or performance information associated with the candidate data pipeline contributors to derive one or more Recommended Data Pipeline Paths. The second sending module 2003 is configured to send the one or more Recommended Data Pipeline Paths to the first network node. This can assist the first network node to select suitable data pipeline contributors, overall system performance is enhanced, and wasted resource consumption is reduced. Other details of the Computing Resource Agent 2000 may be referred to the method 100 described above and are not repeated herein.

[0110] In some embodiments of the Computing Resource Agent 2000, the candidate data pipeline contributors are capable of supporting Network Data Service as per requested in the Data Plane Network Data Service request.[oni] In some embodiments of the Computing Resource Agent 2000, the Candidate Data Pipeline Topology Map includes at least one of the following parameters: Contributor Execution Order, Candidate for Data Pipeline Contributor, Previous Hop, Next Hop, Data Operation, and Data Type.

[0112] In some embodiments of the Computing Resource Agent 2000, the Candidate Data Pipeline Topology Map further includes at least one of the following parameters: Service Type, Service Initiation Trigger, Service Termination Trigger, Service Pipeline Type, Service Area, and Service Performance Requirements.

[0113] In some embodiments of the Computing Resource Agent 2000, the one or more Recommended Data Pipeline Paths includes at least one of the following parameters:

[0114] Data Pipeline Path Recommendation presenting topology communication path among the data pipeline contributors; and

[0115] Data Pipeline Computing Performance presenting estimated computing performance.

[0116] In some embodiments of the Computing Resource Agent 2000, the Data Pipeline Computing Performance includes at least one of the following parameters: end-to-end (E2E) latency, E2E throughput, E2E reliability, and E2E energy consumption.

[0117] In some embodiments of the Computing Resource Agent 2000, the Recommended Data Pipeline Paths are prioritized.

[0118] In some embodiments of the Computing Resource Agent 2000, the data pipeline operation is a cross domain data pipeline operation, and each domain is responsible for its own respective contributor selection.

[0119] In some embodiments of the Computing Resource Agent 2000, each domain is responsible for its aggregated computing performance determination.

[0120] In some embodiments of the Computing Resource Agent 2000, the Computing Resource Agent further includes a notification receiving module, configured to receive from the first network node a notification of data pipeline contributors selected by the first network node.

[0121] In some embodiments, the first network node (e.g., a data control network node, or DPAC in particular) includes at least one memory configured to store program instructions; and at least one processor configured to execute the program instructions, which cause the at least one processor to send a Candidate Data Pipeline Topology Map to a Computing Resource Agent, wherein the Candidate Data Pipeline Topology Map indicates candidate data pipeline contributors to support data pipeline operation according to Data Plane Network Data Service request, receive from the Computing Resource Agent one or more Recommended Data Pipeline Paths that are derived based on computing resource consumption and / or performance information associated with the candidate data pipeline contributors, and determine, based on the one or more Recommended Data Pipeline Paths, a group of data pipeline contributors to be selected for the data pipeline operation. This facilitates the first network node to select suitable data pipeline contributors, overall system performance is enhanced, and wasted resource consumption is reduced. ioAtty. Dkt. No. 10085-01-0181-PCT

[0122] In some embodiments, the Computing Resource Agent includes at least one memory configured to store program instructions; and at least one processor configured to execute the program instructions, which cause the at least one processor to receive a Candidate Data Pipeline Topology Map from a first network node (e.g., a data control network node, or DPAC in particular), wherein the Candidate Data Pipeline Topology Map indicates candidate data pipeline contributors to support data pipeline operation according to Data Plane Network Data Service request, estimate computing resource consumption and / or performance information associated with the candidate data pipeline contributors to derive one or more Recommended Data Pipeline Paths, and send the one or more Recommended Data Pipeline Paths to the first network node. This can assist the first network node to select suitable data pipeline contributors, overall system performance is enhanced, and wasted resource consumption is reduced.

[0123] The embodiment of the present application further provides a computer readable storage medium for storing a computer program. The computer readable storage medium enables a computer to execute corresponding processes implemented in each of the methods of the embodiments of the present application. For brevity, details will not be described herein again.

[0124] The embodiment of the present application further provides a computer program product including computer program instructions. The computer program product enables a computer to execute corresponding processes implemented in each of the methods of the embodiments of the present application. For brevity, details will not be described herein again.

[0125] The embodiment of the present application further provides a computer program. The computer program enables a computer to execute corresponding processes implemented in each of the methods of the embodiments of the present application. For brevity, details will not be described herein again.

[0126] The description of above device embodiments is similar to the description of above method embodiments, having beneficial effects similar to the method embodiments. For technical details not disclosed in the device embodiments of the present application, please refer to the description of the method embodiments of the present application for the purpose of understanding.

[0127] Those of skill in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0128] Further, those of skill in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

[0129] The methods, sequences and / or algorithms described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor.

