METHODS AND APPARATUS FOR DIGITAL TWIN AIDED RESILIENCY

Methods, apparatus, systems, and articles of manufacture for digital twin aided resiliency are disclosed. An example method includes accessing operational statistics corresponding to one or more physical entities, the one or more physical entities including user equipment and network equipment; updating one or more virtual entities within a virtual environment that correspond, respectively, to the one or more physical entities with the operational statistics; simulating a change to the virtual environment based on the operational statistics; generating a recommendation for the network equipment to perform a task based on the simulated change; and in response to determining a confidence of the recommendation meets a threshold confidence, provide the recommendation to the network equipment.

FIELD OF THE DISCLOSURE

This disclosure relates generally to digital twins and, more particularly, to methods and apparatus for digital twin aided resiliency.

BACKGROUND

Next generation wireless networks are expected to support diverse and advanced applications that involve smart cities, electrical grid, autonomous vehicles, etc., which demand greater reliability, lower latency, and higher speed wireless connectivity. Resiliency is an important characteristic of next generation wireless networks that is crucial for meeting application requirements. Resilient networks should be capable of overcoming factors that may cause disruptions in service (e.g., channel variations, user mobility, interference, etc.), to maximize availability and reliability of the wireless links. Over past few decades, wireless networks have evolved significantly to overcome these issues via advanced wireless signal processing techniques, frame structure, protocol design, multi-RAT dual connectivity capability, etc. However, current techniques do not tend to satisfy the requirements of next generation applications.

As used herein, “approximately” and “about” refer to dimensions that may not be exact due to manufacturing tolerances and/or other real world imperfections. As used herein “substantially real time” refers to occurrence in a near instantaneous manner recognizing there may be real world delays for computing time, transmission, etc. Thus, unless otherwise specified, “substantially real time” refers to real time+/−1 second.

DETAILED DESCRIPTION

With the advent of Edge computing, the next generation wireless networks feature the availability of powerful computing resources close to or within a wireless access network. Recent research around Edge computing has focused on providing low-latency and/or bandwidth hungry services to the users. However, techniques to improve resiliency of a wireless network (against natural/human-induced disruptions like user mobility, signal interference, channel blockage, etc.) by utilizing the Edge computing resources have not been explored.

Digital Twin (DT) is an emerging technology and is a key enabler for a range of advanced applications. For example, in intelligent transportation systems, DT can enable a range of safety and traffic efficiency related applications. As such, DT technology can be deployed in Multi-access Edge Computing (MEC) systems alongside the next generation wireless networks. In examples disclosed herein, techniques are disclosed that apply DT technology to improve the resiliency of wireless networks. In general, the DT can be applied at different layers of a wireless protocol stack to improve its resiliency features. DT-based techniques may include, but are not limited to, proactive mobility management to minimize handover failures (HOFs) and radio link failures (RLFs), robust and intelligent beam management in above 6 GHz bands (e.g., mmWave) to mitigate the undesired effects of a physical blockage, expedited application mobility in MEC systems to minimize service interruptions to the users, pre-emptive load balancing between different cells, etc.

In state-of-the-art network designs (5G, LTE, etc.), handover and/or handoff (HO) decisions for user equipment (UEs) are taken mainly based on channel measurements (received signal strength, signal-to-interference-plus-noise-ratio, etc.) reported by the UEs. There are certain chances of handover failures (HOFs) and ping-pong effects due to unforeseen scenarios, especially for high speed UEs. For example, when a high-speed UE abruptly changes from line of sight (LoS) to no line of sight (NLoS) due to a physical obstruction (e.g., a building). These issues impact the resiliency of network.

For above-6 GHz bands, 3GPP has defined procedures for the detection of beam failure at UE, and beam failure recovery (BFR) procedure through which the UE attempts to reestablish connection to the same cell via an alternative beam. This process can take several 10's of ms, and in some scenarios the alternative beams may also be affected resulting in further delays in beam failure recovery process due to multiple attempts. As such, the success of such a procedure is not guaranteed. Upon failure of the recovery process, the UE will be forced to initiate a radio link failure (RLF) procedure and cell reselection, which will induce significant duration of interruption in communication.

An API based framework for application mobility service (AMS) may be used, in which the application relocation and context transfer can be performed via MEC platform managers (MEPMs), and MEC orchestrator (MEO). Therein, the trigger for application mobility is based on the information of UE movement to a new serving cell provided by network functions like network exposure function (NEF), and radio network information (RNI) service. In such a design, the application mobility would always be delayed behind the user's mobility since the application relocation takes some time to complete. This may result in delays and/or interruptions in services provided to the mobile users.

In examples disclosed herein, techniques are disclosed in which DT techniques are used to aid in mobility management and resiliency of a network. As noted above, such DT techniques may include, but are not limited to, proactive mobility management to minimize handover failures (HOFs) and radio link failures (RLFs), robust and intelligent beam management in above 6 GHz bands (e.g., mmWave) to mitigate the undesired effects of a physical blockage, expedited application mobility in MEC systems to minimize service interruptions to the users, pre-emptive load balancing between different cells, etc. While such examples are disclosed herein, the use of DT techniques are generic and can be applied to different types of wireless networks like 5G and beyond, LTE, etc. However, the implementations in such scenarios may be different (e.g., depending on the network type). Examples disclosed herein are explained in the context of a 5G wireless network, but may be equally applicable to any other past, present, and/or future network technologies.

FIG. 1is a block diagram100showing an overview of a configuration for Edge computing, which includes a layer of processing referred to in many of the following examples as an “Edge cloud”. As shown, the Edge cloud110is co-located at an Edge location, such as an access point or base station140, a local processing hub150, or a central office120, and thus may include multiple entities, devices, and equipment instances. The Edge cloud110is located much closer to the endpoint (consumer and producer) data sources160(e.g., autonomous vehicles161, user equipment162, business and industrial equipment163, video capture devices164, drones165, smart cities and building devices166, sensors and IoT devices167, etc.) than the cloud data center130. Compute, memory, and storage resources which are offered at the edges in the Edge cloud110are critical to providing ultra-low latency response times for services and functions used by the endpoint data sources160as well as reduce network backhaul traffic from the Edge cloud110toward cloud data center130thus improving energy consumption and overall network usages among other benefits.

Compute, memory, and storage are scarce resources, and generally decrease depending on the Edge location (e.g., fewer processing resources being available at consumer endpoint devices, than at a base station, than at a central office). However, the closer that the Edge location is to the endpoint (e.g., user equipment (UE)), the more that space and power is often constrained. Thus, Edge computing attempts to reduce the amount of resources needed for network services, through the distribution of more resources which are located closer both geographically and in network access time. In this manner, Edge computing attempts to bring the compute resources to the workload data where appropriate, or, bring the workload data to the compute resources.

The following describes aspects of an Edge cloud architecture that covers multiple potential deployments and addresses restrictions that some network operators or service providers may have in their own infrastructures. These include, variation of configurations based on the Edge location (because edges at a base station level, for instance, may have more constrained performance and capabilities in a multi-tenant scenario); configurations based on the type of compute, memory, storage, fabric, acceleration, or like resources available to Edge locations, tiers of locations, or groups of locations; the service, security, and management and orchestration capabilities; and related objectives to achieve usability and performance of end services. These deployments may accomplish processing in network layers that may be considered as “near Edge”, “close Edge”, “local Edge”, “middle Edge”, or “far Edge” layers, depending on latency, distance, and timing characteristics.

Edge computing is a developing paradigm where computing is performed at or closer to the “Edge” of a network, typically through the use of a compute platform (e.g., x86 or ARM compute hardware architecture) implemented at base stations, gateways, network routers, or other devices which are much closer to endpoint devices producing and consuming the data. For example, Edge gateway servers may be equipped with pools of memory and storage resources to perform computation in real-time for low latency use-cases (e.g., autonomous driving or video surveillance) for connected client devices. Or as an example, base stations may be augmented with compute and acceleration resources to directly process service workloads for connected user equipment, without further communicating data via backhaul networks. Or as another example, central office network management hardware may be replaced with standardized compute hardware that performs virtualized network functions and offers compute resources for the execution of services and consumer functions for connected devices. Within Edge computing networks, there may be scenarios in services which the compute resource will be “moved” to the data, as well as scenarios in which the data will be “moved” to the compute resource. Or as an example, base station compute, acceleration and network resources can provide services in order to scale to workload demands on an as needed basis by activating dormant capacity (subscription, capacity on demand) in order to manage corner cases, emergencies or to provide longevity for deployed resources over a significantly longer implemented lifecycle.

FIG. 2illustrates operational layers among endpoints, an Edge cloud, and cloud computing environments. Specifically,FIG. 2depicts examples of computational use cases205, utilizing the Edge cloud110among multiple illustrative layers of network computing. The layers begin at an endpoint (devices and things) layer200, which accesses the Edge cloud110to conduct data creation, analysis, and data consumption activities. The Edge cloud110may span multiple network layers, such as an Edge devices layer210having gateways, on-premise servers, or network equipment (nodes215) located in physically proximate Edge systems; a network access layer220, encompassing base stations, radio processing units, network hubs, regional data centers (DC), or local network equipment (equipment225); and any equipment, devices, or nodes located therebetween (in layer212, not illustrated in detail). The network communications within the Edge cloud110and among the various layers may occur via any number of wired or wireless mediums, including via connectivity architectures and technologies not depicted.

Examples of latency, resulting from network communication distance and processing time constraints, may range from less than a millisecond (ms) when among the endpoint layer200, under 5 ms at the Edge devices layer210, to even between 10 to 40 ms when communicating with nodes at the network access layer220. Beyond the Edge cloud110are core network230and cloud data center240layers, each with increasing latency (e.g., between 50-60 ms at the core network layer230, to 100 or more ms at the cloud data center layer). As a result, operations at a core network data center235or a cloud data center245, with latencies of at least 50 to 100 ms or more, will not be able to accomplish many time-critical functions of the use cases205. Each of these latency values are provided for purposes of illustration and contrast; it will be understood that the use of other access network mediums and technologies may further reduce the latencies. In some examples, respective portions of the network may be categorized as “close Edge”, “local Edge”, “near Edge”, “middle Edge”, or “far Edge” layers, relative to a network source and destination. For instance, from the perspective of the core network data center235or a cloud data center245, a central office or content data network may be considered as being located within a “near Edge” layer (“near” to the cloud, having high latency values when communicating with the devices and endpoints of the use cases205), whereas an access point, base station, on-premise server, or network gateway may be considered as located within a “far Edge” layer (“far” from the cloud, having low latency values when communicating with the devices and endpoints of the use cases205). It will be understood that other categorizations of a particular network layer as constituting a “close”, “local”, “near”, “middle”, or “far” Edge may be based on latency, distance, number of network hops, or other measurable characteristics, as measured from a source in any of the network layers200-240.

