Near-end computing power and availability table

Abstract

Various examples of the embodiments described herein relate to methods and apparatus for near-end computing power and availability tables. An example of one embodiment relates to a method, the method includes retrieving information including at least one of configuration management (CM) data and performance management (PM) data based on a radio access network entity or function of a predetermined cloud, or a resource type in the radio access network entity or function of the predetermined cloud, or based on a capability of a resource type in a radio access network entity or function of a predetermined cloud, or based on location information of a radio access network entity or function of a predetermined cloud; deriving, from the retrieved information and from the predetermined cloud-based radio access network entity or function, a near-end computing power table for the source cloud-based radio access network entity or function in the vicinity of the target cloud-based radio access network entity or function; and providing the derived near-end computing power table, wherein the near-end computing power table is a neighbor relation table.

Description

Technical Field

[0001] Various examples of embodiments described herein relate to methods and apparatus for proximal computing capabilities and availability tables. Background Art

[0002] As time goes by, communication networks are expanding more and more around the world. Various organizations, such as the European Telecommunications Standards Institute (ETSI), the Third Generation Partnership Project (3GPP), rd Generation Partnership Project), Telecoms & Internet converged Services & Protocols for Advanced Networks (TISPAN), International Telecommunication Union (ITU), 3rd Generation Partnership Project 2 (3GPP2, 3 rd Various organizations such as the Internet Engineering Task Force (IETF), the Institute of Electrical and Electronics Engineers (IEEE), and the WiMAX Forum are working on standards or specifications for telecommunication networks and access environments.

[0003] For example, 3GPP defines a 5G core network function (NF) interface and related application programming interface (API) for each NF to enable communication between NFs. Summary of the invention

[0004] Various examples of the embodiments described in the present disclosure provide certain advantages, such as in the form of one or more improvements explicitly described herein or that are clear to those skilled in the art based on the present disclosure. Therefore, at least some examples of the embodiments of the present disclosure are intended to provide (or otherwise facilitate) at least a portion of the above-mentioned advantages and improvements.

[0005] Various aspects of examples of embodiments of the present disclosure are set out in the claims and relate to methods, apparatus and computer program products in the context of proximal computing capacity and availability tables.

[0006] At least some of the above advantages and improvements may be achieved by the method, apparatus and non-transitory storage medium specified in the claims. Further advantages and improvements may be achieved by the method, apparatus and non-transitory storage medium set out in the respective dependent claims.

[0007] So far, according to various examples of embodiments, a device may include: at least one processor; and at least one memory storing instructions, which, when executed by the at least one processor, may cause the device to at least: retrieve information including at least one of the following: configuration management (CM) data and performance management (PM) data of a wireless access network entity or function based on a predetermined cloud; The method comprises the steps of: retrieving information from a wireless access network entity or function based on a predetermined cloud, obtaining information about a resource type in a wireless access network entity or function based on a predetermined cloud, or obtaining information about a resource type in a wireless access network entity or function based on a predetermined cloud, or obtaining information about a location of a wireless access network entity or function based on a predetermined cloud; deriving a proximal computing capability table of a wireless access network entity or function based on a source cloud in the vicinity of a wireless access network entity or function based on a target cloud according to the retrieved information and according to the wireless access network entity or function based on a predetermined cloud, wherein the derivation is based on a proximity analysis of the wireless access network entity or function based on a predetermined cloud relative to the wireless access network entity or function based on a source cloud, and also based on a capability analysis of a computing resource type in a wireless access network entity or function based on a target cloud in the vicinity of the wireless access network entity or function based on a source cloud; and providing the derived proximal computing capability table, wherein the proximal computing capability table is a neighbor relationship table, and the neighbor relationship table indicates: computing resource types and / or computing resource types capabilities of a wireless access network entity or function based on a target cloud in the vicinity of the wireless access network entity or function based on a source cloud.

[0008] According to various examples of the embodiments, the device can also be enabled to retrieve additional information including at least one of the following: PM data of a predetermined cloud-based wireless access network entity or function, or availability of a computing resource type in a predetermined cloud-based wireless access network entity or function; derive a proximal computing availability table of a source cloud-based wireless access network entity or function in the vicinity of a target cloud-based wireless access network entity or function based on the retrieved additional information and a proximal computing capability table; and provide the derived proximal computing availability table, wherein the proximal computing availability table is a dynamic neighbor relationship table indicating: the current availability of a computing resource type of a target cloud-based wireless access network entity or function in the vicinity of the source cloud-based wireless access network entity or function.

[0009] According to various examples of the embodiments, the resource type indicated by the proximal computing capability table may refer to at least one of the following: support for neural processing units, support for deep processing units, support for graphics processing units, support for content addressable memory (CAM), support for artificial intelligence (AI) accelerators, or support for quantum processing units (QPU); and / or the capability of the resource type indicated by the proximal computing capability table may refer to at least one of the following: supported CPU frequencies, supported AI algorithm types, supported graphics memories, supported number of ray tracing cores, or supported AI and Tensor cores.

[0010] According to various examples of embodiments, the current availability indicated by the proximal computing availability table may include: a predicted availability of the computing type resource within a predetermined time period.

[0011] According to various examples of embodiments, the proximal computing capability table may include the following information: a target node identifier of a target cloud-based wireless access network entity or function; and / or resource types supported by the target cloud-based wireless access network entity or function; and / or capabilities of the resource types; and / or the last update time.

[0012] According to various examples of embodiments, the proximal computing availability table may include the following information: a target node identifier of a target cloud-based wireless access network entity or function; actual and / or predicted availability of supported resource types in the target cloud-based wireless access network entity or function; and / or the last update time.

[0013] According to various examples of the embodiments, the policies related to the proximal computing capability table may include at least one of the following: a switching policy, which is used to select a target cloud-based wireless access network entity or function with the resource type required for switching the endpoint terminal from the target cloud-based wireless access network entity or function based on the proximal computing capability table; and / or an unloading policy, which is used to offload the endpoint terminal-related services to the selected target cloud-based wireless access network entity or function with the required resource type in the target cloud-based wireless access network entity or function based on the proximal computing capability table; and / or a service guidance policy, which is used to guide services between the endpoint terminal and / or the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing capability table; and / or a spectrum sharing policy, which is used to share spectrum between the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing capability table; and / or an AI / ML training policy, which is used to train AI / ML models based on the proximal computing capability table.

[0014] According to various examples of the embodiments, the policy related to the proximal computing availability table may include at least one of the following items: a switching policy, which is used to select a target cloud-based wireless access network entity or function with a current and / or predicted available resource type for switching the endpoint terminal from the target cloud-based wireless access network entity or function based on the proximal computing availability table; and / or an unloading policy, which is used to unload the endpoint terminal related service to the selected target cloud-based wireless access network entity or function with a current and / or predicted available resource type in the target cloud-based wireless access network entity or function based on the proximal computing availability table; and / or a service steering policy, which is used to guide the service between the endpoint terminal and / or the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing availability table; and / or a spectrum sharing policy, which is used to share the spectrum between the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing availability table; and / or an AI / ML training policy, which is used to train the AI / ML model based on the proximal computing availability table.

[0015] According to various examples of the embodiments, the device is enabled to: provide an exported proximal computing capability table and / or provide an exported proximal computing availability table, and may also include: the device is enabled to provide the proximal computing capability table and / or the proximal computing availability table to a source cloud-based wireless access network entity or function, and / or provide the proximal computing capability table and / or the proximal computing availability table to a consumer of the proximal computing capability table and / or the proximal computing availability table that is different from the source cloud-based wireless access network entity or function; and / or wherein the device may be enabled to: provide an exported proximal computing capability table and / or a component that provides an exported proximal computing availability table through at least one of the following interfaces: R1 service or service management and coordinator (SMO, Service Management & Orchestrator) NBI.

[0016] In addition, according to various examples of embodiments, a device may include at least one processor; and at least one memory storing instructions, which instructions, when executed by the at least one processor, may cause the device to at least: apply a proximal computing table, which is a neighbor relationship table of capabilities and / or current availability of computing resource types of at least one target cloud-based wireless access network entity or function in proximity to the source cloud-based wireless access network entity or function; based on the applied proximal computing table, determine the capabilities and / or current availability and / or predicted availability of at least one computing resource type of the target cloud-based wireless access network entity or function; and based on the determination, manage the computing resources of the source cloud-based wireless access network entity or function according to a predetermined proximal computing table policy, wherein the managed computing resources are associated with an endpoint terminal, which is associated with the source cloud-based wireless access network entity or function.

[0017] According to various examples of embodiments, the proximal computing table may include a proximal computing capability table and / or a proximal computing availability table.

[0018] In addition, according to various examples of the embodiments, a device may include a component for retrieving information including at least one of the following: configuration management CM data and performance management PM data of a radio access network entity or function based on a predetermined cloud, or a resource type in a radio access network entity or function based on a predetermined cloud, or a capability of a resource type in a radio access network entity or function based on a predetermined cloud, or location information of a radio access network entity or function based on a predetermined cloud; and for deriving a source cloud-based radio access network in the vicinity of a target cloud-based radio access network entity or function based on the retrieved information and based on the radio access network entity or function based on the predetermined cloud. A component for a proximal computing capability table of an entity or function, the derivation being based on a proximity analysis of a predetermined cloud-based wireless access network entity or function relative to a source cloud-based wireless access network entity or function, and also based on a capability analysis of a computing resource type in a target cloud-based wireless access network entity or function in proximity to the source cloud-based wireless access network entity or function; and a component for providing the derived proximal computing capability table, wherein the proximal computing capability table is a neighbor relationship table indicating a computing resource type and / or a capability of a computing resource type of a target cloud-based wireless access network entity or function in proximity to the source cloud-based wireless access network entity or function.