[0130] It should be understood that any embodiments disclosed herein as being “non-transitory” do not exclude any physical storage medium, but rather exclude only the interpretation that the medium can be construed as a transitory propagating signal.Atty. Dkt. No. 10085-01-0181-PCT

[0131] The elements and components of an embodiment of the invention may be physically, functionally and logically implemented in any suitable way. Indeed, the functionality may be implemented in a single unit, in a plurality of units or as part of other functional units. Although the present invention has been described in connection with some embodiments, it is not intended to be limited to the specific form set forth herein. Rather, the scope of the present invention is limited only by the accompanying claims. Additionally, although a feature may appear to be described in connection with particular embodiments, one skilled in the art would recognize that various features of the described embodiments may be combined in accordance with the invention. In the claims, the term ‘including’ does not exclude the presence of other elements or steps.

[0132] Furthermore, although individually listed, a plurality of means, elements or method steps may be implemented by, for example, a single unit or processor. Additionally, although individual features may be included in different claims, these may possibly be advantageously combined, and the inclusion in different claims does not imply that a combination of features is not feasible and / or advantageous. Also, the inclusion of a feature in one category of claims does not imply a limitation to this category, but rather indicates that the feature is equally applicable to other claim categories, as appropriate.

[0133] Furthermore, the order of features in the claims does not imply any specific order in which the features must be performed and in particular the order of individual steps in a method claim does not imply that the steps must be performed in this order. Rather, the steps may be performed in any suitable order. In addition, singular references do not exclude a plurality. Thus, references to ‘a’, ‘an’, ‘first’, ‘second’, etc. do not preclude a plurality.

[0134] Above all, while the preferred embodiments of the present application have been illustrated and described in detail, various modifications and alterations can be made by persons of ordinary skill in the art. The embodiment of the present application is therefore described in an illustrative but not restrictive sense. It is intended that the present application should not be limited to the particular forms as illustrated, and that all modifications and alterations which maintain the spirit and realm of the present application are within the scope as defined in the appended claims.

Claims

Atty. Dkt. No. 10085-01-0181-PCTWhat is claimed is:

1. A method of pipeline contributor selection, performed by a first network node, the method comprising: sending, by the first network node, a candidate data pipeline topology map to a computing resource agent, wherein the candidate data pipeline topology map indicates candidate data pipeline contributors to support data pipeline operation according to data plane network data service request; receiving from the computing resource agent one or more recommended data pipeline paths that are derived based on computing resource consumption and / or performance information associated with the candidate data pipeline contributors; and based on the one or more recommended data pipeline paths, determining a group of data pipeline contributors to be selected for the data pipeline operation.

2. The method of claim 1, wherein before the step of sending the candidate data pipeline topology map to the computing resource agent, the method further comprises: orchestrating the data pipeline operation according to data plane service parameters derived from the data plane network data service request.

3. The method of claim 1 or 2, wherein before the step of sending the candidate data pipeline topology map to the computing resource agent, the method further comprises: discovering and identifying the candidate data pipeline contributors, which are capable of supporting network data service as per requested in the data plane network data service request, via at least one of a network repository function (NRF) and a service communication proxy (SCP).

4. The method of any of claims 1 to 3, wherein the candidate data pipeline topology map comprises at least one of the following parameters: contributor execution order, candidate for data pipeline contributor, previous hop, next hop, data operation, and data type.

5. The method of claim 4, wherein the candidate data pipeline topology map further comprises at least one of the following parameters: service type, service initiation trigger, service termination trigger, service pipeline type, service area, and service performance requirements.

6. The method of any of claims 1 to 5, wherein the one or more recommended data pipeline paths comprises at least one of the following parameters: data pipeline path recommendation presenting topology communication path among the data pipeline contributors; and data pipeline computing performance presenting estimated computing performance.

7. The method of claim 6, wherein the data pipeline computing performance comprises at least one of the following parameters: end-to-end (E2E) latency, E2E throughput, E2E reliability, and E2E energy consumption.

8. The method of any of claims 1 to 7, wherein the recommended data pipeline paths are prioritized.

9. The method of any of claims 1 to 8, wherein the data pipeline operation is a cross domain data pipeline operation, and each domain is responsible for its own respective contributor selection.

10. The method of any of claims 1 to 9, wherein each domain is responsible for its aggregated computing performance determination.

11. The method of any of claims 1 to 10, wherein the step of determining the group of data pipeline contributors to be selected for the data pipeline operation comprises: sending a request message to each of target data pipeline contributors associated with the one or more recommended data pipeline paths for seeking commitment to participate in the data pipeline operation; and selecting a data pipeline path from the one or more recommended data pipeline paths according to a response message from each of the target data pipeline contributors, wherein the determined group of data pipeline contributors is associated with the selected data pipeline path.