The various use cases205may access resources under usage pressure from incoming streams, due to multiple services utilizing the Edge cloud. To achieve results with low latency, the services executed within the Edge cloud110balance varying requirements in terms of: (a) Priority (throughput or latency) and Quality of Service (QoS) (e.g., traffic for an autonomous car may have higher priority than a temperature sensor in terms of response time requirement; or, a performance sensitivity/bottleneck may exist at a compute/accelerator, memory, storage, or network resource, depending on the application); (b) Reliability and Resiliency (e.g., some input streams need to be acted upon and the traffic routed with mission-critical reliability, where as some other input streams may be tolerate an occasional failure, depending on the application); and (c) Physical constraints (e.g., power, cooling and form-factor, etc.).

The end-to-end service view for these use cases involves the concept of a service-flow and is associated with a transaction. The transaction details the overall service requirement for the entity consuming the service, as well as the associated services for the resources, workloads, workflows, and business functional and business level requirements. The services executed with the “terms” described may be managed at each layer in a way to assure real time, and runtime contractual compliance for the transaction during the lifecycle of the service. When a component in the transaction is missing its agreed to Service Level Agreement (SLA), the system as a whole (components in the transaction) may provide the ability to (1) understand the impact of the SLA violation, and (2) augment other components in the system to resume overall transaction SLA, and (3) implement steps to remediate.

Thus, with these variations and service features in mind, Edge computing within the Edge cloud110may provide the ability to serve and respond to multiple applications of the use cases205(e.g., object tracking, video surveillance, connected cars, etc.) in real-time or near real-time, and meet ultra-low latency requirements for these multiple applications. These advantages enable a whole new class of applications (e.g., Virtual Network Functions (VNFs), Function as a Service (FaaS), Edge as a Service (EaaS), standard processes, etc.), which cannot leverage conventional cloud computing due to latency or other limitations.

However, with the advantages of Edge computing comes the following caveats. The devices located at the Edge are often resource constrained and therefore there is pressure on usage of Edge resources. Typically, this is addressed through the pooling of memory and storage resources for use by multiple users (tenants) and devices. The Edge may be power and cooling constrained and therefore the power usage needs to be accounted for by the applications that are consuming the most power. There may be inherent power-performance tradeoffs in these pooled memory resources, as many of them are likely to use emerging memory technologies, where more power requires greater memory bandwidth. Likewise, improved security of hardware and root of trust trusted functions are also required, because Edge locations may be unmanned and may even need permissioned access (e.g., when housed in a third-party location). Such issues are magnified in the Edge cloud110in a multi-tenant, multi-owner, or multi-access setting, where services and applications are requested by many users, especially as network usage dynamically fluctuates and the composition of the multiple stakeholders, use cases, and services changes.

At a more generic level, an Edge computing system may be described to encompass any number of deployments at the previously discussed layers operating in the Edge cloud110(network layers200-240), which provide coordination from client and distributed computing devices. One or more Edge gateway nodes, one or more Edge aggregation nodes, and one or more core data centers may be distributed across layers of the network to provide an implementation of the Edge computing system by or on behalf of a telecommunication service provider (“telco”, or “TSP”), internet-of-things service provider, cloud service provider (CSP), enterprise entity, or any other number of entities. Various implementations and configurations of the Edge computing system may be provided dynamically, such as when orchestrated to meet service objectives.

As such, the Edge cloud110is formed from network components and functional features operated by and within Edge gateway nodes, Edge aggregation nodes, or other Edge compute nodes among network layers210-230. The Edge cloud110thus may be embodied as any type of network that provides Edge computing and/or storage resources which are proximately located to radio access network (RAN) capable endpoint devices (e.g., mobile computing devices, IoT devices, smart devices, etc.), which are discussed herein. In other words, the Edge cloud110may be envisioned as an “Edge” which connects the endpoint devices and traditional network access points that serve as an ingress point into service provider core networks, including mobile carrier networks (e.g., Global System for Mobile Communications (GSM) networks, Long-Term Evolution (LTE) networks, 5G/6G networks, etc.), while also providing storage and/or compute capabilities. Other types and forms of network access (e.g., Wi-Fi, long-range wireless, wired networks including optical networks, etc.) may also be utilized in place of or in combination with such 3GPP carrier networks.

The network components of the Edge cloud110may be servers, multi-tenant servers, appliance computing devices, and/or any other type of computing devices. For example, the Edge cloud110may include an appliance computing device that is a self-contained electronic device including a housing, a chassis, a case, or a shell. In some circumstances, the housing may be dimensioned for portability such that it can be carried by a human and/or shipped. Example housings may include materials that form one or more exterior surfaces that partially or fully protect contents of the appliance, in which protection may include weather protection, hazardous environment protection (e.g., electromagnetic interference (EMI), vibration, extreme temperatures, etc.), and/or enable submergibility. Example housings may include power circuitry to provide power for stationary and/or portable implementations, such as alternating current (AC) power inputs, direct current (DC) power inputs, AC/DC converter(s), DC/AC converter(s), DC/DC converter(s), power regulators, transformers, charging circuitry, batteries, wired inputs, and/or wireless power inputs. Example housings and/or surfaces thereof may include or connect to mounting hardware to enable attachment to structures such as buildings, telecommunication structures (e.g., poles, antenna structures, etc.), and/or racks (e.g., server racks, blade mounts, etc.). Example housings and/or surfaces thereof may support one or more sensors (e.g., temperature sensors, vibration sensors, light sensors, acoustic sensors, capacitive sensors, proximity sensors, infrared or other visual thermal sensors, etc.). One or more such sensors may be contained in, carried by, or otherwise embedded in the surface and/or mounted to the surface of the appliance. Example housings and/or surfaces thereof may support mechanical connectivity, such as propulsion hardware (e.g., wheels, rotors such as propellers, etc.) and/or articulating hardware (e.g., robot arms, pivotable appendages, etc.). In some circumstances, the sensors may include any type of input devices such as user interface hardware (e.g., buttons, switches, dials, sliders, microphones, etc.). In some circumstances, example housings include output devices contained in, carried by, embedded therein and/or attached thereto. Output devices may include displays, touchscreens, lights, light-emitting diodes (LEDs), speakers, input/output (I/O) ports (e.g., universal serial bus (USB)), etc. In some circumstances, Edge devices are devices presented in the network for a specific purpose (e.g., a traffic light), but may have processing and/or other capacities that may be utilized for other purposes. Such Edge devices may be independent from other networked devices and may be provided with a housing having a form factor suitable for its primary purpose; yet be available for other compute tasks that do not interfere with its primary task. Edge devices include Internet of Things devices. The appliance computing device may include hardware and software components to manage local issues such as device temperature, vibration, resource utilization, updates, power issues, physical and network security, etc. Example hardware for implementing an appliance computing device is described in conjunction withFIG. 7B. The Edge cloud110may also include one or more servers and/or one or more multi-tenant servers. Such a server may include an operating system and implement a virtual computing environment. A virtual computing environment may include a hypervisor managing (e.g., spawning, deploying, commissioning, destroying, decommissioning, etc.) one or more virtual machines, one or more containers, etc. Such virtual computing environments provide an execution environment in which one or more applications and/or other software, code, or scripts may execute while being isolated from one or more other applications, software, code, or scripts.

InFIG. 3, various client endpoints310(in the form of mobile devices, computers, autonomous vehicles, business computing equipment, industrial processing equipment) exchange requests and responses that are specific to the type of endpoint network aggregation. For instance, client endpoints310may obtain network access via a wired broadband network, by exchanging requests and responses322through an on-premise network system332. Some client endpoints310, such as mobile computing devices, may obtain network access via a wireless broadband network, by exchanging requests and responses324through an access point (e.g., a cellular network tower)334. Some client endpoints310, such as autonomous vehicles may obtain network access for requests and responses326via a wireless vehicular network through a street-located network system336. However, regardless of the type of network access, the TSP may deploy aggregation points342,344within the Edge cloud110to aggregate traffic and requests. Thus, within the Edge cloud110, the TSP may deploy various compute and storage resources, such as at Edge aggregation nodes340, to provide requested content. The Edge aggregation nodes340and other systems of the Edge cloud110are connected to a cloud or data center360, which uses a backhaul network350to fulfill higher-latency requests from a cloud/data center for websites, applications, database servers, etc. Additional or consolidated instances of the Edge aggregation nodes340and the aggregation points342,344, including those deployed on a single server framework, may also be present within the Edge cloud110or other areas of the TSP infrastructure.

It should be appreciated that the Edge computing systems and arrangements discussed herein may be applicable in various solutions, services, and/or use cases involving mobility. As an example,FIG. 4shows a simplified vehicle compute and communication use case involving mobile access to applications in an Edge computing system400that implements an Edge cloud110. In this use case, respective client compute nodes410may be embodied as in-vehicle compute systems (e.g., in-vehicle navigation and/or infotainment systems) located in corresponding vehicles which communicate with the Edge gateway nodes420during traversal of a roadway. For instance, the Edge gateway nodes420may be located in a roadside cabinet or other enclosure built-into a structure having other, separate, mechanical utility, which may be placed along the roadway, at intersections of the roadway, or other locations near the roadway. As respective vehicles traverse along the roadway, the connection between its client compute node410and a particular Edge gateway device420may propagate so as to maintain a consistent connection and context for the client compute node410. Likewise, mobile Edge nodes may aggregate at the high priority services or according to the throughput or latency resolution requirements for the underlying service(s) (e.g., in the case of drones). The respective Edge gateway devices420include an amount of processing and storage capabilities and, as such, some processing and/or storage of data for the client compute nodes410may be performed on one or more of the Edge gateway devices420.

The Edge gateway devices420may communicate with one or more Edge resource nodes440, which are illustratively embodied as compute servers, appliances or components located at or in a communication base station442(e.g., a base station of a cellular network). As discussed above, the respective Edge resource nodes440include an amount of processing and storage capabilities and, as such, some processing and/or storage of data for the client compute nodes410may be performed on the Edge resource node440. For example, the processing of data that is less urgent or important may be performed by the Edge resource node440, while the processing of data that is of a higher urgency or importance may be performed by the Edge gateway devices420(depending on, for example, the capabilities of each component, or information in the request indicating urgency or importance). Based on data access, data location or latency, work may continue on Edge resource nodes when the processing priorities change during the processing activity. Likewise, configurable systems or hardware resources themselves can be activated (e.g., through a local orchestrator) to provide additional resources to meet the new demand (e.g., adapt the compute resources to the workload data).

The Edge resource node(s)440also communicate with the core data center450, which may include compute servers, appliances, and/or other components located in a central location (e.g., a central office of a cellular communication network). The core data center450may provide a gateway to the global network cloud460(e.g., the Internet) for the Edge cloud110operations formed by the Edge resource node(s)440and the Edge gateway devices420. Additionally, in some examples, the core data center450may include an amount of processing and storage capabilities and, as such, some processing and/or storage of data for the client compute devices may be performed on the core data center450(e.g., processing of low urgency or importance, or high complexity).