[0019] According to various examples of the embodiments, the device may also include a component for retrieving additional information including at least one of the following: PM data of a predetermined cloud-based wireless access network entity or function, or the availability of a computing resource type in a predetermined cloud-based wireless access network entity or function; a component for deriving a proximal computing availability table of a source cloud-based wireless access network entity or function in the vicinity of a target cloud-based wireless access network entity or function based on the retrieved additional information and a proximal computing capability table; and a component for providing the derived proximal computing availability table, wherein the proximal computing availability table is a dynamic neighbor relationship table indicating the current availability of a computing resource type of a target cloud-based wireless access network entity or function in the vicinity of the source cloud-based wireless access network entity or function.

[0020] According to various examples of the embodiments, the resource type indicated by the proximal computing capability table may refer to at least one of the following: support for neural processing units, support for deep processing units, support for graphics processing units, support for content addressable memory CAM, support for artificial intelligence AI accelerators, or support for quantum processing units QPU; and / or wherein the capability of the resource type indicated by the proximal computing capability table may refer to at least one of the following: supported CPU frequency, supported AI algorithm type, supported graphics memory, supported number of ray tracing cores, or supported AI and Tensor cores.

[0021] According to various examples of embodiments, the current availability indicated by the proximal computing availability table may include a predicted availability of the computing type resource within a predetermined time period.

[0022] According to various examples of embodiments, the proximal computing capability table may include the following information: a target node identifier of a target cloud-based wireless access network entity or function; and / or resource types supported by the target cloud-based wireless access network entity or function; and / or capabilities of the resource types; and / or the last update time.

[0023] According to various examples of embodiments, the proximal computing availability table may include the following information: a target node identifier of a target cloud-based wireless access network entity or function; actual and / or predicted availability of supported resource types in the target cloud-based wireless access network entity or function; and / or the last update time.

[0024] According to various examples of the embodiments, the policies related to the proximal computing capability table may include at least one of the following: a switching policy, which is used to select a target cloud-based wireless access network entity or function with the resource type required for switching the endpoint terminal from the target cloud-based wireless access network entity or function based on the proximal computing capability table; and / or an unloading policy, which is used to offload the endpoint terminal-related services to the selected target cloud-based wireless access network entity or function with the required resource type in the target cloud-based wireless access network entity or function based on the proximal computing capability table; and / or a service guidance policy, which is used to guide services between the endpoint terminal and / or the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing capability table; and / or a spectrum sharing policy, which is used to share spectrum between the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing capability table; and / or an AI / ML training policy, which is used to train AI / ML models based on the proximal computing capability table.

[0025] According to various examples of the embodiments, the policies related to the proximal computing availability table may include at least one of the following: a switching policy, which is used to select a target cloud-based wireless access network entity or function with a current and / or predicted available resource type for switching the endpoint terminal from the target cloud-based wireless access network entity or function based on the proximal computing availability table; and / or an unloading policy, which is used to offload the endpoint terminal related services to the selected target cloud-based wireless access network entity or function with a current and / or predicted available resource type in the target cloud-based wireless access network entity or function based on the proximal computing availability table; and / or a service steering policy, which is used to guide services between the endpoint terminal and / or the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing availability table; and / or a spectrum sharing policy, which is used to share spectrum between the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing availability table; and / or an AI / ML training policy, which is used to train an AI / ML model based on the proximal computing availability table.

[0026] According to various examples of embodiments, the device may also include a component for providing a proximal computing capability table and / or a proximal computing availability table to a source cloud-based wireless access network entity or function, and / or a component for providing the proximal computing capability table and / or the proximal computing availability table to a consumer of a proximal computing capability table and / or a proximal computing availability table different from the source cloud-based wireless access network entity or function; and / or wherein the device may also include a component for providing an exported proximal computing capability table and / or an exported proximal computing availability table through at least one of the following interfaces: R1 service or SMO NBI.

[0027] In addition, according to various examples of embodiments, a device may include a component for applying a proximal computing table, which is a neighbor relationship table based on the capabilities and / or current availability of at least one computing resource type of a target cloud-based wireless access network entity or function in the vicinity of the source cloud-based wireless access network entity or function; a component for determining the capabilities and / or current availability and / or predicted availability of at least one computing resource type of a target cloud-based wireless access network entity or function based on the applied proximal computing table; and a component for managing the computing resources of the source cloud-based wireless access network entity or function according to a predetermined proximal computing table policy based on the determination, wherein the managed computing resources are associated with an endpoint terminal, which is associated with the source cloud-based wireless access network entity or function.

[0028] According to various examples of embodiments, the proximal computing table may include: a proximal computing capability table and / or a proximal computing availability table.

[0029] According to various examples of embodiments, the expression “component for…” (for example, “component for retrieving”) in combination with the above-mentioned specific functions may also be understood as “configured to…” (for example, “configured to retrieve”) in combination with the specific functions.

[0030] In addition, according to at least some examples of the embodiments, a method may include retrieving information including at least one of the following: configuration management CM data and performance management PM data of a radio access network entity or function based on a predetermined cloud, or a resource type in a radio access network entity or function based on a predetermined cloud, or a capability of a resource type in a radio access network entity or function based on a predetermined cloud, or location information of a radio access network entity or function based on a predetermined cloud; deriving, based on the retrieved information and based on the radio access network entity or function based on the predetermined cloud, a radio access network based on a source cloud and in the vicinity of the radio access network entity or function based on the target cloud; The method comprises the steps of: generating a proximal computing capability table of a wireless access network entity or function based on a predetermined cloud, the proximal computing capability table being derived based on a proximity analysis of a wireless access network entity or function based on a predetermined cloud relative to a wireless access network entity or function based on a source cloud, and also based on a capability analysis of a computing resource type in a wireless access network entity or function based on a target cloud in the proximity of the wireless access network entity or function based on the source cloud; and providing the derived proximal computing capability table, wherein the proximal computing capability table is a neighbor relationship table, the neighbor relationship table indicating: computing resource types and / or capabilities of computing resource types of wireless access network entities or functions based on a target cloud in the proximity of the wireless access network entity or function based on the source cloud.

[0031] According to at least some examples of the embodiments, the method may also include retrieving additional information including at least one of the following: PM data of a wireless access network entity or function based on a predetermined cloud, or availability of a computing resource type in a wireless access network entity or function based on a predetermined cloud; deriving a proximal computing availability table of a wireless access network entity or function based on a source cloud in the vicinity of a wireless access network entity or function based on a target cloud based on the retrieved additional information and a proximal computing capability table; and providing the derived proximal computing availability table, wherein the proximal computing availability table is a dynamic neighbor relationship table indicating the current availability of a computing resource type of a wireless access network entity or function based on a target cloud in the vicinity of a wireless access network entity or function based on a source cloud.

[0032] According to at least some examples of the embodiments, the resource type indicated by the proximal computing capability table may refer to at least one of the following: support for neural processing units, support for deep processing units, support for graphics processing units, support for content addressable memory CAM, support for artificial intelligence AI accelerators, or support for quantum processing units QPU; and / or wherein the capability of the resource type indicated by the proximal computing capability table may refer to at least one of the following: supported CPU frequencies, supported AI algorithm types, supported graphics memories, supported number of ray tracing cores, or supported AI and Tensor cores.

[0033] According to at least some examples of embodiments, the current availability indicated by the proximal computing availability table may include a predicted availability of the computing type resource within a predetermined time period.

[0034] According to at least some examples of the embodiments, the proximal computing capability table may include the following information: a target node identifier of a target cloud-based wireless access network entity or function; and / or resource types supported by the target cloud-based wireless access network entity or function; and / or capabilities of the resource types; and / or the last update time.

[0035] According to at least some examples of the embodiments, the proximal computing availability table may include the following information: a target node identifier of a target cloud-based wireless access network entity or function; actual and / or predicted availability of supported resource types in the target cloud-based wireless access network entity or function; and / or the last update time.

[0036] According to at least some examples of the embodiments, the policies related to the proximal computing capability table may include at least one of the following: a switching policy, which is used to select a target cloud-based wireless access network entity or function with the resource type required for switching the endpoint terminal from the target cloud-based wireless access network entity or function based on the proximal computing capability table; and / or an unloading policy, which is used to offload the endpoint terminal related services to the selected target cloud-based wireless access network entity or function with the required resource type in the target cloud-based wireless access network entity or function based on the proximal computing capability table; and / or a service steering policy, which is used to guide services between the endpoint terminal and / or the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing capability table; and / or a spectrum sharing policy, which is used to share spectrum between the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing capability table; and / or an AI / ML training policy, which is used to train AI / ML models based on the proximal computing capability table.

[0037] According to at least some examples of the embodiments, the policies related to the proximal computing availability table may include at least one of the following items: a switching policy, which is used to select a target cloud-based wireless access network entity or function with a current and / or predicted available resource type for switching the endpoint terminal from the target cloud-based wireless access network entity or function based on the proximal computing availability table; and / or an unloading policy, which is used to unload the endpoint terminal related services to the selected target cloud-based wireless access network entity or function with a current and / or predicted available resource type in the target cloud-based wireless access network entity or function based on the proximal computing availability table; and / or a service steering policy, which is used to guide services between the endpoint terminal and / or the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing availability table; and / or a spectrum sharing policy, which is used to share spectrum between the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing availability table; and / or an AI / ML training policy, which is used to train an AI / ML model based on the proximal computing availability table.

[0038] According to at least some examples of the embodiments, the provision of the derived proximal computing capability table and / or the provision of the derived proximal computing availability table may include at least one of the following items: providing the proximal computing capability table and / or the proximal computing availability table to a source cloud-based wireless access network entity or function, and / or providing the proximal computing capability table and / or the proximal computing availability table to a consumer of the proximal computing capability table and / or the proximal computing availability table that is different from the source cloud-based wireless access network entity or function; and / or wherein the provision of the derived proximal computing capability table and / or the provision of the derived proximal computing availability table may be performed via at least one of the following interfaces: R1 service or SMO NBI.