12. The method of any of claims 1 to 11, wherein after the step of determining the group of data pipeline contributors, the method further comprises: notifying the computing resource agent of the determined group of data pipeline contributors as a feedback.Atty. Dkt. No. 10085-01-0181-PCT13. The method of any of claims 1 to 12, wherein the first network node is a data control network node .

14. The method of any of claims 1 to 13 , wherein the first network node is a data plane access controller (DPAC).

15. A method for pipeline contributor selection assistance, performed by a computing resource agent, the method comprising: receiving, by the computing resource agent, a candidate data pipeline topology map from a first network node, wherein the candidate data pipeline topology map indicates candidate data pipeline contributors to support data pipeline operation according to data plane network data service request; estimating computing resource consumption and / or performance information associated with the candidate data pipeline contributors to derive one or more recommended data pipeline paths; and sending the one or more recommended data pipeline paths to the first network node.

16. The method of claim 15 , wherein the candidate data pipeline contributors are capable of supporting network data service as per requested in the data plane network data service request.

17. The method of claim 15 or 16, wherein the candidate data pipeline topology map comprises at least one of the following parameters: contributor execution order, candidate for data pipeline contributor, previous hop, next hop, data operation, and data type.

18. The method of claim 17, wherein the candidate data pipeline topology map further comprises at least one of the following parameters: service type, service initiation trigger, service termination trigger, service pipeline type, service area, and service performance requirements.

19. The method of any of claims 15 to 18, wherein the one or more recommended data pipeline paths comprises at least one of the following parameters: data pipeline path recommendation presenting topology communication path among the data pipeline contributors; and data pipeline computing performance presenting estimated computing performance.

20. The method of claim 19, wherein the data pipeline computing performance comprises at least one of the following parameters: end-to-end (E2E) latency, E2E throughput, E2E reliability, and E2E energy consumption.

21. The method of any of claims 15 to 20, wherein the recommended data pipeline paths are prioritized.

22. The method of any of claims 15 to 21, wherein the data pipeline operation is a cross domain data pipeline operation, and each domain is responsible for its own respective contributor selection.

23. The method of any of claims 15 to 22, wherein each domain is responsible for its aggregated computing performance determination.

24. The method of any of claims 15 to 23, wherein after the step of sending the one or more recommended data pipeline paths to the first network node, the method further comprises: receiving from the first network node a notification of data pipeline contributors selected by the first network node.

25. The method of any of claims 15 to 24, wherein the first network node is a data control network node.

26. The method of any of claims 15 to 25, wherein the first network node is a data plane access controller (DPAC).

27. A first network node, comprising: a first sending module, configured to send a candidate data pipeline topology map to a computing resource agent, wherein the candidate data pipeline topology map indicates candidate data pipeline contributors to support data pipeline operation according to data plane network data service request; a first receiving module, configured to receive from the computing resource agent one or more recommended data pipeline paths that are derived based on computing resource consumption and / or performance information associated with the candidate data pipeline contributors; and a determination module, configured to determine, based on the one or more recommended data pipeline paths, a group of data pipeline contributors to be selected for the data pipeline operation.

28. A computing resource agent, comprising:Atty. Dkt. No. 10085-01-0181-PCT a second receiving module, configured to receive a candidate data pipeline topology map from a first network node, wherein the candidate data pipeline topology map indicates candidate data pipeline contributors to support data pipeline operation according to data plane network data service request; an estimation module, configured to estimate computing resource consumption and / or performance information associated with the candidate data pipeline contributors to derive one or more recommended data pipeline paths; and a second sending module, configured to send the one or more recommended data pipeline paths to the first network node.

29. A first network node, comprising: at least one memory configured to store program instructions; and at least one processor configured to execute the program instructions, which cause the at least one processor to execute the method of any of claims 1 to 14.

30. A computing resource agent, comprising: at least one memory configured to store program instructions; and at least one processor configured to execute the program instructions, which cause the at least one processor to execute the method of any of claims 15 to 26.

31. A non-transitory machine-readable storage medium having stored thereon instructions that, when executed by a computer, cause the computer to execute the method of any one of claims 1 to 14 or the method of any one of claims 15 to 26.

32. A chip, comprising: a processor, configured to call and run a computer program stored in a memory, to cause a device in which the chip is installed to execute the method of any one of claims 1 to 14 or the method of any one of claims 15 to 26.

33. A computer readable storage medium, in which a computer program is stored, wherein the computer program causes a computer to execute the method of any one of claims 1 to 14 or the method of any one of claims 15 to 26.

34. A computer program product, comprising a computer program, wherein the computer program causes a computer to execute the method of any one of claims 1 to 14 or the method of any one of claims 15 to 26.

35. A computer program, wherein the computer program causes a computer to execute the method of any one of claims 1 to 14 or the method of any one of claims 15 to 26.

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