The Edge gateway nodes420or the Edge resource nodes440may offer the use of stateful applications432and a geographic distributed database434. Although the applications432and database434are illustrated as being horizontally distributed at a layer of the Edge cloud110, it will be understood that resources, services, or other components of the application may be vertically distributed throughout the Edge cloud (including, part of the application executed at the client compute node410, other parts at the Edge gateway nodes420or the Edge resource nodes440, etc.). Additionally, as stated previously, there can be peer relationships at any level to meet service objectives and obligations. Further, the data for a specific client or application can move from Edge to Edge based on changing conditions (e.g., based on acceleration resource availability, following the car movement, etc.). For instance, based on the “rate of decay” of access, prediction can be made to identify the next owner to continue, or when the data or computational access will no longer be viable. These and other services may be utilized to complete the work that is needed to keep the transaction compliant and lossless.

In further scenarios, a container436(or pod of containers) may be flexibly migrated from an Edge node420to other Edge nodes (e.g.,420,440, etc.) such that the container with an application and workload does not need to be reconstituted, re-compiled, re-interpreted in order for migration to work. However, in such settings, there may be some remedial or “swizzling” translation operations applied. For example, the physical hardware at node440may differ from Edge gateway node420and therefore, the hardware abstraction layer (HAL) that makes up the bottom Edge of the container will be re-mapped to the physical layer of the target Edge node. This may involve some form of late-binding technique, such as binary translation of the HAL from the container native format to the physical hardware format, or may involve mapping interfaces and operations. A pod controller may be used to drive the interface mapping as part of the container lifecycle, which includes migration to/from different hardware environments.

The scenarios encompassed byFIG. 4may utilize various types of mobile Edge nodes, such as an Edge node hosted in a vehicle (e.g., a car, truck, tram, train, etc.) or other mobile unit, as the Edge node will move to other geographic locations along the platform hosting it. With vehicle-to-vehicle communications, individual vehicles may even act as network Edge nodes for other cars, (e.g., to perform caching, reporting, data aggregation, etc.). Thus, it will be understood that the application components provided in various Edge nodes may be distributed in static or mobile settings, including coordination between some functions or operations at individual endpoint devices or the Edge gateway nodes420, some others at the Edge resource node440, and others in the core data center450or global network cloud460.

In further configurations, the Edge computing system may implement FaaS computing capabilities through the use of respective executable applications and functions. In an example, a developer writes function code (e.g., “computer code” herein) representing one or more computer functions, and the function code is uploaded to a FaaS platform provided by, for example, an Edge node or data center. A trigger such as, for example, a service use case or an Edge processing event, initiates the execution of the function code with the FaaS platform.

In an example of FaaS, a container is used to provide an environment in which function code (e.g., an application which may be provided by a third party) is executed. The container may be any isolated-execution entity such as a process, a Docker or Kubernetes container, a virtual machine, etc. Within the Edge computing system, various datacenter, Edge, and endpoint (including mobile) devices are used to “spin up” functions (e.g., activate and/or allocate function actions) that are scaled on demand. The function code gets executed on the physical infrastructure (e.g., Edge computing node) device and underlying virtualized containers. Finally, the function(s) is/are “spun down” (e.g., deactivated and/or deallocated) on the infrastructure in response to the execution being completed.

Further aspects of FaaS may enable deployment of Edge functions in a service fashion, including a support of respective functions that support Edge computing as a service (Edge-as-a-Service or “EaaS”). Additional features of FaaS may include: a granular billing component that enables customers (e.g., computer code developers) to pay only when their code gets executed; common data storage to store data for reuse by one or more functions; orchestration and management among individual functions; function execution management, parallelism, and consolidation; management of container and function memory spaces; coordination of acceleration resources available for functions; and distribution of functions between containers (including “warm” containers, already deployed or operating, versus “cold” which require initialization, deployment, or configuration).

In an example, Edge provisioning node444includes one or more servers and one or more storage devices/disks. The storage devices and/or storage disks host computer readable instructions such as the example computer readable instructions782ofFIG. 7B, as described below. Similarly to Edge gateway devices420described above, the one or more servers of the Edge provisioning node444are in communication with a base station442or other network communication entity. In some examples, the one or more servers are responsive to requests to transmit the software instructions to a requesting party as part of a commercial transaction. Payment for the delivery, sale, and/or license of the software instructions may be handled by the one or more servers of the software distribution platform and/or via a third-party payment entity. The servers enable purchasers and/or licensors to download the computer readable instructions782from the Edge provisioning node444. For example, the software instructions, which may correspond to the example computer readable instructions782ofFIG. 7B, may be downloaded to the example processor platform/s, which is to execute the computer readable instructions to implement the methods described herein.

In some examples, the processor platform(s) that execute the computer readable instructions can be physically located in different geographic locations, legal jurisdictions, etc. In some examples, one or more servers of the Edge provisioning node444periodically offer, transmit, and/or force updates to the software instructions (e.g., the example computer readable instructions782ofFIG. 7B) to ensure improvements, patches, updates, etc. are distributed and applied to the software instructions implemented at the end user devices. In some examples, different components of the computer readable instructions can be distributed from different sources and/or to different processor platforms; for example, different libraries, plug-ins, components, and other types of compute modules, whether compiled or interpreted, can be distributed from different sources and/or to different processor platforms. For example, a portion of the software instructions (e.g., a script that is not, in itself, executable) may be distributed from a first source while an interpreter (capable of executing the script) may be distributed from a second source.

FIG. 5illustrates a mobile Edge system reference architecture (or MEC architecture)500, such as is indicated by ETSI MEC specifications.FIG. 5specifically illustrates a MEC architecture500with MEC hosts502and504providing functionalities in accordance with the ETSI GS MEC-003 specification. In some aspects, enhancements to the MEC platform532and the MEC platform manager506may be used for providing specific computing functions within the MEC architecture500.

Referring toFIG. 5, the MEC network architecture500can include MEC hosts502and504, a virtualization infrastructure manager (VIM)508, an MEC platform manager506, an MEC orchestrator510, an operations support system512, a user app proxy514, a UE app518running on UE520, and CFS portal516. The MEC host502can include a MEC platform532with filtering rules control component540, a DNS handling component542, a service registry538, and MEC services536. The MEC services536can include at least one scheduler, which can be used to select resources for instantiating MEC apps (or NFVs)526,527, and528upon virtualization infrastructure522. The MEC apps526and528can be configured to provide services530and531, which can include processing network communications traffic of different types associated with one or more wireless connections (e.g., connections to one or more RAN or telecom-core network entities). The MEC app505instantiated within MEC host504can be similar to the MEC apps526-7728instantiated within MEC host502. The virtualization infrastructure522includes a data plane524coupled to the MEC platform via an MP2 interface. Additional interfaces between various network entities of the MEC architecture500are illustrated inFIG. 5.

The MEC platform manager506can include MEC platform element management component544, MEC app rules and requirements management component546, and MEC app lifecycle management component548. The various entities within the MEC architecture500can perform functionalities as disclosed by the ETSI GS MEC-003 specification. In some aspects, the remote application (or app)550is configured to communicate with the MEC host502(e.g., with the MEC apps526-528) via the MEC orchestrator510and the MEC platform manager506.

FIG. 6illustrates an example MEC service architecture600. MEC service architecture600includes the MEC service605, a multi-access edge (ME) platform610(corresponding to MEC platform532), and applications (Apps) 1 to N (where N is a number). As an example, the App 1 may be a content delivery network (CDN) app/service hosting 1 to n sessions (where n is a number that is the same or different than N), App 2 may be a gaming app/service which is shown as hosting two sessions, and App N may be some other app/service which is shown as a single instance (e.g., not hosting any sessions). Each App may be a distributed application that partitions tasks and/or workloads between resource providers (e.g., servers such as ME platform610) and consumers (e.g., UEs, user apps instantiated by individual UEs, other servers/services, network functions, application functions, etc.). Each session represents an interactive information exchange between two or more elements, such as a client-side app and its corresponding server-side app, a user app instantiated by a UE and a MEC app instantiated by the ME platform610, and/or the like. A session may begin when App execution is started or initiated and ends when the App exits or terminates execution. Additionally or alternatively, a session may begin when a connection is established and may end when the connection is terminated. Each App session may correspond to a currently running App instance. Additionally or alternatively, each session may correspond to a Protocol Data Unit (PDU) session or multi-access (MA) PDU session. A PDU session is an association between a UE and a DN that provides a PDU connectivity service, which is a service that provides for the exchange of PDUs between a UE and a Data Network. An MA PDU session is a PDU Session that provides a PDU connectivity service, which can use one access network at a time, or simultaneously a 3GPP access network and a non-3GPP access network. Furthermore, each session may be associated with a session identifier (ID) which is data the uniquely identifies a session, and each App (or App instance) may be associated with an App ID (or App instance ID) which is data the uniquely identifies an App (or App instance).

The MEC service605provides one or more MEC services536to MEC service consumers (e.g., Apps 1 to N). The MEC service605may optionally run as part of the platform (e.g., ME platform610) or as an application (e.g., ME app). Different Apps 1 to N, whether managing a single instance or several sessions (e.g., CDN), may request specific service info per their requirements for the whole application instance or different requirements per session. The MEC service605may aggregate all the requests and act in a manner that will help optimize the BW usage and improve Quality of Experience (QoE) for applications.

The MEC service605provides a MEC service API that supports both queries and subscriptions (e.g., pub/sub mechanism) that are used over a Representational State Transfer (“REST” or “RESTful”) API or over alternative transports such as a message bus. For RESTful architectural style, the MEC APIs contain the HTTP protocol bindings for traffic management functionality.

Each Hypertext Transfer Protocol (HTTP) message is either a request or a response. A server listens on a connection for a request, parses each message received, interprets the message semantics in relation to the identified request target, and responds to that request with one or more response messages. A client constructs request messages to communicate specific intentions, examines received responses to see if the intentions were carried out, and determines how to interpret the results. The target of an HTTP request is called a “resource”. Additionally or alternatively, a “resource” is an object with a type, associated data, a set of methods that operate on it, and relationships to other resources if applicable. Each resource is identified by at least one Uniform Resource Identifier (URI), and a resource URI identifies at most one resource. Resources are acted upon by the RESTful API using HTTP methods (e.g., POST, GET, PUT, DELETE, etc.). With every HTTP method, one resource URI is passed in the request to address one particular resource. Operations on resources affect the state of the corresponding managed entities.