[0039] In addition, according to at least some examples of the embodiments, a method may include applying a proximal computing table, which is a neighbor relationship table based on the capabilities and / or current availability of computing resource types of at least one target cloud-based wireless access network entity or function in proximity to the source cloud-based wireless access network entity or function; based on the applied proximal computing table, determining the capabilities and / or current availability and / or predicted availability of computing resource types of at least one target cloud-based wireless access network entity or function; and based on the determination, managing the computing resources of the source cloud-based wireless access network entity or function according to a predetermined proximal computing table policy, wherein the managed computing resources are associated with an endpoint terminal, which is associated with the source cloud-based wireless access network entity or function.

[0040] According to at least some examples of embodiments, the proximal computing table may include a proximal computing capability table and / or a proximal computing availability table.

[0041] Furthermore, according to at least some examples of the embodiments, a computer program product for a computer may be provided, the computer program product comprising a software code portion for executing the steps of any one of the above methods when the above product is run on the computer.

[0042] According to at least some examples of the embodiments, regarding the computer program product, the computer program product may include a computer-readable medium on which the above-mentioned software code portion is stored, and / or the computer program product can be directly loadable into the internal memory of the computer, and / or can be sent via a network by means of at least one of an upload process, a download process and a push process.

[0043] Proximal computing power and availability tables are facilitated according to any of the various aspects recited in the claims, thereby providing at least some of the advantages and improvements described above.

[0044] In more detail, the present disclosure describes a proximal computing capability and availability table that, according to some examples of embodiments, can be more advantageous than proprietary solutions for at least the following reasons.

[0045] For example, the examples of embodiments described in the present disclosure may provide the following advantages. That is, computing resource type capability / availability aware offloading decisions in communication and computing integrated networks, also known as computing network convergence. In addition, application mobility performance is improved, especially for applications with high computing and low latency requirements, such as XR-based applications. In addition, solutions based on computing network convergence are enabled to be more widely accepted by operators and end users.

[0046] Other advantages will become apparent to those skilled in the art from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Some of the embodiments of the present disclosure are described below by way of example only with reference to the accompanying drawings, in which:

[0048] Figure 1 A joint RAN+compute node for computing and network convergence according to various examples of embodiments of the present disclosure is shown;

[0049] Figure 2a , Figure 2b , Figure 2c (connected at A-A' and B-B' and A"-A"' and B"-B"') shows an O-RAN standard dependency, near-end computing capability table according to various examples of embodiments of the present disclosure;

[0050] Figure 3a , Figure 3b , Figure 3c (connected at A-A' and B-B' and A"-A"' and B"-B"') shows an O-RAN standard dependency, near-end computing availability table according to various examples of embodiments of the present disclosure;

[0051] Figure 4 A diagram showing various examples according to embodiments of the present disclosure showing a diagram of the mobility of a UE running an XR application based on a near-end computing capability / availability table / policy;

[0052] Figure 5 A flowchart illustrating steps corresponding to an example method according to various examples of embodiments of the present disclosure is shown;

[0053] Figure 6 A flowchart illustrating steps corresponding to an example method according to various examples of embodiments of the present disclosure is shown;

[0054] Figure 7 A block diagram illustrating an example apparatus according to various examples of embodiments of the present disclosure; and

[0055] Figure 8 A block diagram illustrating an example apparatus according to various examples of embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0056] Generally speaking, two or more endpoints involved in the communication between endpoints may be implemented as a specific type of endpoint (e.g., a communication station or entity or function, such as a terminal device, a user equipment (UE, User Equipment), or other communication network elements, databases, servers, hosts, etc.), and may be implemented as one or more network elements or functions (e.g., virtualized network functions), such as communication network control elements or functions, for example, access network elements (such as access points (AP, Access Point), wireless base stations (BS, Base Station), relay stations, eNB, gNB, etc.); and core network elements or functions, such as control nodes, support nodes, service nodes, gateways, user plane functions, access and mobility functions, etc. These endpoints may belong to a single communication network system, different communication network systems, or a combination of at least one same communication network system and at least one different communication network system.

[0057] In the following, various examples of the embodiments will be described using a communication network architecture based on 3GPP standards such as 5G / NR as an example of a communication network to which the examples of the embodiments may be applied, without limiting the examples of the embodiments to such an architecture. It is clear to those skilled in the art that the examples of the embodiments may also be applied to other types of communication networks, such as 4G and / or LTE (even 6G and higher), in which mobile communication principles such as Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), Personal Communications Services (PCS), Wideband Code Division Multiple Access (WCDMA), systems using ultra-wideband (UWB) technology, mobile ad-hoc networks (MANET), wired access, etc. In addition, without loss of generality, the description of some examples of the embodiments is related to mobile communication networks, but the present disclosure can be extended and applied to any other type of communication network, such as a wired communication network or a data center network.

[0058] The examples of the following embodiments are to be understood as merely illustrative in nature. Although portions of the present disclosure may refer to "an," "one," or "some" examples of (multiple) embodiments in several specific locations, this does not necessarily mean that each such reference relates to the same (multiple) examples of (multiple) embodiments, or that the features described apply only to a single example of an embodiment. Individual features from different embodiments may also be combined to provide other examples of embodiments. In addition, terms such as "comprising" and "including" should be understood as not limiting the examples of the described embodiments to consisting only of the features already mentioned; such examples of embodiments may also include features, structures, units, modules, etc. that are not specifically mentioned.

[0059] A simplified system architecture of a (telecommunication) communication network including a mobile communication system (in which some examples of the embodiments are applicable) may include the architecture of one or more communication networks. The one or more communication networks may include (multiple) radio access network subsystems and (multiple) core networks. Such an architecture may include: one or more communication network control elements or functions, access network elements, radio access network elements, cloud-based radio access elements, access service network gateways or base transceiver stations, such as base stations (BS), access points (AP), NodeB (NB), eNB, cloud radio access network (RAN) nodes or gNB, distributed or centralized units (CU, Centralized Unit), which control (multiple) corresponding coverage areas or cells, and one or more communication stations are capable of communicating with them via one or more channels via one or more communication beams for sending several types of data in multiple access domains, such as communication elements or functions, such as user equipment (e.g., customer equipment), mobile devices, or terminal devices, such as UE, or another device with similar functions, such as a modem chipset, chip, module, etc., which may also be part of a station, element, function or application configured to communicate, such as UE, an entity or function that can be used in a machine-to-machine communication architecture, or attached as a separate element to an element, function or application configured to communicate, etc. In addition, it may include (core) network elements or network functions ((core) network control unit or network function, (core) network management element or network function), such as gateway network element / function, mobility management entity, mobile switching center, server, database, etc.

[0060] The functionality and interconnection of the described elements and functions (which also depends on the actual network type) are clear to a person skilled in the art and can be described in the corresponding specification, so their description is omitted here. However, it should be noted that in addition to those described in detail below, several additional network elements and signaling links may be used to communicate with elements, functions or applications, such as communication endpoints, communication network control units (such as servers, gateways, radio network controllers), and other units of the same or other communication networks.

[0061] It should be understood that according to some examples of embodiments, a so-called "fluid" or flexible network concept can be implemented, in which the operations and functions of a network element, a network function or another entity of the network can be performed in a flexible manner in different entities or functions (such as in a node, a host or a server). Therefore, the "division of labor" between the network elements, functions or entities involved can vary from case to case.

[0062] In some examples of embodiments, the present disclosure relates to a proximal computing capability and availability table.

[0063] Computing and network convergence enables the collaboration of communication networks and computing. This is an approach that softens the boundaries between cloud computing and networks in order to utilize their resources in the most efficient way. This allows applications to take advantage of this convergence of computing and network resources. Cloud-based RAN solutions can play an important role in computing and network convergence, as an edge computing platform, it has the potential to share some idle computing resources. New-age applications based on XR and V2X can benefit from computing and network convergence, but in addition to strict requirements on both communication and computing, they also require application mobility. New network architectures that converge computing and networking need to meet these application requirements.

[0064] The types of computing resources required for different applications may vary depending on the nature of the application. However, the types of computing resources available in different nodes may differ from the capabilities required for a particular application. The type(s) of computing resources mentioned here may be, but are not limited to, support for neural processing units, deep processing units, graphics processing units, content addressable memories (CAMs), artificial intelligence (AI) accelerators, and / or quantum processing units (QPUs), etc. Figure 1 A diagram of a joint RAN+computing node 110 used in computing and network convergence (CNC) is given in FIG. According to various examples of embodiments, Figure 1 Also shown are UE 122 in coverage area 121 provided by, for example, cloud RAN node 120, UE 132 in coverage area 131 provided by, for example, cloud RAN node 130, and UE 142 in coverage area 141 provided by, for example, cloud RAN node 140. In addition, the capability of the resource type indicated by the proximal computing capability table may refer to at least one of the following: supported CPU frequency, supported AI algorithm type, supported graphics memory, supported number of ray tracing cores, or supported AI and Tensor cores.

[0065] In the compute-network convergence approach, the idea is to use idle processing resources from cloud RAN nodes (e.g. if the network load is low) to perform some edge computing related tasks, thereby using cloud resources efficiently. In particular, leveraging the proximity of cloud RAN nodes to end users / devices is a key factor in meeting the low latency requirements of tasks / applications. This convergence approach can be used to offload other nodes and / or support various low latency applications. However, in order to exploit this opportunity, the following factors need to be considered:

[0066] 1. Whether the idle resources match the task requirements in terms of the type of computing resources and their capabilities. The type of computing resources mentioned here can be, but is not limited to, support for neural processing units, deep processing units, graphics processing units, etc.;

[0067] 2. Whether the amount of idle resources of the required type / capability is sufficient to meet the mission requirements;

[0068] 3. Whether the nodes are within the proximity required to support service continuity and service latency requirements of the application.

[0069] The above problem is more prominent when the UE needs to run applications with high computation and low latency requirements (such as XR-based applications). Similarly, this problem is more prominent in distributed AI / ML model training and inference as part of use cases such as AI as a service.

[0070] This specification proposes a device and method for realizing the perception of information about the computing capabilities of other nearby cloud RAN nodes and the management of such information in a cloud RAN node. Therefore, it is recommended to introduce a proximal computing table that stores information about the computing capabilities of the cloud RAN node. This information can be directly stored and maintained by the cloud RAN node and / or other management entities (e.g., SMO / SON / RIC / O&M). The proximal computing table can be of the following types:

[0071] 1. Proximal computing capability table: A neighbor relationship table based on the capabilities of computing resource types. The computing resource types mentioned here can be, but are not limited to, support for neural processing units, deep processing units, graphics processing units, etc.

[0072] 2. Near-end computing availability table: A dynamic neighbor relationship table based on the current load / availability of computing resource types. The table also predicts the load / availability of resources within a specific time window on a specific day, for example, during which time window the most idle resources are expected.

[0073] In addition, in this specification, a method for exporting / maintaining a proximal computing table is proposed. The above method for exporting / maintaining a table can be performed by a service management and orchestrator (SMO, Service Management & Orchestrator) / self-organizing network (SON, Self Organizing Networks) / RAN Intelligent Controller (RIC, RAN Intelligent Controller) / O&M and / or the cloud RAN node itself. Such a method needs to take into account the computing power of different resource types and their availability in different nearby cloud RAN nodes.

[0074] Furthermore, in this specification, methods are proposed for using proximal calculation tables by cloud RAN nodes and / or other network entities, for example through network configuration models and policies.

[0075] For example, in the case of mobility of applications such as XR-based applications, the handover decision made by Cloud RAN can also take into account the above-mentioned proximal computing capability table and proximal computing availability table. This helps to handover the application to a target node near the computing resource type with relevant capabilities and availability.

[0076] Referring to Figure 2 (comprising part 1 of 3 parts to part 3 of 3 parts; connected at A-A' and B-B' and A"-A"' and B"-B"'), a process for deriving / maintaining a proximal computing capability table and its use in an O-RAN-based cloud RAN node is proposed.

[0077] With reference to FIG. 2 , the following steps are outlined in more detail, according to various examples of embodiments.

[0078] Steps 1 to 4 involve O1 data collection:

[0079] -rApp (e.g., an application running to obtain the proximal computing capability table) 210 retrieves different data related to deriving / maintaining the proximal computing capability table. The data includes, but is not limited to: configuration management (CM) and performance management (PM) data of different cloud RAN nodes, capabilities of resource types in different cloud RAN nodes, and location information of cloud RAN nodes.

[0080] It should be noted that rApp is a term defined by O-RAN and that, according to various examples of embodiments, there may be any number of rApps corresponding to different use cases. rApp may represent a non-RT RIC application, i.e., an application designed to consume and / or produce R1 services. According to various examples of embodiments, rApp may provide value-added services related to intelligent RAN optimization and operation, leveraging the functionality provided by the SMO / No-RT RIC framework.

[0081] - CM / PM data is retrieved using R1 services provided by the SMO / Non-RT RIC framework 220, which in turn retrieves relevant data from different Cloud RAN nodes 230 using the O1 interface.

[0082] Steps 5 to 8 involve O2 data collection:

[0083] - The capabilities of the resource type are retrieved using the R1 services provided by the SMO / No-RT RIC framework 220 , which in turn retrieves the relevant data from the O-Cloud 240 associated with the different Cloud RAN nodes 230 using the O2 interface.

[0084] Steps 9 to 11 involve data analysis and inference:

[0085] -rApp 210 derives a proximal computing capability table for each cloud RAN node based on the proximity analysis of different cloud RAN nodes in the neighborhood and the capability analysis of computing resource types in different cloud RAN nodes in the neighborhood. As used in this specification, the term proximity may be understood to mean that the cell coverage area of ​​the source node has at least some overlap with the cell coverage area of ​​the target node.

[0086] Steps 12 to 15 involve supplying:

[0087] - rApp 210 requests R1 services provided by SMO / non-RT RIC framework 220 to provision / update near-end computing capability tables / policies. This can be achieved using near-RT RIC or directly using cloud RAN nodes.

[0088] - In a variant using A1 policies towards near-RT RIC, rApp 210 creates / updates corresponding policies and requests R1 services related to A1 policies. R1 services in turn create / update policies at one or more near-RT RIC 250 instances using the A1 interface. Then, as part of the relevant use case, the near-RT RIC 250 will use E2 control or E2 policy to enforce at the cloud RAN node 230.

[0089] - In the variant using O1 CM towards the near RT RIC 250, the rApp 210 creates / updates the corresponding information model related to the near end computing capability table and requests R1 services related to CM provisioning. The R1 services in turn create / update CM objects at the near RT RIC 250 using the O1 CM interface. The near RT RIC 250 will then enforce at the cloud RAN node 230 using E2 controls or E2 policies as part of the relevant use case.

[0090] - In the variant using O1 CM directly towards the cloud RAN node 230, the rApp 210 creates / updates the corresponding information model related to the proximal computing capabilities table and requests the R1 services related to CM provisioning. The R1 services in turn use the O1 CM interface to create / update the CM object at the cloud RAN node 230. The cloud RAN node 230 will then use this table as part of the relevant use case.

[0091] The proximal computing capability table / policy may include the following information: a target node identifier of a target cloud RAN node (e.g., for a target cloud-based radio access network entity or function); and / or resource types supported by each target node: and / or capabilities of the resource types (e.g., supported by each target node); and / or the last update time.

[0092] Examples of proximal computing capability policies may relate to: a switching policy, for example, for switching the UE to a target cloud RAN node with the required resource type; and / or an offloading policy, for example, for offloading UE traffic to a target cloud RAN node with the required resource type. Here, the UE traffic may relate to: XR rendering, where a GPU may be required; or AI / ML processing, such as performing inference or training where an NPU may be required. In addition, additional policies may be: a traffic steering policy for steering traffic between the UE and / or the target cloud RAN node and / or the source cloud RAN node; a spectrum sharing policy for sharing spectrum (e.g., frequency spectrum, time spectrum) between the target cloud RAN node and / or the source cloud RAN node. In addition, the policy may be an AI / ML training policy for training AI / ML models.

[0093] Now referring to Figure 3 (including part 1 of 3 parts to part 3 of 3 parts; connected at A-A' and B-B' and A"-A"' and B"-B"'), a process for deriving / maintaining a proximal computing availability table and its use in an O-RAN-based cloud RAN node is proposed.

[0094] With reference to FIG3 , the following steps are outlined in more detail, according to various examples of embodiments.

[0095] Steps 1 to 4 involve O1 data collection:

[0096] -rApp 310 retrieves different data related to deriving / maintaining the proximal computing availability table. This data includes but is not limited to: performance management (PM) data of different cloud RAN nodes, and load / availability of computing resource types in different cloud RAN nodes.

[0097] -PM data is retrieved using R1 services provided by the SMO / non-RT RIC framework 320, which in turn retrieves relevant data from different Cloud RAN nodes 330 using the O1 interface.

[0098] Steps 5 to 8 involve O2 data collection:

[0099] - Compute the load / availability of resource types, retrieved using the R1 service provided by the SMO / Non-RT RIC framework 320 , which in turn retrieves relevant data from the O-Cloud 340 associated with the different Cloud RAN nodes 330 using the O2 interface.

[0100] Steps 9 to 10 involve data analysis and inference:

[0101] - rApp 310 derives a proximal computing availability table for the cloud RAN node based on load / availability analysis of computing resource types in different target nodes in the corresponding proximal computing capacity table (as described above with reference to FIG. 2 ). rApp 310 may rely on AI / ML model-based load / availability pattern analysis of computing resource types, which includes predictions of available idle resources within a specific time window in a specific day, e.g., within that time window, the most idle resources are expected, along with which confidence levels, etc. may be determined and opened.

[0102] Steps 11 to 14 involve supplying:

[0103] - rApp 310 requests R1 services provided by SMO / non-RT RIC framework 320 to provision / update near end computing availability table / policy. This can be achieved using near RT RIC 350 or using cloud RAN node 330 directly.

[0104] - In a variant using A1 policies towards the near-RT RIC 350, the rApp 310 creates / updates the corresponding policies and requests R1 services related to the A1 policies. The R1 services in turn create / update policies at one or more near-RT RIC 350 instances using the A1 interface. The near-RT RIC 350 will then enforce at the cloud RAN node 330 using E2 controls or E2 policies as part of the relevant use case.

[0105] - In the variant using O1 CM towards the near RT RIC 350, the rApp 310 creates / updates the corresponding information model related to the near end compute availability table and requests R1 services related to CM provisioning. The R1 services in turn create / update CM objects at the near RT RIC 350 using the O1 CM interface. The near RT RIC 350 will then enforce at the cloud RAN node 330 using E2 control or E2 policy as part of the relevant use case.

[0106] - In the variant using O1 CM directly towards the cloud RAN node 330, the rApp 310 creates / updates the corresponding information model related to the proximal compute availability table and requests the R1 service related to CM provisioning. The R1 service in turn creates / updates the CM object at the cloud RAN node 330 using the O1 CM interface. The cloud RAN node 330 will then use this table as part of the relevant use case.

[0107] According to various examples of embodiments, the proximal computing availability table / policy may include the following information. Target node ID, actual / predicted availability of each supported resource type in the target node, and / or the last update time, which indicates the time when the availability information of the target node was updated. The actual / predicted availability of each supported resource type in the target node may include: a specific time window for a given availability, a resource type, an availability percentage, a flag indicating whether it is a predicted value, (multiple) criteria specific to the resource type of the target node for all use cases or a specific use case (e.g., a resource availability threshold for switching), and / or the highest availability resource for AI / ML services.

[0108] Examples of proximal computing availability policies may relate to: a switching policy, e.g., for switching the UE to a node with the required resource type capabilities and / or availability, which is predicted or current; and / or an offloading policy, e.g., for offloading UE traffic to a target cloud RAN node with the required resource type capabilities or availability. Here, the UE traffic may relate to: XR rendering, where a GPU may be required; or AI / ML processing, such as performing inference or training where an NPU may be required. In addition, additional policies may be traffic steering policies for steering traffic between UEs and / or target cloud RAN nodes and / or source cloud RAN nodes; spectrum sharing policies for sharing spectrum (e.g., frequency spectrum, time spectrum, time-frequency spectrum) between target cloud RAN nodes and / or source cloud RAN nodes. In addition, the policy may be an AI / ML training policy for training AI / ML models.