Considering that a resource could be anything, and that the uniform interface provided by HTTP is similar to a window through which one can observe and act upon such a thing only through the communication of messages to some independent actor on the other side, an abstraction is needed to represent (“take the place of”) the current or desired state of that thing in our communications. That abstraction is called a representation. For the purposes of HTTP, a “representation” is information that is intended to reflect a past, current, or desired state of a given resource, in a format that can be readily communicated via the protocol. A representation comprises a set of representation metadata and a potentially unbounded stream of representation data. Additionally or alternatively, a resource representation is a serialization of a resource state in a particular content format.

An origin server might be provided with, or be capable of generating, multiple representations that are each intended to reflect the current state of a target resource. In such cases, some algorithm is used by the origin server to select one of those representations as most applicable to a given request, usually based on content negotiation. This “selected representation” is used to provide the data and metadata for evaluating conditional requests constructing the payload for response messages (e.g., 200 OK, 304 Not Modified responses to GET, and the like). A resource representation is included in the payload body of an HTTP request or response message. Whether a representation is required or not allowed in a request depends on the HTTP method used (see e.g., Fielding et al., “Hypertext Transfer Protocol (HTTP/1.1): Semantics and Content”, IETF RFC 7231 (June 2014)).

The MEC API resource Universal Resource Indicators (URIs) are discussed in various ETSI MEC standards, such as those mentioned herein. The MTS API supports additional application-related error information to be provided in the HTTP response when an error occurs (see e.g., clause 6.15 of ETSI GS MEC 009 V2.1.1 (2019-01) (“[MEC009]”)). The syntax of each resource URI follows [MEC009], as well as Berners-Lee et al., “Uniform Resource Identifier (URI): Generic Syntax”, IETF Network Working Group, RFC 3986 (January 2005) and/or Nottingham, “URI Design and Ownership”, IETF RFC 8820 (June 2020). In the RESTful MEC service APIs, including the VIS API, the resource URI structure for each API has the following structure:

Here, “apiRoot” includes the scheme (“https”), host and optional port, and an optional prefix string. The “apiName” defines the name of the API (e.g., MTS API, RNI API, etc.). The “apiVersion” represents the version of the API, and the “apiSpecificSuffixes” define the tree of resource URIs in a particular API. The combination of “apiRoot”, “apiName” and “apiVersion” is called the root URI. The “apiRoot” is under control of the deployment, whereas the remaining parts of the URI are under control of the API specification. In the above root, “apiRoot” and “apiName” are discovered using the service registry (see e.g., service registry538inFIG. 5). It includes the scheme (“http” or “https”), host and optional port, and an optional prefix string. For the a given MEC API, the “apiName” may be set to “mec” and “apiVersion” may be set to a suitable version number (e.g., “v1” for version 1). The MEC APIs support HTTP over TLS (also known as HTTPS). All resource URIs in the MEC API procedures are defined relative to the above root URI.

The JSON content format may also be supported. The JSON format is signaled by the content type “application/j son”. The MTS API may use the OAuth 2.0 client credentials grant type with bearer tokens (see e.g., [MEC009]). The token endpoint can be discovered as part of the service availability query procedure defined in [MEC009]. The client credentials may be provisioned into the MEC app using known provisioning mechanisms.

In further examples, any of the compute nodes or devices discussed with reference to the present Edge computing systems and environment may be fulfilled based on the components depicted inFIGS. 7A and 7B. Respective Edge compute nodes may be embodied as a type of device, appliance, computer, or other “thing” capable of communicating with other Edge, networking, or endpoint components. For example, an Edge compute device may be embodied as a personal computer, server, smartphone, a mobile compute device, a smart appliance, an in-vehicle compute system (e.g., a navigation system), a self-contained device having an outer case, shell, etc., or other device or system capable of performing the described functions.

In the simplified example depicted inFIG. 7A, an Edge compute node700includes a compute engine (also referred to herein as “compute circuitry”)702, an input/output (I/O) subsystem (also referred to herein as “I/O circuitry”)708, data storage (also referred to herein as “data storage circuitry”)710, a communication circuitry subsystem712, and, optionally, one or more peripheral devices (also referred to herein as “peripheral device circuitry”)714. In other examples, respective compute devices may include other or additional components, such as those typically found in a computer (e.g., a display, peripheral devices, etc.). Additionally, in some examples, one or more of the illustrative components may be incorporated in, or otherwise form a portion of, another component.

The compute node700may be embodied as any type of engine, device, or collection of devices capable of performing various compute functions. In some examples, the compute node700may be embodied as a single device such as an integrated circuit, an embedded system, a field-programmable gate array (FPGA), a system-on-a-chip (SOC), or other integrated system or device. In the illustrative example, the compute node700includes or is embodied as a processor (also referred to herein as “processor circuitry”)704and a memory (also referred to herein as “memory circuitry”)706. The processor704may be embodied as any type of processor(s) capable of performing the functions described herein (e.g., executing an application). For example, the processor704may be embodied as a multi-core processor(s), a microcontroller, a processing unit, a specialized or special purpose processing unit, or other processor or processing/controlling circuit.

In some examples, the processor704may be embodied as, include, or be coupled to an FPGA, an application specific integrated circuit (ASIC), reconfigurable hardware or hardware circuitry, or other specialized hardware to facilitate performance of the functions described herein. Also in some examples, the processor704may be embodied as a specialized x-processing unit (xPU) also known as a data processing unit (DPU), infrastructure processing unit (IPU), or network processing unit (NPU). Such an xPU may be embodied as a standalone circuit or circuit package, integrated within an SOC, or integrated with networking circuitry (e.g., in a SmartNIC, or enhanced SmartNIC), acceleration circuitry, storage devices, storage disks, or AI hardware (e.g., GPUs, programmed FPGAs, or ASICs tailored to implement an AI model such as a neural network). Such an xPU may be designed to receive, retrieve, and/or otherwise obtain programming to process one or more data streams and perform specific tasks and actions for the data streams (such as hosting microservices, performing service management or orchestration, organizing or managing server or data center hardware, managing service meshes, or collecting and distributing telemetry), outside of the CPU or general purpose processing hardware. However, it will be understood that an xPU, an SOC, a CPU, and other variations of the processor704may work in coordination with each other to execute many types of operations and instructions within and on behalf of the compute node700.

The memory706may be embodied as any type of volatile (e.g., dynamic random access memory (DRAM), etc.) or non-volatile memory or data storage capable of performing the functions described herein. Volatile memory may be a storage medium that requires power to maintain the state of data stored by the medium. Non-limiting examples of volatile memory may include various types of random access memory (RAM), such as DRAM or static random access memory (SRAM). One particular type of DRAM that may be used in a memory module is synchronous dynamic random access memory (SDRAM).

In an example, the memory device (e.g., memory circuitry) is any number of block addressable memory devices, such as those based on NAND or NOR technologies (for example, Single-Level Cell (“SLC”), Multi-Level Cell (“MLC”), Quad-Level Cell (“QLC”), Tri-Level Cell (“TLC”), or some other NAND). In some examples, the memory device(s) includes a byte-addressable write-in-place three dimensional crosspoint memory device, or other byte addressable write-in-place non-volatile memory (NVM) devices, such as single or multi-level Phase Change Memory (PCM) or phase change memory with a switch (PCMS), NVM devices that use chalcogenide phase change material (for example, chalcogenide glass), resistive memory including metal oxide base, oxygen vacancy base and Conductive Bridge Random Access Memory (CB-RAM), nanowire memory, ferroelectric transistor random access memory (FeTRAM), magneto resistive random access memory (MRAM) that incorporates memristor technology, spin transfer torque (STT)-MRAM, a spintronic magnetic junction memory based device, a magnetic tunneling junction (MTJ) based device, a DW (Domain Wall) and SOT (Spin Orbit Transfer) based device, a thyristor based memory device, a combination of any of the above, or other suitable memory. A memory device may also include a three-dimensional crosspoint memory device (e.g., Intel® 3D XPoint™ memory), or other byte addressable write-in-place nonvolatile memory devices. The memory device may refer to the die itself and/or to a packaged memory product. In some examples, 3D crosspoint memory (e.g., Intel® 3D XPoint™ memory) may include a transistor-less stackable cross point architecture in which memory cells sit at the intersection of word lines and bit lines and are individually addressable and in which bit storage is based on a change in bulk resistance. In some examples, all or a portion of the memory706may be integrated into the processor704. The memory706may store various software and data used during operation such as one or more applications, data operated on by the application(s), libraries, and drivers.

In some examples, resistor-based and/or transistor-less memory architectures include nanometer scale phase-change memory (PCM) devices in which a volume of phase-change material resides between at least two electrodes. Portions of the example phase-change material exhibit varying degrees of crystalline phases and amorphous phases, in which varying degrees of resistance between the at least two electrodes can be measured. In some examples, the phase-change material is a chalcogenide-based glass material. Such resistive memory devices are sometimes referred to as memristive devices that remember the history of the current that previously flowed through them. Stored data is retrieved from example PCM devices by measuring the electrical resistance, in which the crystalline phases exhibit a relatively lower resistance value(s) (e.g., logical “0”) when compared to the amorphous phases having a relatively higher resistance value(s) (e.g., logical “1”).

Example PCM devices store data for long periods of time (e.g., approximately 10 years at room temperature). Write operations to example PCM devices (e.g., set to logical “0”, set to logical “1”, set to an intermediary resistance value) are accomplished by applying one or more current pulses to the at least two electrodes, in which the pulses have a particular current magnitude and duration. For instance, a long low current pulse (SET) applied to the at least two electrodes causes the example PCM device to reside in a low-resistance crystalline state, while a comparatively short high current pulse (RESET) applied to the at least two electrodes causes the example PCM device to reside in a high-resistance amorphous state.

In some examples, implementation of PCM devices facilitates non-von Neumann computing architectures that enable in-memory computing capabilities. Generally speaking, traditional computing architectures include a central processing unit (CPU) communicatively connected to one or more memory devices via a bus. As such, a finite amount of energy and time is consumed to transfer data between the CPU and memory, which is a known bottleneck of von Neumann computing architectures. However, PCM devices minimize and, in some cases, eliminate data transfers between the CPU and memory by performing some computing operations in-memory. Stated differently, PCM devices both store information and execute computational tasks. Such non-von Neumann computing architectures may implement vectors having a relatively high dimensionality to facilitate hyperdimensional computing, such as vectors having 10,000 bits. Relatively large bit width vectors enable computing paradigms modeled after the human brain, which also processes information analogous to wide bit vectors.

The compute circuitry702is communicatively coupled to other components of the compute node700via the I/O subsystem708, which may be embodied as circuitry and/or components to facilitate input/output operations with the compute circuitry702(e.g., with the processor704and/or the main memory706) and other components of the compute circuitry702. For example, the I/O subsystem708may be embodied as, or otherwise include, memory controller hubs, input/output control hubs, integrated sensor hubs, firmware devices, communication links (e.g., point-to-point links, bus links, wires, cables, light guides, printed circuit board traces, etc.), and/or other components and subsystems to facilitate the input/output operations. In some examples, the I/O subsystem708may form a portion of a system-on-a-chip (SoC) and be incorporated, along with one or more of the processor704, the memory706, and other components of the compute circuitry702, into the compute circuitry702.