[0109] Reference now Figure 4 , showing example diagrams of the use of proximal computing capability tables / policies and / or proximal computing availability tables / policies according to various examples of embodiments.

[0110] Figure 4 The coverage of different (multiple) UEs 450, 412, 422, 432 and 442 in is given by four nodes, i.e., blue node 1 410, pink node 2 420, yellow node 3 430, and green node 4 440; blue node 1 410 provides coverage for the first area 411, pink node 2 420 provides coverage for the second area 421, yellow node 3 430 provides coverage for the third area 431, and green node 4 440 provides coverage for the fourth area 441. All nodes except pink node 2 420 have GPU capabilities. It should be understood that pink node 2 420, yellow node 3 430, and green node 4 440 are near blue node 1 410.

[0111] Figure 4 UE 450 in Figure 4 410 and is in the first coverage area 411. The UE 450 has an XR application running in it, and the latency requirements of the XR application can be better met by a node with GPU capabilities. Figure 4 When moving away from the first coverage area 411 as shown by the middle arrow, the UE 450 can be served by any of the other nodes 420, 430, or 440, however, only the yellow node 3 430 and the green node 4440 can have acceptable latency to meet the GPU requirements. The proximal computing capability table / policy in the blue node 1 410 enables the blue node 1 410 (or in the case of considering the blue node 1 410, running the proximal computing performance table / policy to represent another entity or function of the source node) to consider the computing capability of the resource type in the target node to make a switching decision. This means that the blue node 1 410 can select the yellow node 3 430 or the green node 4 440 to switch the UE 450, but cannot select the pink node 2 420.

[0112] In addition to capacity, the load / availability of the corresponding resource type in the target node is also an important factor in the switching decision. Figure 4 In the example, yellow node 3 430 has a higher GPU load than green node 4 440, and therefore the availability of GPU resources required by UE 450 is lower. The proximal computing availability table / policy in blue node 1 410 enables it to take into account the load / availability of resource types in the target node to make a switching decision. This means that blue node 1 410 will select green node 4 440 to switch UE 450 so that the XR application can continue to execute at the required latency level after the switch.

[0113] For example, refer to Figures 1 to 4 According to various examples of embodiments, at least some of the following advantages can be achieved. That is, computing resource type capability / availability aware offloading decisions in communication and computing integrated networks, also known as computing network convergence. In addition, application mobility performance is improved, especially for applications with high computing and low latency requirements, such as XR-based applications. In addition, solutions based on computing network convergence are enabled to be more widely accepted by operators and end users.

[0114] In the following, further examples of embodiments are described with respect to the above-mentioned methods and / or apparatuses.

[0115] Reference now Figure 5 , a flowchart illustrating steps corresponding to an example method according to various examples of embodiments of the present disclosure is shown.

[0116] Specifically, according to Figure 5 In S510, the method includes retrieving information including at least one of the following items: configuration management CM data and performance management PM data of a predetermined cloud-based radio access network entity or function (e.g., a predetermined cloud RAN node, such as pink node 2 420, yellow node 3 430, and green node 4 440), or a resource type in the predetermined cloud-based radio access network entity or function, or a capability of a resource type in the predetermined cloud-based radio access network entity or function, or location information of the predetermined cloud-based radio access network entity or function.

[0117] It should be noted that the predetermined cloud-based wireless access network entity or function used in this document may include at least one of the following items:

[0118] - A list of cloud-based radio access network entities or functions (e.g., cloud RAN nodes) given by the user of the rApp for which a proximal computing capacity table needs to be generated. The user can provide this list using a GUI-based function or other means herein;

[0119] - A list of cloud-based radio access network entities or functions given by the user of R1 service for which a proximal computing capability table needs to be generated;

[0120] - a list of cloud-based radio access network entities or functions given by the user of the SMO NBI for which a proximal computing capability table needs to be generated; or

[0121] - A list of all cloud-based radio access network entities or functions managed by the SMO or equivalent management function for which the proximal computing capability table needs to be generated.

[0122] In addition, in S520, the method includes deriving a proximal computing capability table of a source cloud-based radio access network entity or function (e.g., a source cloud RAN node, such as such a blue node 1 410) in the vicinity of a target cloud-based radio access network entity or function based on the retrieved information and based on a predetermined cloud-based radio access network entity or function. The deriving is based on a proximity analysis of the predetermined cloud-based radio access network entity or function relative to the source cloud-based radio access network entity or function, and also based on a capability analysis of a computing resource type in the target cloud-based radio access network entity or function in the vicinity of the source cloud-based radio access network entity or function.

[0123] In addition, in S530, the method includes providing an exported proximal computing capability table. The proximal computing capability table is a neighbor relationship table indicating computing resource types and / or capabilities of computing resource types of target cloud-based wireless access network entities or functions in the vicinity of the source cloud-based wireless access network entities or functions.

[0124] In addition, according to at least some examples of the embodiments, the method may also include retrieving additional information including at least one of the following items: PM data of a wireless access network entity or function based on a predetermined cloud, or availability of a computing resource type in a wireless access network entity or function based on a predetermined cloud; deriving a proximal computing availability table of a wireless access network entity or function based on a source cloud in the vicinity of a wireless access network entity or function based on a target cloud from the retrieved additional information and the proximal computing capability table; and providing the derived proximal computing availability table, wherein the proximal computing availability table is a dynamic neighbor relationship table indicating: the current availability of a computing resource type of a wireless access network entity or function based on a target cloud in the vicinity of a wireless access network entity or function based on a source cloud.

[0125] According to at least some examples of the embodiments, the resource type indicated by the proximal computing capability table may refer to at least one of the following: support for neural processing units, support for deep processing units, support for graphics processing units, support for CAM, support for AI accelerators, or support for QPU; and / or the capability of the resource type indicated by the proximal computing capability table may refer to at least one of the following: supported CPU frequencies, supported AI algorithm types, supported graphics memory, supported number of ray tracing cores, or supported AI and Tensor cores.

[0126] According to at least some examples of embodiments, the current availability indicated by the proximal computing availability table may include a predicted availability of the computing type resource within a predetermined time period.

[0127] According to at least some examples of the embodiments, the proximal computing capability table may include the following information: a target node identifier of a target cloud-based wireless access network entity or function; and / or resource types supported by the target cloud-based wireless access network entity or function; and / or capabilities of the resource types; and / or the last update time.

[0128] According to at least some examples of the embodiments, the proximal computing availability table may include the following information: a target node identifier of a target cloud-based wireless access network entity or function; actual and / or predicted availability of supported resource types in the target cloud-based wireless access network entity or function; and / or the last update time.

[0129] According to at least some examples of the embodiments, the policies related to the proximal computing capability table may include at least one of the following items: a switching policy, which is used to select a target cloud-based wireless access network entity or function with the resource type required for switching the endpoint terminal from the target cloud-based wireless access network entity or function based on the proximal computing capability table; and / or an unloading policy, which is used to offload the endpoint terminal-related services to the selected target cloud-based wireless access network entity or function with the required resource type in the target cloud-based wireless access network entity or function based on the proximal computing capability table; and / or a service guidance policy, which is used to guide the service between the endpoint terminal and / or the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing capability table; and / or a spectrum sharing policy, which is used to share the spectrum between the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing capability table; and / or an AI / ML training policy, which is used to train the AI / ML model based on the proximal computing capability table.

[0130] According to at least some examples of the embodiments, the policies related to the proximal computing availability table may include at least one of the following: a switching policy, which is used to select a target cloud-based wireless access network entity or function with a current and / or predicted available resource type for switching the endpoint terminal from the target cloud-based wireless access network entity or function based on the proximal computing availability table; and / or an unloading policy, which is used to unload the endpoint terminal related services to the selected target cloud-based wireless access network entity or function with a current and / or predicted available resource type in the target cloud-based wireless access network entity or function based on the proximal computing availability table; and / or a service steering policy, which is used to guide services between the endpoint terminal and / or the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing availability table; and / or a spectrum sharing policy, which is used to share spectrum between the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing availability table; and / or an AI / ML training policy, which is used to train an AI / ML model based on the proximal computing availability table.

[0131] According to at least some examples of the embodiments, the provision of the derived proximal computing capability table and / or the provision of the derived proximal computing availability table may include at least one of the following items: providing the proximal computing capability table and / or the proximal computing availability table to a source cloud-based wireless access network entity or function, and / or providing the proximal computing capability table and / or the proximal computing availability table to a consumer of the proximal computing capability table and / or the proximal computing availability table that is different from the source cloud-based wireless access network entity or function; and / or wherein the provision of the derived proximal computing capability table and / or the provision of the derived proximal computing availability table may be performed via at least one of the following interfaces: R1 service or SMO NBI.

[0132] Reference now Figure 6 , a flowchart illustrating steps corresponding to an example method according to various examples of embodiments of the present disclosure is shown.

[0133] Specifically, according to Figure 6 In S610, the method includes applying a proximal computing table (e.g., a proximal computing capability / availability table as described in detail above with reference to FIGS. 2 and 3 ), which is a neighbor relationship table (e.g., a dynamic neighbor relationship table) of computing resource types, capabilities, and / or current availability of at least one target cloud-based wireless access network entity or function in proximity to the source cloud-based wireless access network entity or function.

[0134] Furthermore, in S620, the method includes determining, based on the applied proximal computing table, a computing resource type, capability and / or current availability and / or predicted availability of at least one target cloud-based wireless access network entity or function.

[0135] In addition, in S630, the method includes: based on the determination, managing components of computing resources of a source cloud-based wireless access network entity or function according to a predetermined proximal computing table strategy, wherein the managed computing resources are associated with an endpoint terminal (e.g., UE, such as UE 450), which is associated with the source cloud-based wireless access network entity or function.