The one or more illustrative data storage devices/disks710may be embodied as one or more of any type(s) of physical device(s) configured for short-term or long-term storage of data such as, for example, memory devices, memory, circuitry, memory cards, flash memory, hard disk drives (HDDs), solid-state drives (SSDs), and/or other data storage devices/disks. Individual data storage devices/disks710may include a system partition that stores data and firmware code for the data storage device/disk710. Individual data storage devices/disks710may also include one or more operating system partitions that store data files and executables for operating systems depending on, for example, the type of compute node700.

The communication circuitry712may be embodied as any communication circuit, device, or collection thereof, capable of enabling communications over a network between the compute circuitry702and another compute device (e.g., an Edge gateway of an implementing Edge computing system). The communication circuitry712may be configured to use any one or more communication technology (e.g., wired or wireless communications) and associated protocols (e.g., a cellular networking protocol such a 3GPP 4G or 5G standard, a wireless local area network protocol such as IEEE 802.11/Wi-Fi®, a wireless wide area network protocol, Ethernet, Bluetooth®, Bluetooth Low Energy, a IoT protocol such as IEEE 802.15.4 or ZigBee®, low-power wide-area network (LPWAN) or low-power wide-area (LPWA) protocols, etc.) to effect such communication.

The illustrative communication circuitry712includes a network interface controller (NIC)720, which may also be referred to as a host fabric interface (HFI). The NIC720may be embodied as one or more add-in-boards, daughter cards, network interface cards, controller chips, chipsets, or other devices that may be used by the compute node700to connect with another compute device (e.g., an Edge gateway node). In some examples, the NIC720may be embodied as part of a system-on-a-chip (SoC) that includes one or more processors, or included on a multichip package that also contains one or more processors. In some examples, the NIC720may include a local processor (not shown) and/or a local memory (not shown) that are both local to the NIC720. In such examples, the local processor of the NIC720may be capable of performing one or more of the functions of the compute circuitry702described herein. Additionally, or alternatively, in such examples, the local memory of the NIC720may be integrated into one or more components of the client compute node at the board level, socket level, chip level, and/or other levels.

Additionally, in some examples, a respective compute node700may include one or more peripheral devices714. Such peripheral devices714may include any type of peripheral device found in a compute device or server such as audio input devices, a display, other input/output devices, interface devices, and/or other peripheral devices, depending on the particular type of the compute node700. In further examples, the compute node700may be embodied by a respective Edge compute node (whether a client, gateway, or aggregation node) in an Edge computing system or like forms of appliances, computers, subsystems, circuitry, or other components.

In a more detailed example,FIG. 7Billustrates a block diagram of an example of components that may be present in an Edge computing node750for implementing the techniques (e.g., operations, processes, methods, and methodologies) described herein. This Edge computing node750provides a closer view of the respective components of node700when implemented as or as part of a computing device (e.g., as a mobile device, a base station, server, gateway, etc.). The Edge computing node750may include any combination of the hardware or logical components referenced herein, and it may include or couple with any device usable with an Edge communication network or a combination of such networks. The components may be implemented as integrated circuits (ICs), portions thereof, discrete electronic devices, or other modules, instruction sets, programmable logic or algorithms, hardware, hardware accelerators, software, firmware, or a combination thereof adapted in the Edge computing node750, or as components otherwise incorporated within a chassis of a larger system.

The Edge computing device750may include processing circuitry in the form of a processor752, which may be a microprocessor, a multi-core processor, a multithreaded processor, an ultra-low voltage processor, an embedded processor, an xPU/DPU/IPU/NPU, special purpose processing unit, specialized processing unit, or other known processing elements. The processor752may be a part of a system on a chip (SoC) in which the processor752and other components are formed into a single integrated circuit, or a single package, such as the Edison™ or Galileo™ SoC boards from Intel Corporation, Santa Clara, Calif. As an example, the processor752may include an Intel® Architecture Core™ based CPU processor, such as a Quark™, an Atom™, an i3, an i5, an i7, an i9, or an MCU-class processor, or another such processor available from Intel®. However, any number other processors may be used, such as available from Advanced Micro Devices, Inc. (AMD®) of Sunnyvale, Calif., a MIPS®-based design from MIPS Technologies, Inc. of Sunnyvale, Calif., an ARM®-based design licensed from ARM Holdings, Ltd. or a customer thereof, or their licensees or adopters. The processors may include units such as an A5-13 processor from Apple® Inc., a Snapdragon™ processor from Qualcomm® Technologies, Inc., or an OMAP™ processor from Texas Instruments, Inc. The processor752and accompanying circuitry may be provided in a single socket form factor, multiple socket form factor, or a variety of other formats, including in limited hardware configurations or configurations that include fewer than all elements shown inFIG. 7B.

The processor752may communicate with a system memory754over an interconnect756(e.g., a bus). Any number of memory devices may be used to provide for a given amount of system memory. As examples, the memory754may be random access memory (RAM) in accordance with a Joint Electron Devices Engineering Council (JEDEC) design such as the DDR or mobile DDR standards (e.g., LPDDR, LPDDR2, LPDDR3, or LPDDR4). In particular examples, a memory component may comply with a DRAM standard promulgated by JEDEC, such as JESD79F for DDR SDRAM, JESD79-2F for DDR2 SDRAM, JESD79-3F for DDR3 SDRAM, JESD79-4A for DDR4 SDRAM, JESD209 for Low Power DDR (LPDDR), JESD209-2 for LPDDR2, JESD209-3 for LPDDR3, and JESD209-4 for LPDDR4. Such standards (and similar standards) may be referred to as DDR-based standards and communication interfaces of the storage devices that implement such standards may be referred to as DDR-based interfaces. In various implementations, the individual memory devices may be of any number of different package types such as single die package (SDP), dual die package (DDP) or quad die package (Q17P). These devices, in some examples, may be directly soldered onto a motherboard to provide a lower profile solution, while in other examples the devices are configured as one or more memory modules that in turn couple to the motherboard by a given connector. Any number of other memory implementations may be used, such as other types of memory modules, e.g., dual inline memory modules (DIMMs) of different varieties including but not limited to microDIMMs or MiniDIMMs.

To provide for persistent storage of information such as data, applications, operating systems and so forth, a storage758may also couple to the processor752via the interconnect756. In an example, the storage758may be implemented via a solid-state disk drive (SSDD). Other devices that may be used for the storage758include flash memory cards, such as Secure Digital (SD) cards, microSD cards, eXtreme Digital (XD) picture cards, and the like, and Universal Serial Bus (USB) flash drives. In an example, the memory device may be or may include memory devices that use chalcogenide glass, multi-threshold level NAND flash memory, NOR flash memory, single or multi-level Phase Change Memory (PCM), a resistive memory, nanowire memory, ferroelectric transistor random access memory (FeTRAM), anti-ferroelectric memory, magnetoresistive random access memory (MRAM) memory that incorporates memristor technology, resistive memory including the metal oxide base, the oxygen vacancy base and the conductive bridge Random Access Memory (CB-RAM), or spin transfer torque (STT)-MRAM, a spintronic magnetic junction memory based device, a magnetic tunneling junction (MTJ) based device, a DW (Domain Wall) and SOT (Spin Orbit Transfer) based device, a thyristor based memory device, or a combination of any of the above, or other memory.

In low power implementations, the storage758may be on-die memory or registers associated with the processor752. However, in some examples, the storage758may be implemented using a micro hard disk drive (HDD). Further, any number of new technologies may be used for the storage758in addition to, or instead of, the technologies described, such resistance change memories, phase change memories, holographic memories, or chemical memories, among others.

The components may communicate over the interconnect756. The interconnect756may include any number of technologies, including industry standard architecture (ISA), extended ISA (EISA), peripheral component interconnect (PCI), peripheral component interconnect extended (PCIx), PCI express (PCIe), or any number of other technologies. The interconnect756may be a proprietary bus, for example, used in an SoC based system. Other bus systems may be included, such as an Inter-Integrated Circuit (I2C) interface, a Serial Peripheral Interface (SPI) interface, point to point interfaces, and a power bus, among others.

The interconnect756may couple the processor752to a transceiver766, for communications with the connected Edge devices762. The transceiver766may use any number of frequencies and protocols, such as 2.4 Gigahertz (GHz) transmissions under the IEEE 802.15.4 standard, using the Bluetooth® low energy (BLE) standard, as defined by the Bluetooth® Special Interest Group, or the ZigBee® standard, among others. Any number of radios, configured for a particular wireless communication protocol, may be used for the connections to the connected Edge devices762. For example, a wireless local area network (WLAN) unit may be used to implement Wi-Fi® communications in accordance with the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard. In addition, wireless wide area communications, e.g., according to a cellular or other wireless wide area protocol, may occur via a wireless wide area network (WWAN) unit.

The wireless network transceiver766(or multiple transceivers) may communicate using multiple standards or radios for communications at a different range. For example, the Edge computing node750may communicate with close devices, e.g., within about 10 meters, using a local transceiver based on Bluetooth Low Energy (BLE), or another low power radio, to save power. More distant connected Edge devices762, e.g., within about 50 meters, may be reached over ZigBee® or other intermediate power radios. Both communications techniques may take place over a single radio at different power levels or may take place over separate transceivers, for example, a local transceiver using BLE and a separate mesh transceiver using ZigBee®.

Any number of other radio communications and protocols may be used in addition to the systems mentioned for the wireless network transceiver766, as described herein. For example, the transceiver766may include a cellular transceiver that uses spread spectrum (SPA/SAS) communications for implementing high-speed communications. Further, any number of other protocols may be used, such as Wi-Fi® networks for medium speed communications and provision of network communications. The transceiver766may include radios that are compatible with any number of 3GPP (Third Generation Partnership Project) specifications, such as Long Term Evolution (LTE) and 5th Generation (5G) communication systems, discussed in further detail at the end of the present disclosure. A network interface controller (NIC)768may be included to provide a wired communication to nodes of the Edge cloud795or to other devices, such as the connected Edge devices762(e.g., operating in a mesh). The wired communication may provide an Ethernet connection or may be based on other types of networks, such as Controller Area Network (CAN), Local Interconnect Network (LIN), DeviceNet, ControlNet, Data Highway+, PROFIBUS, or PROFINET, among many others. An additional NIC768may be included to enable connecting to a second network, for example, a first NIC768providing communications to the cloud over Ethernet, and a second NIC768providing communications to other devices over another type of network.

Given the variety of types of applicable communications from the device to another component or network, applicable communications circuitry used by the device may include or be embodied by any one or more of components764,766,768, or770. Accordingly, in various examples, applicable means for communicating (e.g., receiving, transmitting, etc.) may be embodied by such communications circuitry.

The Edge computing node750may include or be coupled to acceleration circuitry764, which may be embodied by one or more artificial intelligence (AI) accelerators, a neural compute stick, neuromorphic hardware, an FPGA, an arrangement of GPUs, an arrangement of xPUs/DPUs/IPU/NPUs, one or more SoCs, one or more CPUs, one or more digital signal processors, dedicated ASICs, or other forms of specialized processors or circuitry designed to accomplish one or more specialized tasks. These tasks may include AI processing (including machine learning, training, inferencing, and classification operations), visual data processing, network data processing, object detection, rule analysis, or the like. These tasks also may include the specific Edge computing tasks for service management and service operations discussed elsewhere in this document.

The interconnect756may couple the processor752to a sensor hub or external interface770that is used to connect additional devices or subsystems. The devices may include sensors772, such as accelerometers, level sensors, flow sensors, optical light sensors, camera sensors, temperature sensors, global navigation system (e.g., GPS) sensors, pressure sensors, barometric pressure sensors, and the like. The hub or interface770further may be used to connect the Edge computing node750to actuators774, such as power switches, valve actuators, an audible sound generator, a visual warning device, and the like.

In some optional examples, various input/output (I/O) devices may be present within or connected to, the Edge computing node750. For example, a display or other output device784may be included to show information, such as sensor readings or actuator position. An input device786, such as a touch screen or keypad may be included to accept input. An output device784may include any number of forms of audio or visual display, including simple visual outputs such as binary status indicators (e.g., light-emitting diodes (LEDs)) and multi-character visual outputs, or more complex outputs such as display screens (e.g., liquid crystal display (LCD) screens), with the output of characters, graphics, multimedia objects, and the like being generated or produced from the operation of the Edge computing node750. A display or console hardware, in the context of the present system, may be used to provide output and receive input of an Edge computing system; to manage components or services of an Edge computing system; identify a state of an Edge computing component or service; or to conduct any other number of management or administration functions or service use cases.

A battery776may power the Edge computing node750, although, in examples in which the Edge computing node750is mounted in a fixed location, it may have a power supply coupled to an electrical grid, or the battery may be used as a backup or for temporary capabilities. The battery776may be a lithium ion battery, or a metal-air battery, such as a zinc-air battery, an aluminum-air battery, a lithium-air battery, and the like.

A battery monitor/charger778may be included in the Edge computing node750to track the state of charge (SoCh) of the battery776, if included. The battery monitor/charger778may be used to monitor other parameters of the battery776to provide failure predictions, such as the state of health (SoH) and the state of function (SoF) of the battery776. The battery monitor/charger778may include a battery monitoring integrated circuit, such as an LTC4020 or an LT5990 from Linear Technologies, an ADT7488A from ON Semiconductor of Phoenix Ariz., or an IC from the UCD90xxx family from Texas Instruments of Dallas, Tex. The battery monitor/charger778may communicate the information on the battery776to the processor752over the interconnect756. The battery monitor/charger778may also include an analog-to-digital (ADC) converter that enables the processor752to directly monitor the voltage of the battery776or the current flow from the battery776. The battery parameters may be used to determine actions that the Edge computing node750may perform, such as transmission frequency, mesh network operation, sensing frequency, and the like.

A power block780, or other power supply coupled to a grid, may be coupled with the battery monitor/charger778to charge the battery776. In some examples, the power block780may be replaced with a wireless power receiver to obtain the power wirelessly, for example, through a loop antenna in the Edge computing node750. A wireless battery charging circuit, such as an LTC4020 chip from Linear Technologies of Milpitas, Calif., among others, may be included in the battery monitor/charger778. The specific charging circuits may be selected based on the size of the battery776, and thus, the current required. The charging may be performed using the Airfuel standard promulgated by the Airfuel Alliance, the Qi wireless charging standard promulgated by the Wireless Power Consortium, or the Rezence charging standard, promulgated by the Alliance for Wireless Power, among others.

The storage758may include instructions782in the form of software, firmware, or hardware commands to implement the techniques described herein. Although such instructions782are shown as code blocks included in the memory754and the storage758, it may be understood that any of the code blocks may be replaced with hardwired circuits, for example, built into an application specific integrated circuit (ASIC).

In an example, the instructions782provided via the memory754, the storage758, or the processor752may be embodied as a non-transitory, machine-readable medium760including code to direct the processor752to perform electronic operations in the Edge computing node750. The processor752may access the non-transitory, machine-readable medium760over the interconnect756. For instance, the non-transitory, machine-readable medium760may be embodied by devices described for the storage758or may include specific storage units such as storage devices and/or storage disks that include optical disks (e.g., digital versatile disk (DVD), compact disk (CD), CD-ROM, Blu-ray disk), flash drives, floppy disks, hard drives (e.g., SSDs), or any number of other hardware devices in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or caching). The non-transitory, machine-readable medium760may include instructions to direct the processor752to perform a specific sequence or flow of actions, for example, as described with respect to the flowchart(s) and block diagram(s) of operations and functionality depicted above. As used herein, the terms “machine-readable medium” and “computer-readable medium” are interchangeable. As used herein, the term “non-transitory computer-readable medium” is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media.

Also in a specific example, the instructions782on the processor752(separately, or in combination with the instructions782of the machine readable medium760) may configure execution or operation of a trusted execution environment (TEE)790. In an example, the TEE790operates as a protected area accessible to the processor752for secure execution of instructions and secure access to data. Various implementations of the TEE790, and an accompanying secure area in the processor752or the memory754may be provided, for instance, through use of Intel® Software Guard Extensions (SGX) or ARM® TrustZone® hardware security extensions, Intel® Management Engine (ME), or Intel® Converged Security Manageability Engine (CSME). Other aspects of security hardening, hardware roots-of-trust, and trusted or protected operations may be implemented in the device750through the TEE790and the processor752.

While the illustrated examples ofFIG. 7AandFIG. 7Binclude example components for a compute node and a computing device, respectively, examples disclosed herein are not limited thereto. As used herein, a “computer” may include some or all of the example components ofFIGS. 7A and/or 7Bin different types of computing environments. Example computing environments include Edge computing devices (e.g., Edge computers) in a distributed networking arrangement such that particular ones of participating Edge computing devices are heterogenous or homogeneous devices. As used herein, a “computer” may include a personal computer, a server, user equipment, an accelerator, etc., including any combinations thereof. In some examples, distributed networking and/or distributed computing includes any number of such Edge computing devices as illustrated inFIGS. 7A and/or 7B, each of which may include different sub-components, different memory capacities, I/O capabilities, etc. For example, because some implementations of distributed networking and/or distributed computing are associated with particular desired functionality, examples disclosed herein include different combinations of components illustrated inFIGS. 7A and/or 7Bto satisfy functional objectives of distributed computing tasks. In some examples, the term “compute node” or “computer” only includes the example processor704, memory706and I/O subsystem708ofFIG. 7A. In some examples, one or more objective functions of a distributed computing task(s) rely on one or more alternate devices/structure located in different parts of an Edge networking environment, such as devices to accommodate data storage (e.g., the example data storage710), input/output capabilities (e.g., the example peripheral device(s)714), and/or network communication capabilities (e.g., the example NIC720).

In some examples, computers operating in a distributed computing and/or distributed networking environment (e.g., an Edge network) are structured to accommodate particular objective functionality in a manner that reduces computational waste. For instance, because a computer includes a subset of the components disclosed inFIGS. 7A and 7B, such computers satisfy execution of distributed computing objective functions without including computing structure that would otherwise be unused and/or underutilized. As such, the term “computer” as used herein includes any combination of structure ofFIGS. 7A and/or 7Bthat is capable of satisfying and/or otherwise executing objective functions of distributed computing tasks. In some examples, computers are structured in a manner commensurate to corresponding distributed computing objective functions in a manner that downscales or upscales in connection with dynamic demand. In some examples, different computers are invoked and/or otherwise instantiated in view of their ability to process one or more tasks of the distributed computing request(s), such that any computer capable of satisfying the tasks proceed with such computing activity.

In the illustrated examples ofFIGS. 7A and 7B, computing devices include operating systems. As used herein, an “operating system” is software to control example computing devices, such as the example Edge compute node700ofFIG. 7Aand/or the example Edge compute node750ofFIG. 7B. Example operating systems include, but are not limited to consumer-based operating systems (e.g., Microsoft® Windows® 10, Google® Android® OS, Apple® Mac® OS, etc.). Example operating systems also include, but are not limited to industry-focused operating systems, such as real-time operating systems, hypervisors, etc. An example operating system on a first Edge compute node may be the same or different than an example operating system on a second Edge compute node. In some examples, the operating system invokes alternate software to facilitate one or more functions and/or operations that are not native to the operating system, such as particular communication protocols and/or interpreters. In some examples, the operating system instantiates various functionalities that are not native to the operating system. In some examples, operating systems include varying degrees of complexity and/or capabilities. For instance, a first operating system corresponding to a first Edge compute node includes a real-time operating system having particular performance expectations of responsivity to dynamic input conditions, and a second operating system corresponding to a second Edge compute node includes graphical user interface capabilities to facilitate end-user I/O.

FIG. 8is a block diagram illustrating an example architecture implemented in accordance with the teachings of this disclosure. In particular,FIG. 8shows a high level architecture800of the framework disclosed herein, upon which the digital twin based predictions can be used to improve the robustness of radio networks. The illustrated example ofFIG. 8shows a 5G core810in communication with a MEC system820. Here, the integration of MEC system820with 5G core810is shown as an example and considered for explanation of ideas throughout this disclosure. However, the techniques of this disclosure can be applied to other networks like LTE, beyond 5G, etc. In such examples, the interface(s) with the MEC system820might be different. In the illustrated example ofFIG. 8, the MEC system820includes a MEC orchestrator825, digital twin circuitry830, a radio network information (RNI) service835, a location service840, an environment perception service845, a radio network recommendations service850, a map service855, a forecast service, one or more road side units (RSUs)865, and one or more sensors870. In the illustrated example ofFIG. 8, the one or more sensors870includes one or more cameras, lidars, etc. The 5G core810is in communication with a g node b (gNB)890, and a UE895.

FIG. 9is a block diagram illustrating relationships between functional entities within the MEC system820ofFIG. 8, and a dataflow between those entities. As shown in the illustrated example ofFIG. 9, the digital twin circuitry receives information from one or more of the forecast services860, the environmental perception service845, the RNI service835, and the RSUs865. The example digital twin circuitry830provides recommendation messages to one or more of the radio network recommendations service850, the RSUs865, and/or the MEC orchestrator825.