[0136] It should be noted that according to Figure 6 The method may be applied to a cloud-based radio access network entity or function, such as a cloud RAN node (e.g., blue node 1 410), but is not limited thereto. Instead, the method may also be applied by other network entities or functions, such as in a near-RT RIC or another rApp, or in other consumers of the information / near-end computing table (such as SMONBI / R1 service consumers).

[0137] According to at least some examples of embodiments, the proximal computing table may include a proximal computing capability table and / or a proximal computing availability table.

[0138] Reference now Figure 7 , Figure 7 Shown is a block diagram of an example apparatus illustrating various examples according to embodiments of the present disclosure.

[0139] Specifically, Figure 7 A block diagram of an apparatus 700 according to various examples of embodiments is shown, which may represent the apparatus described above with reference to Figures 1 to 4 The rApps (e.g., rApps 210, 310) described herein may participate in the proximal computing capacity and availability table. Furthermore, even though reference is made to an rApp, the device and / or rApp may be a cloud-based radio access network entity or function (e.g., a cloud RAN node) or another device or function with similar tasks, such as a chipset, chip, module, application, etc., which may also be part of a network element or attached to a network element as a separate element, and / or a network management function and / or service management and orchestrator, etc. It should be understood that each box and any combination thereof may be implemented by various components or combinations thereof, such as hardware, software, firmware, one or more processors, and / or circuit systems.

[0140] Figure 7 The device 700 shown may include a processing circuit system, a processing function, a control unit or a processor 710, such as a CPU, etc., which is suitable for implementing a near-end computing power and availability table. The processor 710 may include one or more processing parts or functions dedicated to a specific process as described below, or the process may be run in a single processor or processing function. The part for performing such a specific process may also be provided as a discrete component, or provided in one or more other processors, processing functions or processing parts, such as in a physical processor (such as a CPU) or in one or more physical or virtual entities. Reference numerals 731 and 732 represent input / output (I / O) units or functions (interfaces) connected to the processor or processing function 710. The I / O units 731 and 732 may be a combined unit including a communication device toward several entities / elements, or may include a distributed structure with multiple different interfaces for different entities / elements. Reference numeral 720 represents a memory, which may be used, for example, to store data and programs / instructions to be executed by the processor or processing function 710 and / or used as a working storage device for the processor or processing function 710. It should be noted that the memory 720 may be implemented using one or more memory portions of the same or different types of memory, but may also represent external memory, such as an external database provided on a cloud server.

[0141] The processor or processing function 710 is configured to perform processing related to the above-mentioned processing. In particular, the processor or processing circuit system or function 710 includes one or more of the following sub-parts. Sub-part 711 is a retrieval part, which can be used as a part for retrieving information. Part 711 can be configured to perform according to Figure 5 In addition, subsection 712 is a deriving section, which can be used as a section for deriving a proximal computing capability table of a source cloud-based wireless access network entity or function in the vicinity of a target cloud-based wireless access network entity or function according to the retrieved information and according to a predetermined cloud-based wireless access network entity or function. Section 712 can be configured to perform Figure 5 In addition, subsection 713 is a providing section, which can be used as a section for providing the derived proximal computing capability table. Section 713 can be configured to perform Figure 5 The S530 is processed.

[0142] Reference now Figure 8 , Figure 8 A block diagram illustrating an example apparatus according to various examples of embodiments of the present disclosure is shown.

[0143] Specifically, Figure 8 A block diagram illustrating an apparatus 800 according to various examples of embodiments is shown, which may represent the apparatus described above with reference to Figures 1 to 4 The source cloud-based radio access network entity or function (e.g., a cloud RAN node, such as blue node 1 410), which can participate in the proximal computing capacity and availability table. In addition, even if reference is made to a cloud-based radio access network entity or function, the device and / or cloud-based radio access network entity or function can also be a rApp (e.g., rApp 210, 310) or another device or function with similar tasks, such as a chipset, chip, module, application, etc., which can also be part of a network element or attached to a network element as a separate element, etc. It should be understood that each box and any combination thereof can be implemented by various components or combinations thereof, such as hardware, software, firmware, one or more processors and / or circuit systems.

[0144] Figure 8The device 800 shown may include a processing circuit system, a processing function, a control unit or a processor 810, such as a CPU, etc., which is suitable for implementing a near-end computing power and availability table. The processor 810 may include one or more processing parts or functions dedicated to a specific process as described below, or the process may be run in a single processor or processing function. The part for performing such a specific process may also be provided as a discrete component, or provided in one or more other processors, processing functions or processing parts, such as in a physical processor (such as a CPU) or in one or more physical or virtual entities. Reference numerals 831 and 832 represent input / output (I / O) units or functions (interfaces) connected to the processor or processing function 810. I / O units 831 and 832 may be a combined unit including a communication device toward several entities / elements, or may include a distributed structure with multiple different interfaces for different entities / elements. Reference numeral 820 represents a memory, which may be used, for example, to store data and programs / instructions to be executed by the processor or processing function 810 and / or used as a working storage device for the processor or processing function 810. It should be noted that the memory 820 may be implemented using one or more memory portions of the same or different types of memory, but may also represent external memory, such as an external database provided on a cloud server.

[0145] The processor or processing function 810 is configured to perform processing related to the above-mentioned processing. In particular, the processor or processing circuit system or function 810 includes one or more of the following sub-parts. Sub-part 811 is an application part, which can be used as a part for applying the near-end calculation table. Part 811 can be configured to perform according to Figure 6 In addition, subsection 812 is a determination section, which can be used as a section for determining the capability and / or current availability and / or predicted availability of at least one computing resource type of a target cloud-based wireless access network entity or function. Section 812 can be configured to perform Figure 6 In addition, sub-section 813 is a management section, which can be used as a section for managing computing resources. Section 813 can be configured to execute according to Figure 6 The S630 is processed.

[0146] It should be noted that, as mentioned above Figure 7 and Figure 8 The apparatuses 700 and 800 described above may include additional / additional sub-parts, which may allow the apparatuses 700 and 800 to perform the above-referenced Figures 1 to 4 as well as Figure 5 to Figure 6 The method / method steps described.

[0147] In addition, according to various examples of the embodiments, a device may include a component for retrieving information including at least one of the following items: configuration management CM data and performance management PM data of a wireless access network entity or function based on a predetermined cloud, or a resource type in a wireless access network entity or function based on a predetermined cloud, or a capability of a resource type in a wireless access network entity or function based on a predetermined cloud, or location information of a wireless access network entity or function based on a predetermined cloud; and for deriving, from the retrieved information and from the wireless access network entity or function based on the predetermined cloud, a wireless access network entity based on a source cloud and in the vicinity of a wireless access network entity or function based on a target cloud. The invention relates to a component for deriving a proximal computing capability table of a wireless access network entity or function based on a predetermined cloud, the proximal computing capability table being derived based on a proximity analysis of a wireless access network entity or function based on a predetermined cloud relative to a wireless access network entity or function based on a source cloud, and also based on a capability analysis of a computing resource type in a wireless access network entity or function based on a target cloud in the proximity of the wireless access network entity or function based on the source cloud; and a component for providing the derived proximal computing capability table, wherein the proximal computing capability table is a neighbor relationship table indicating: computing resource types and / or capabilities of computing resource types of wireless access network entities or functions based on a target cloud in the proximity of the wireless access network entity or function based on the source cloud.

[0148] According to various examples of the embodiments, the device may also include a component for retrieving additional information including at least one of the following items: PM data of a wireless access network entity or function based on a predetermined cloud, or the availability of a computing resource type in a wireless access network entity or function based on a predetermined cloud; a component for deriving a proximal computing availability table of a wireless access network entity or function based on a source cloud in the vicinity of a wireless access network entity or function based on a target cloud based on the retrieved additional information and a proximal computing capability table; and a component for providing the derived proximal computing availability table, wherein the proximal computing availability table is a dynamic neighbor relationship table indicating the current availability of a computing resource type of a wireless access network entity or function based on a target cloud in the vicinity of a wireless access network entity or function based on a source cloud.

[0149] According to various examples of the embodiments, the resource type indicated by the proximal computing capability table may refer to at least one of the following items: support for neural processing units, support for deep processing units, support for graphics processing units, support for content addressable memory CAM, support for artificial intelligence AI accelerators, or support for quantum processing units QPU; and / or wherein the capability of the resource type indicated by the proximal computing capability table may refer to at least one of the following items: supported CPU frequency, supported AI algorithm type, supported graphics memory, supported number of ray tracing cores, or supported AI and Tensor cores.

[0150] According to various examples of embodiments, the current availability indicated by the proximal computing availability table may include a predicted availability of the computing type resource within a predetermined time period.

[0151] According to various examples of embodiments, the proximal computing capability table may include the following information: a target node identifier of a target cloud-based wireless access network entity or function; and / or resource types supported by the target cloud-based wireless access network entity or function; and / or capabilities of the resource types; and / or the last update time.

[0152] According to various examples of embodiments, the proximal computing availability table may include the following information: a target node identifier of a target cloud-based wireless access network entity or function; actual and / or predicted availability of supported resource types in the target cloud-based wireless access network entity or function; and / or the last update time.

[0153] According to various examples of the embodiments, the policies related to the proximal computing capability table may include at least one of the following items: a switching policy, which is used to select a target cloud-based wireless access network entity or function with the resource type required for switching the endpoint terminal from the target cloud-based wireless access network entity or function based on the proximal computing capability table; and / or an unloading policy, which is used to offload the endpoint terminal-related services to the selected target cloud-based wireless access network entity or function with the required resource type in the target cloud-based wireless access network entity or function based on the proximal computing capability table; and / or a service guidance policy, which is used to guide services between the endpoint terminal and / or the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing capability table; and / or a spectrum sharing policy, which is used to share spectrum between the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing capability table; and / or an AI / ML training policy, which is used to train AI / ML models based on the proximal computing capability table.