The environmental perception service (EPS)845receives and/or collects information from one or more of the map service855, the sensors870, the location service840, and/or the RSUs865. The EPS845collects live information about the environment from different sources such as connected sensors870(wired or wireless), RSUs865, and Location service840. The EPS845also obtains static information of the environment such as buildings, road infrastructure, etc., via high definition (HD) maps provided by map service855. The EPS845processes the received and/or collected data using, for example, sensor fusion methods, to develop a contextual understanding of the environment. Recent advancements in the field of autonomous systems have allowed for real-time environmental perception capabilities with accurate semantic and kinematic details. The EPS845provides the semantic and kinematic information to the DT circuitry830. While in the illustrated example ofFIG. 9, the EPS845processes such received and/or collected information for generation of the semantic and kinematic information, in some examples, the DT circuitry830may itself process the received and/or collected information.

For the EPS845to map the perceived objects (vehicles, pedestrians, etc.) to the UE IDs (network configured IDs) of wireless networks, the EPS845matches the locations reported by the UEs (via network) to the estimated locations of the perceived objects. The EPS module can obtain location related information about the UEs and other network nodes from Location service840. The location service840, in turn, retrieves the location information from the 5G system which supports both 3GPP and non-3GPP technologies to achieve higher positioning accuracies. In some examples, the RSUs865also provide location information to the EPS which are reported by the UEs through periodic broadcast messages such as basic safety message (BSM), collective perception message (CPM), etc.

As noted above, a digital twin is a real-time virtual representation of a physical entity such as an object, a system, or a process. Using connected sensors, this cyber-physical technology permits connectivity and synchronization between the physical components and their digital counterparts. Further, through analytics and simulations using a digital model (e.g., implemented using the DT circuitry830), a digital twin system can produce future predictions with rich insights about the physical entity.

The DT circuitry830shown inFIG. 8creates a virtual environment of a physical scenario in which the physical entities in the real scenario (e.g., vehicles, pedestrians, buildings, road infrastructure, etc.) are represented as digital actors (e.g., models) in the virtual environment. The DT circuitry830obtains live information of the semantic and kinematic parameters of the physical entities in the environment from Environmental Perception Service845. The semantic parameters provide information about the type of an entity such as person, car, bicyclist, building, road, etc., while the kinematic parameters provide information about the mobility of an entity that include position, velocity, heading direction, etc. The DT circuitry830can obtain wireless access network related information via RNI service835that provides details such as radio network conditions and measurements, information about connected UEs, radio access bearers, etc. Additionally, the DT circuitry830can also obtain wireless network related information pertaining to the RSUs865that are connected to the MEC system820directly. The DT circuitry830can also get information about the local environmental conditions (such as rain, fog, visibility conditions, etc.) through the external forecast services860.

The DT module continuously synchronizes the digital models (actors) in a virtual environment with their respective physical entities through the live information obtained from sources including, for example, EPS845, RNI835, RSUs865, etc. Then, the DT circuitry830performs analytics and simulations using the digital models to generate future predictions (e.g., in real-time) of the parameters of interest such as future positions of actors, wireless channel state, blocking of LoS links, etc. The scope of the simulations covers the parameters of interest like locations and velocities of users, channel conditions (received signal strength, SINR, etc.) at the UEs, etc. The simulations in the DT circuitry830may be based on deterministic and/or AI based algorithms to generate the future predictions. The live measured parameters, such as UE locations, SINR, etc., received by the DT circuitry830can be used as ground truth data to continuously train the AI models and improve the prediction accuracies.

Based on the insights obtained from future predictions, the DT circuitry830generates recommendation messages proactively which can be used to improve resiliency of the network and services, and robustness of the wireless links. The recommendation messages generated by the DT circuitry830can include suggestions related to UE handover (HO), MEC applications mobility, communications and compute resource allocations, beam management, network routing paths, etc. The DT circuitry830can send the recommendation messages to, for example, The 5G network via the proposed radio network recommendations (RNR) service, the connected RSUs' management planes via the MEC host's local network, and/or the MEC orchestrator.

The example radio network recommendations (RNR) service850enables recommendations to be provided to the core network and gNBs. In some examples, the recommendations may be in the form of configurations and/or other parameters. In this manner, the DT circuitry830uses the RNR service850to convey recommendation messages to the 5G network810ofFIG. 8. The RNR service accesses the services provided by relevant 5G core network functions via the MEC orchestrator825to convey the recommendation of the DT circuitry830.

FIG. 10is a block diagram of an example implementation of the example digital twin circuitry830ofFIGS. 8 and/or 9. The example digital twin circuitry830includes information accessor circuitry1010, virtual environment management circuitry1020, a virtual environment memory1030, simulation circuitry1040, recommendation generator circuitry1050, recommendation manager circuitry1060, and recommendation provider circuitry1070.

The example information accessor circuitry1010of the illustrated example ofFIG. 10accesses semantic and kinematic statistic information from the EPS845. The example information accessor circuitry1010accesses network information and measurement reports from the RNI service835, and/or the RSUs865. In some examples, the information accessor circuitry1010also accesses information from the forecast services860.

In some examples, the digital twin circuitry830includes means for accessing. For example, the means for accessing may be implemented by the information accessor circuitry1010. In some examples, the information accessor circuitry1010may be implemented by machine executable instructions such as that implemented by at least blocks1110, and1120ofFIG. 11executed by processor circuitry, which may be implemented by the example processor circuitry1812ofFIG. 18, the example processor circuitry1900ofFIG. 19, and/or the example Field Programmable Gate Array (FPGA) circuitry2000ofFIG. 20. In other examples, the information accessor circuitry1010is implemented by other hardware logic circuitry, hardware implemented state machines, and/or any other combination of hardware, software, and/or firmware. For example, the information accessor circuitry1010may be implemented by at least one or more hardware circuits (e.g., processor circuitry, discrete and/or integrated analog and/or digital circuitry, an FPGA, an Application Specific Integrated Circuit (ASIC), a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware, but other structures are likewise appropriate.

The example virtual environment management circuitry1020of the illustrated example ofFIG. 10updates a virtual environment stored in the virtual environment memory1030based on the accessed information. In this manner, the virtual environment mirrors (e.g., is a digital twin of) the physical environment.

In some examples, the digital twin circuitry830includes means for updating. For example, the means for updating may be implemented by the virtual environment management circuitry1020. In some examples, the virtual environment management circuitry1020may be implemented by machine executable instructions such as that implemented by at least block1130ofFIG. 11executed by processor circuitry, which may be implemented by the example processor circuitry1812ofFIG. 18, the example processor circuitry1900ofFIG. 19, and/or the example Field Programmable Gate Array (FPGA) circuitry2000ofFIG. 20. In other examples, the virtual environment management circuitry1020is implemented by other hardware logic circuitry, hardware implemented state machines, and/or any other combination of hardware, software, and/or firmware. For example, the virtual environment management circuitry1020may be implemented by at least one or more hardware circuits (e.g., processor circuitry, discrete and/or integrated analog and/or digital circuitry, an FPGA, an Application Specific Integrated Circuit (ASIC), a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware, but other structures are likewise appropriate.

The example virtual environment memory1030of the illustrated example ofFIG. 10is implemented by any memory, storage device and/or storage disc for storing data such as, for example, flash memory, magnetic media, optical media, solid state memory, hard drive(s), thumb drive(s), etc. Furthermore, the data stored in the example virtual environment memory1030may be in any data format such as, for example, binary data, comma delimited data, tab delimited data, structured query language (SQL) structures, etc. While, in the illustrated example, the virtual environment memory1030is illustrated as a single device, the example virtual environment memory1030and/or any other data storage devices described herein may be implemented by any number and/or type(s) of memories. In the illustrated example ofFIG. 10, the example virtual environment memory1030stores a virtual representation of state of entities in the physical environment including both entities (e.g., devices) in communication with the network as well as entities (e.g., objects) not in communication with the network.

The example simulation circuitry1040of the illustrated example ofFIG. 10simulates changes to the environment represented by the virtual environment memory1030. Such changes may be based on, for example, the semantic and kinematic statistics and/or the network information and measurement reports accessed by the information accessor circuitry1010. In this manner, the simulated changes represent possible changes to the virtual environment and, as a result, possible changes to the physical environment. In response to such potential changes, various tasks may be beneficial to the reliability and/or resiliency of the network including, for example, performing a handover operation and/or initializing resources in anticipation of a handover operation.

In some examples, the digital twin circuitry830includes means for simulating. For example, the means for simulating may be implemented by the simulation circuitry1040. In some examples, the virtual simulation circuitry1040may be implemented by machine executable instructions such as that implemented by at least block1140ofFIG. 11executed by processor circuitry, which may be implemented by the example processor circuitry1812ofFIG. 18, the example processor circuitry1900ofFIG. 19, and/or the example Field Programmable Gate Array (FPGA) circuitry2000ofFIG. 20. In other examples, the simulation circuitry1040is implemented by other hardware logic circuitry, hardware implemented state machines, and/or any other combination of hardware, software, and/or firmware. For example, the simulation circuitry1040may be implemented by at least one or more hardware circuits (e.g., processor circuitry, discrete and/or integrated analog and/or digital circuitry, an FPGA, an Application Specific Integrated Circuit (ASIC), a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware, but other structures are likewise appropriate.

The example recommendation generator circuitry1050of the illustrated example ofFIG. 10generates one or more recommendations. Such recommendations may include, for example, performance of a handover operation and/or initializing resources in anticipation of a handover operation.

In some examples, the digital twin circuitry830includes means for generating. For example, the means for generating may be implemented by the recommendation generator circuitry1050. In some examples, the recommendation generator circuitry1050may be implemented by machine executable instructions such as that implemented by at least block1150ofFIG. 11executed by processor circuitry, which may be implemented by the example processor circuitry1812ofFIG. 18, the example processor circuitry1900ofFIG. 19, and/or the example Field Programmable Gate Array (FPGA) circuitry2000ofFIG. 20. In other examples, the recommendation generator circuitry1050is implemented by other hardware logic circuitry, hardware implemented state machines, and/or any other combination of hardware, software, and/or firmware. For example, the recommendation generator circuitry1050may be implemented by at least one or more hardware circuits (e.g., processor circuitry, discrete and/or integrated analog and/or digital circuitry, an FPGA, an Application Specific Integrated Circuit (ASIC), a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware, but other structures are likewise appropriate.

The example recommendation manager circuitry1060of the illustrated example ofFIG. 10determines whether any recommendations meet a threshold confidence level. In some examples, multiple different recommendations may meet the threshold confidence level. In such an example, the highest confidence non-conflicting recommendations are provided to the appropriate entities. Two recommendations may be conflicting when those recommendations would cause actions that would be in conflict with another.