[0154] According to various examples of the embodiments, the policies related to the proximal computing availability table may include at least one of the following items: a switching policy, which is used to select a target cloud-based wireless access network entity or function with a current and / or predicted available resource type for switching the endpoint terminal from the target cloud-based wireless access network entity or function based on the proximal computing availability table; and / or an unloading policy, which is used to unload the endpoint terminal related services to the selected target cloud-based wireless access network entity or function with a current and / or predicted available resource type in the target cloud-based wireless access network entity or function based on the proximal computing availability table; and / or a service steering policy, which is used to guide services between the endpoint terminal and / or the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing availability table; and / or a spectrum sharing policy, which is used to share spectrum between the target cloud-based wireless access network entity or function and / or the source cloud-based wireless access network entity or function based on the proximal computing availability table; and / or an AI / ML training policy, which is used to train an AI / ML model based on the proximal computing availability table.

[0155] According to various examples of embodiments, the device may also include a component for providing a proximal computing capability table and / or a proximal computing availability table to a source cloud-based wireless access network entity or function, and / or a component for providing the proximal computing capability table and / or the proximal computing availability table to a consumer of the proximal computing capability table and / or the proximal computing availability table that is different from the source cloud-based wireless access network entity or function; and / or wherein the device may also include a component for providing an exported proximal computing capability table and / or an exported proximal computing availability table through at least one of the following interfaces: R1 service or SMO NBI.

[0156] In addition, according to various examples of embodiments, a device may include a component for applying a proximal computing table, which is a neighbor relationship table based on the capabilities and / or current availability of at least one computing resource type of a target cloud-based wireless access network entity or function in proximity to the source cloud-based wireless access network entity or function; a component for determining the capabilities and / or current availability and / or predicted availability of at least one computing resource type of a target cloud-based wireless access network entity or function based on the applied proximal computing table; and a component for managing the computing resources of the source cloud-based wireless access network entity or function according to a predetermined proximal computing table policy based on the determination, wherein the managed computing resources are associated with an endpoint terminal, which is associated with the source cloud-based wireless access network entity or function.

[0157] According to various examples of embodiments, the proximal computing table may include a proximal computing capability table and / or a proximal computing availability table.

[0158] According to various examples of embodiments, the expression “component for…” (for example, “component for retrieving”) in combination with the above-mentioned specific functions may also be understood as “configured to…” (for example, “configured to retrieve”) in combination with the specific functions.

[0159] Furthermore, according to at least some examples of embodiments, a computer program product for a computer may be provided, the computer program product comprising a software code portion for executing the steps of any one of the above methods when the above product is run on the computer.

[0160] According to at least some examples of the embodiments, with respect to a computer program product, the computer program product may include a computer-readable medium on which the above-mentioned software code portion is stored, and / or the computer program product may be directly loadable into the internal memory of the computer and / or may be sendable via a network by means of at least one of an upload process, a download process, and a push process.

[0161] It should be understood

[0162] -The access technology through which services are transferred to and from entities in a communication network can be any suitable current or future technology, and technologies such as wireless local access network (WLAN), Worldwide Interoperability for Microwave Access (WiMAX), LTE, LTE-A, 5G, 6G, Bluetooth, infrared, etc. can be used; in addition, examples of embodiments can also apply wired technology, such as IP-based access technology, such as a wired network or a fixed line.

[0163] -Examples of embodiments suitable for being implemented as software code or portions thereof and running using a processor or processing functionality are independent of software code and may be specified using any known or future developed programming language, such as a high-level programming language, such as object-oriented C, C, C++, C#, Java, Python, Javascript, other scripting languages, etc., or a low-level programming language, such as machine language or assembler.

[0164] -The implementation of the examples of the embodiments is hardware independent and can be implemented using any known or future developed hardware technology or any mixture of these technologies, such as a microprocessor or central processing unit (CPU), a metal oxide semiconductor (MOS), a complementary MOS (CMOS), a bipolar MOS (BiMOS), a bipolar CMOS (BiCMOS), an emitter coupled logic (ECL) and / or a transistor-transistor logic (TTL).

[0165] -Examples of the embodiments may be implemented as separate devices, means, units, means or functions, or in a distributed manner, for example, one or more processors or processing functions may be used or shared in a process, or one or more processing segments or processing portions may be used and shared in a process, where one physical processor or more than one physical processor may be used to implement one or more processing portions dedicated to a specific process being described,

[0166] - the device may be implemented by a semiconductor chip, a chipset, or a (hardware) module including such a chip or chipset;

[0167] -Examples of the embodiments may also be implemented as any combination of hardware and software, such as application specific integrated circuit (ASIC) components, field programmable gate arrays (FPGA) or complex programmable logic devices (CPLD) or digital signal processor (DSP) components.

[0168] - The examples of the embodiments may also be implemented as a computer program product, including a computer usable medium having computer readable program code embodied therein, the computer readable program code being adapted to execute the processes as described in the embodiments, wherein the computer usable medium may be a non-transitory medium.

[0169] The term "circuitry" may refer to one or more or all of the following examples of embodiments:

[0170] (a) Hardware circuit implementation only (such as implementation in analog and / or digital circuitry only)

[0171] as well as

[0172] (b) a combination of hardware circuitry and software such as (where applicable):

[0173] (i) a combination of analog and / or digital hardware circuits and software / firmware, and

[0174] (ii) hardware processor(s) with software (including any portion of digital signal processor(s), software and memory(s) that work together to enable a device such as a mobile phone or server to perform various functions), and

[0175] Hardware circuit(s) and / or processor(s), such as microprocessor(s) or a portion of microprocessor(s), which requires software (e.g., firmware)

[0176] But when it is not needed, the software does not need to exist.”

[0177] This definition of circuitry applies to all uses of the term herein, including in any claims. As another example, as used herein, the term circuitry also covers an implementation of only a hardware circuit or processor (or multiple processors) or a portion of a hardware circuit or processor and its accompanying software and / or firmware. For example, the term circuitry also covers a baseband integrated circuit or processor integrated circuit for a mobile device, or a similar integrated circuit in a server, cellular network device, or other computing or network device, if applicable to the particular claim element.

[0178] The term "non-transitory" as used herein is a restriction on the medium itself (eg, tangible, rather than a signal), rather than a restriction on the persistence of data storage (eg, RAM vs. ROM).

[0179] As used herein, “at least one of the following:” and “at least one of…” and similar expressions (wherein a list of two or more elements is connected by “and” or “or”) refer to at least any one of these elements, or at least any two or more of these elements, or at least all of these elements.

[0180] Although the present disclosure has been described herein before with reference to various examples of embodiments thereof, the present disclosure is not limited thereto and various modifications may be made to the present disclosure as will be apparent to those skilled in the art.

[0181] The abbreviations used in this article have the following meanings:

[0182] 3GPP: Third Generation Partnership Project

[0183] 3GGP2: 3rd Generation Partnership Project 2

[0184] 4G: Fourth Generation

[0185] 5G: Fifth Generation

[0186] 5GC: 5G core network

[0187] 6G: Sixth Generation

[0188] AI: Artificial Intelligence

[0189] AP: Access Point

[0190] API: Application Programming Interface

[0191] BS: Base Station

[0192] CDMA: Code Division Multiple Access

[0193] CM: Configuration Management

[0194] CNC: Convergence of computing and networking

[0195] CPU: Central Processing Unit

[0196] CU: Centralized Unit

[0197] DPU: Data Processing Unit

[0198] DU: Distributed Unit

[0199] eNB: Evolved Node B

[0200] ETSI: European Telecommunications Standards Institute

[0201] gNB: Next Generation Node B

[0202] GPU: Graphics Processing Unit

[0203] HO: Handover

[0204] IEEE: Institute of Electrical and Electronics Engineers

[0205] ITU: International Telecommunication Union

[0206] LTE: Long Term Evolution

[0207] LTE-A: Long Term Evolution Advanced

[0208] MANET: Mobile Ad Hoc Network

[0209] MEC: Mobile Edge Computing

[0210] ML: Machine Learning

[0211] NB: Node B

[0212] NBI: Northbound Interface

[0213] NG-RAN: NG Radio Access Network

[0214] NPU: Neural Processing Unit

[0215] NR: New Radio

[0216] NW: Network

[0217] PM: Performance Management

[0218] RAM: Random Access Memory

[0219] RAN: Radio Access Network

[0220] RIC: RAN Intelligent Controller

[0221] ROM: Read Only Memory

[0222] SMO: Service Management and Orchestrator

[0223] SON: Self-Organizing Network

[0224] TISPAN: Telecom and Internet Convergence Services and Advanced Network Protocols

[0225] UE: User Equipment

[0226] UWB: Ultra Wideband

[0227] WCDMA: Wideband Code Division Multiple Access

[0228] WiMAX: Worldwide Interoperability for Microwave Access

[0229] WLAN: Wireless Local Area Network

Claims

1. A proximal computing method, comprising: Retrieve information that includes at least one of the following: Configuration management (CM) data and performance management (PM) data of radio access network entities or functions based on a predetermined cloud, or The resource type in the predetermined cloud-based wireless access network entity or function, or The capability of the resource type in the predetermined cloud-based radio access network entity or function, or The location information of the predetermined cloud-based wireless access network entity or function; deriving, from the retrieved information and from the predetermined cloud-based radio access network entity or function, a proximal computing capability table for a source cloud-based radio access network entity or function in the vicinity of a target cloud-based radio access network entity or function, The deriving is based on a proximity analysis of the radio access network entity or function based on the predetermined cloud relative to the radio access network entity or function based on the source cloud, and also based on a capability analysis of a computing resource type in the radio access network entity or function based on the target cloud in the proximity of the radio access network entity or function based on the source cloud; as well as providing said derived proximal computing capability table, The proximal computing capability table is a neighbor relationship table, which indicates the computing resource types and / or capabilities of the computing resource types of the wireless access network entities or functions based on the target cloud in the vicinity of the wireless access network entities or functions based on the source cloud.

2. The method according to claim 1, further comprising: Retrieve additional information including at least one of the following: PM data of a predetermined cloud-based wireless access network entity or function, or availability of computing resource types in the predetermined cloud-based radio access network entity or function; deriving, from the retrieved further information and the proximal computing capabilities table, a proximal computing availability table of the source cloud-based radio access network entity or function in proximity to the target cloud-based radio access network entity or function; as well as providing said derived proximal computing availability table, The proximal computing availability table is a dynamic neighbor relationship table, which indicates: the current availability of computing resource types of the wireless access network entities or functions based on the target cloud in the vicinity of the wireless access network entities or functions based on the source cloud.

3. The method according to claim 1 or 2, The resource type indicated by the proximal computing capability table refers to at least one of the following items: Support for Neural Processing Units, Support for deep processing units, Support for graphics processing units, Support for content addressable memory CAM, Support for artificial intelligence (AI) accelerators, or Support for Quantum Processing Units (QPUs); and / or The capability of the resource type indicated by the proximal computing capability table refers to at least one of the following items: Supported CPU frequencies, Supported AI algorithm types, Supported graphics memory, The number of ray tracing cores supported, or Supported AI and Tensor cores.

4. The method according to claim 2 or 3, wherein the current availability indicated by the proximal computing availability table comprises: A predicted availability of the computing-type resource for a predetermined time period.

5. The method according to claim 1 or 2, wherein the proximal computing capability table comprises the following information: a target node identifier based on a radio access network entity or function of the target cloud; and / or Resource types supported by wireless access network entities or functions based on the target cloud; and / or The capabilities of the resource type; and / or Last updated time.

6. The method according to claim 2 or 3, wherein the proximal computing availability table comprises the following information: The target node identifier of the target cloud-based radio access network entity or function; The actual and / or predicted availability of supported resource types in the target cloud-based radio access network entity or function; and / or Last updated time.

7. The method according to claim 1 or 2, wherein the policy associated with the proximal computing capability table comprises at least one of the following: A handover strategy for selecting, based on the proximal computing capability table, from the wireless access network entities or functions based on the target cloud, a wireless access network entity or function based on the target cloud having a resource type required by the handover endpoint terminal; and / or an offloading strategy for offloading the endpoint terminal related services to the selected target cloud-based wireless access network entity or function having the required resource type among the target cloud-based wireless access network entity or function based on the target cloud based on the proximal computing capability table; and / or A service steering strategy for steering services between the endpoint terminal, and / or a wireless access network entity or function based on the target cloud, and / or a wireless access network entity or function based on the source cloud based on the proximal computing capability table; and / or A spectrum sharing strategy for sharing spectrum between wireless access network entities or functions based on the target cloud and / or wireless access network entities or functions based on the source cloud based on the proximal computing capability table; and / or An AI / ML training strategy is used to train an AI / ML model based on the proximal computing capability table.

8. The method according to claim 2 or 3, wherein the policy associated with the proximal computing availability table comprises at least one of the following: a handover strategy for selecting, based on the proximal calculated availability table, a target cloud-based radio access network entity or function from the target cloud-based radio access network entities or functions having current and / or predicted available resource types for the handover endpoint terminal; and / or an offloading strategy for offloading the endpoint terminal related services to a target cloud-based radio access network entity or function selected from the target cloud-based radio access network entities or functions having current and / or predicted available resource types based on the proximal calculated availability table; and / or A service steering strategy for steering services between the endpoint terminal, and / or a radio access network entity or function based on the target cloud, and / or a radio access network entity or function based on the source cloud based on the proximal calculated availability table; and / or A spectrum sharing strategy for sharing spectrum between the target cloud-based radio access network entity or function and / or the source cloud-based radio access network entity or function based on the proximal calculated availability table; and / or An AI / ML training strategy is used to train an AI / ML model based on the proximal computing availability table.

9. The method according to claim 2, Wherein the providing of the derived proximal computing capability table and / or the providing of the derived proximal computing availability table comprises at least one of the following: providing the proximal computing capability table and / or the proximal computing availability table to a wireless access network entity or function based on the source cloud, and / or Providing the proximal computing capability table and / or the proximal computing availability table to a consumer of the proximal computing capability table and / or the proximal computing availability table that is different from a wireless access network entity or function based on the source cloud; and / or Wherein said providing of said derived proximal computing capability table and / or said providing of said derived proximal computing availability table is performed via at least one of the following interfaces: R1 service or SMO NBI.

10. A proximal computing device, comprising: at least one processor; as well as at least one memory storing instructions, which, when executed by the at least one processor, cause the apparatus to at least: Retrieve information that includes at least one of the following: Configuration management (CM) data and performance management (PM) data of a radio access network entity or function based on a predetermined cloud, or Based on the resource type in the radio access network entity or function of the predetermined cloud, or Based on the capabilities of resource types in the radio access network entity or function of the predetermined cloud, or Location information of a radio access network entity or function based on the predetermined cloud; deriving a proximal computing capability table of a source cloud-based radio access network entity or function in the vicinity of a target cloud-based radio access network entity or function from the retrieved information and from the predetermined cloud-based radio access network entity or function, The deriving is based on a proximity analysis of the radio access network entity or function based on the predetermined cloud relative to the radio access network entity or function based on the source cloud, and also based on a capability analysis of a computing resource type in the radio access network entity or function based on the target cloud in the proximity of the radio access network entity or function based on the source cloud; as well as providing said derived proximal computing capability table, The proximal computing capability table is a neighbor relationship table, which indicates: computing resource types and / or capabilities of computing resource types of wireless access network entities or functions based on the target cloud in the vicinity of the wireless access network entities or functions based on the source cloud.

11. The apparatus of claim 10, wherein the apparatus is further configured to: Retrieve additional information including at least one of the following: PM data of a radio access network entity or function based on the predetermined cloud, or Availability of computing resource types in a radio access network entity or function based on the predetermined cloud; deriving, from the retrieved further information and the proximal computing capabilities table, a proximal computing availability table of the source cloud-based radio access network entity or function in proximity to the target cloud-based radio access network entity or function; as well as providing said derived proximal computing availability table, The proximal computing availability table is a dynamic neighbor relationship table, which indicates: the current availability of computing resource types based on the wireless access network entity or function of the target cloud in the vicinity of the wireless access network entity or function based on the source cloud.

12. The device according to claim 10 or 11, The resource type indicated by the proximal computing capability table refers to at least one of the following items: Support for Neural Processing Units, Support for deep processing units, Support for graphics processing units, Support for content addressable memory CAM, Support for artificial intelligence (AI) accelerators, or Support for Quantum Processing Units (QPUs); and / or The capability of the resource type indicated by the proximal computing capability table refers to at least one of the following items: Supported CPU frequencies, Supported AI algorithm types, Supported graphics memory, The number of ray tracing cores supported, or Supported AI and Tensor cores.

13. The apparatus according to claim 11 or 12, wherein the current availability indicated by the proximal computing availability table comprises: A predicted availability of the computing-type resource for a predetermined time period.

14. The apparatus according to claim 10 or 11, wherein the proximal computing capability table comprises the following information: a target node identifier based on a radio access network entity or function of the target cloud; and / or Resource types supported by wireless access network entities or functions based on the target cloud; and / or The capabilities of the resource type; and / or Last updated time.

15. The apparatus according to claim 11 or 12, wherein the proximal computing availability table comprises the following information: a target node identifier based on a radio access network entity or function of the target cloud; Based on the actual and / or predicted availability of supported resource types in the radio access network entities or functions of the target cloud; and / or Last updated time.

16. The apparatus according to claim 10 or 11, wherein the policy associated with the proximal computing capability table comprises at least one of the following: A handover strategy for selecting, based on the proximal computing capability table, from the wireless access network entities or functions based on the target cloud, a wireless access network entity or function based on the target cloud that has a resource type required by the handover endpoint terminal; and / or an offloading strategy for offloading the endpoint terminal related services to the selected target cloud-based wireless access network entity or function having the required resource type in the target cloud-based wireless access network entity or function based on the target cloud based on the proximal computing capability table; and / or A service steering strategy for steering services between the endpoint terminal, and / or a wireless access network entity or function based on the target cloud, and / or a wireless access network entity or function based on the source cloud based on the proximal computing capability table; and / or A spectrum sharing strategy for sharing spectrum between wireless access network entities or functions based on the target cloud and / or wireless access network entities or functions based on the source cloud based on the proximal computing capability table; and / or The AI / ML training strategy is used to train the AI / ML model based on the proximal computing capability table.

17. The apparatus according to claim 11 or 12, wherein the policy associated with the proximal computing availability table comprises at least one of the following: a handover strategy for selecting, based on the proximal calculated availability table, a target cloud-based radio access network entity or function from the target cloud-based radio access network entities or functions having current and / or predicted available resource types for the handover endpoint terminal; and / or an offloading strategy for offloading the endpoint terminal related services to a target cloud-based radio access network entity or function selected from the target cloud-based radio access network entities or functions having current and / or predicted available resource types based on the proximal calculated availability table; and / or A service steering strategy for steering services between the endpoint terminal, and / or a radio access network entity or function based on the target cloud, and / or a radio access network entity or function based on the source cloud based on the proximal calculated availability table; and / or A spectrum sharing strategy for sharing spectrum between a radio access network entity or function based on the target cloud and / or a radio access network entity or function based on the source cloud based on the proximal calculated availability table; and / or The AI / ML training strategy is used to train the AI / ML model based on the proximal computing availability table.

18. The device according to claim 11, The device being caused to provide the derived proximal computing capability table and / or providing the derived proximal computing availability table further comprises: The device is configured to: providing the proximal computing capability table and / or the proximal computing availability table to a wireless access network entity or function based on the source cloud, and / or providing the proximal computing capability table and / or the proximal computing availability table to a consumer of the proximal computing capability table and / or the proximal computing availability table that is different from a wireless access network entity or function based on the source cloud; and / or The apparatus is configured to: provide the derived proximal computing capability table and / or provide the derived proximal computing availability table via at least one of the following interfaces: R1 service or SMO NBI.

19. A computer program product for a computer, comprising software code portions for performing the steps of any one of claims 1 to 9 when the product is run on the computer.

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