In some examples, the digital twin circuitry830includes means for managing. For example, the means for managing may be implemented by the recommendation manager circuitry1060. In some examples, the recommendation manager circuitry1060may be implemented by machine executable instructions such as that implemented by at least block1160,1165ofFIG. 11executed by processor circuitry, which may be implemented by the example processor circuitry1812ofFIG. 18, the example processor circuitry1900ofFIG. 19, and/or the example Field Programmable Gate Array (FPGA) circuitry2000ofFIG. 20. In other examples, the recommendation manager circuitry1060is implemented by other hardware logic circuitry, hardware implemented state machines, and/or any other combination of hardware, software, and/or firmware. For example, the recommendation manager circuitry1060may be implemented by at least one or more hardware circuits (e.g., processor circuitry, discrete and/or integrated analog and/or digital circuitry, an FPGA, an Application Specific Integrated Circuit (ASIC), a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware, but other structures are likewise appropriate.

The example recommendation provider circuitry1070of the illustrated example ofFIG. 10provides recommendation information to various other network equipment. In some examples, the recommendation provider circuitry1070communicates with the radio network recommendations service850to provide radio network recommendations to elements within the 5G core810(e.g., to directly recommend to a gNB to perform a task). In some examples, the recommendation provider circuitry1070communicates with one or more RSUs to facilitate recommendations related to beam forming. In some examples, the recommendation provider circuitry1070communicates with the MEC orchestrator825to facilitate mobility management.

In some examples, the digital twin circuitry830includes means for providing. For example, the means for providing may be implemented by the recommendation provider circuitry1070. In some examples, the recommendation provider circuitry1070may be implemented by machine executable instructions such as that implemented by at least block1170ofFIG. 11executed by processor circuitry, which may be implemented by the example processor circuitry1812ofFIG. 18, the example processor circuitry1900ofFIG. 19, and/or the example Field Programmable Gate Array (FPGA) circuitry2000ofFIG. 20. In other examples, the recommendation provider circuitry1070is implemented by other hardware logic circuitry, hardware implemented state machines, and/or any other combination of hardware, software, and/or firmware. For example, the recommendation provider circuitry1070may be implemented by at least one or more hardware circuits (e.g., processor circuitry, discrete and/or integrated analog and/or digital circuitry, an FPGA, an Application Specific Integrated Circuit (ASIC), a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware, but other structures are likewise appropriate.

While an example manner of implementing the digital twin circuitry830ofFIG. 8is illustrated inFIG. 10, one or more of the elements, processes, and/or devices illustrated inFIG. 10may be combined, divided, re-arranged, omitted, eliminated, and/or implemented in any other way. Further, the example information accessor circuitry1010, the example virtual environment management circuitry1020, the example simulation circuitry1040, the example recommendation generator circuitry1050, the example recommendation manager circuitry1060, the example recommendation provider circuitry1070, and/or, more generally, the example digital twin circuitry830ofFIG. 8, may be implemented by hardware, software, firmware, and/or any combination of hardware, software, and/or firmware. Thus, for example, any of the example information accessor circuitry1010, the example virtual environment management circuitry1020, the example simulation circuitry1040, the example recommendation generator circuitry1050, the example recommendation manager circuitry1060, the example recommendation provider circuitry1070, and/or, more generally, the example digital twin circuitry830ofFIG. 8, could be implemented by processor circuitry, analog circuit(s), digital circuit(s), logic circuit(s), programmable processor(s), programmable microcontroller(s), graphics processing unit(s) (GPU(s)), digital signal processor(s) (DSP(s)), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)), and/or field programmable logic device(s) (FPLD(s)) such as Field Programmable Gate Arrays (FPGAs). When reading any of the apparatus or system claims of this patent to cover a purely software and/or firmware implementation, at least one of the example information accessor circuitry1010, the example virtual environment management circuitry1020, the example simulation circuitry1040, the example recommendation generator circuitry1050, the example recommendation manager circuitry1060, the example recommendation provider circuitry1070, and/or, more generally, the example digital twin circuitry830ofFIG. 8is/are hereby expressly defined to include a non-transitory computer readable storage device or storage disk such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc., including the software and/or firmware. Further still, the example digital twin circuitry830ofFIG. 8may include one or more elements, processes, and/or devices in addition to, or instead of, those illustrated inFIG. 10, and/or may include more than one of any or all of the illustrated elements, processes and devices.

FIG. 11is a flowchart representative of example machine readable instructions and/or example operations1100that may be executed and/or instantiated by processor circuitry to generate a recommendation. The machine readable instructions and/or operations1100ofFIG. 11begin at block1110, at which the information accessor circuitry1010accesses semantic and kinematic statistic information from the EPS845. (Block1110). The example information accessor circuitry1010accesses network information and measurement reports from the RNI service835, and/or the RSUs865. (Block1120). In some examples, the information accessor circuitry1010also accesses current and future local environment conditions from the forecast services860. (Block1125). In some examples, the current and future local environment conditions may represent current and/or predicted weather conditions including, for example, temperature, rain, snow, fog, etc. In examples disclosed herein, the semantic and kinematic statistic information and the network information and measurement reports may be generically referred to as operational statistics.

The virtual environment management circuitry1020updates the virtual environment memory1030based on the accessed information. (Block1130). In this manner, the virtual environment memory1030represents a virtual environment that mirrors (e.g., is a digital twin of) the physical environment. The example simulation circuitry1040simulates changes to the environment represented by the virtual environment memory1030. (Block1140). Such changes may be based on, for example, the semantic and kinematic statistics and/or the network information and measurement reports received at blocks1110and1120, respectively. In this manner, the simulated changes represent possible changes to the virtual environment and, as a result, possible changes to the physical environment. If, for example, a UE was moving at a rate of speed that would soon cause the UE to transition from having LoS of a gNB to not having LoS of the gNB, simulation of such change can be used to begin a handover from the gNB to another gNB prior to the UE not having LoS with the gNB.

Based on the simulated changes, the recommendation generator1050generates one or more recommendations. (Block1150). In some examples, multiple different changes may be simulated, and those different changes may have varying degrees of confidence that those changes are likely to occur. In some examples, multiple different situational changes may result in a same recommendation (e.g., perform a handoff from a first gNB to a second gNB). For example, considering the above LoS example, there may be a 50% likelihood that the UE continues on its current path of travel at its current rate of speed, a 25% likelihood that the UE stops moving, and a 25% likelihood that the UE continues on its current path of travel at an increased rate of speed. In both of the situations where the UE continues on its current path (e.g., totaling to a 75% likelihood), a handover may be recommended, whereas in the situation where the UE stops moving, no handover is recommended. The recommendation manager circuitry1060evaluates the confidence(s) of varying situational changes, and aggregates outcomes to associate a confidence for each recommendation. (Block1160). Continuing the above example, there may therefore be a 75% confidence that a handover should be initiated.

The example recommendation manager circuitry1060determines whether any recommendations meet a threshold confidence level. (Block1165). If the threshold confidence level is met (e.g., block1165returns a result of YES), the recommendation (e.g., the recommendation having the threshold confidence) is provided to the relevant recipient by the recommendation provider circuitry1070. (Block1170). In some examples, multiple different recommendations may meet the threshold confidence level. In such an example, the highest confidence non-conflicting recommendations are provided to the appropriate entities. Two recommendations may be conflicting when those recommendations would cause actions that would be in conflict with another. For example, if there were a first recommendation to handover a UE from a source node to a first target node, and a second recommendation to handover the UE from the source node to a second target node, the recommendation having the higher confidence would be selected, as the UE cannot handover to both the first and second node.

After providing the recommendation(s) to the appropriate entity(ies), the example process1100ofFIG. 11terminates. The example process ofFIG. 11may be repeated, however, on a periodic basis (e.g., every one hundred milliseconds, every second, every minute, etc.) and/or on an a-periodic basis (e.g., in response to receipt of updated semantic and/or kinematic information, in response to receipt of updated network information and/or measurement reports).

FIG. 12is a flowchart representative of example machine readable instructions and/or example operations1100that may be executed and/or instantiated by processor circuitry to perform a handoff in response to a recommendation. In existing 5G systems, the HO decisions are taken solely based on measurements reported by the UEs. In the baseline HO process, a UE in connected mode is configured by gNB to report certain measurement events, based on which the gNB makes HO decisions. When the gNB decides to HO a UE to a target gNB, the UE initiates HO preparation phase with the target gNB, and after that the gNB sends a command to the UE to execute the HO process. However, depending on the environment and the UE's velocity, there are certain chances for HoF and ping-pong effects. For example, when a high-speed UE's link abruptly changes from LoS to NLoS due to blocking by a building. There are certain HO related parameters like RSRP/RSRQ/SINR thresholds, hysteresis, offset, etc., which can be adjusted appropriately to reduce HoF. However, due to uncertainties in the environment and dynamics of a UE's behavior, the UEs may experience significant radio link failures (RLFs) and HoFs as a result of too-early or too-late HOs.

The machine readable instructions and/or operations1200ofFIG. 12begin at block1210, at which the gNB890receives a handoff recommendation from an entity other than the UE895. (Block1210). That is, as opposed to existing approaches, where the UI initiates the HO preparation, the recommendation to initiate the HO is provided by another entity such as, for example the digital twin circuitry830. The example gNB determines whether the HO resources are available (Block1220) and, if so, initiates a handoff procedure for the UE. (Block1230). If the HO resources are not available (e.g., block1220returns a result of NO), no action is taken.

FIG. 13is a communication diagram1300illustrating proactive mobility management to reduce handover failures. In the illustrate example ofFIG. 13, the DT circuitry830collects semantic and kinematic information, and network information and measurement reports. Using this information, the DT circuitry830simulates a virtual environment and generates a recommendation message. In the illustrated example ofFIG. 13, the recommendation message is a handover recommendation message.

In examples disclosed herein, the HO recommendation message includes a list of HO recommendation structures, each corresponding to a UE that may require HO in near future. Each recommendation structure includes, for example, a recommendation ID, a network ID for the UE, a recommendation type (e.g., new, update, revoke, etc.), a timestamp indicating a recommended future time at which the source gNB may start HO preparation, a timestamp indicating a future time before which the HO execution should be completed to avoid HOF or RLF, a recommended HO type (e.g., HO, CHO, dual connectivity, etc.), one (in case of HO/CHO/DC) or more (in case of CHO) recommendations. The recommendation includes, for example, a recommended target cell/gNB ID for HO and a confidence value of the recommendation.

Since the HO recommendations are for future times, the predictions at the DT circuitry830might change based on updated information from sensors and measurement reports. In such cases, the DT can send changes to the previously sent recommendations by setting the recommendation type field to “update” and recommendation ID to the previous ID for which the update is being sent. The DT circuitry830may also cancel/revoke a previously sent recommendation by setting the recommendation type field to “revoke”.

A gNB (e.g., gNB1320) typically uses certain criteria on the measurements reported by a UE1310to make HO decisions. In examples disclosed herein, the gNB1320additionally utilizes recommendations from the DT circuitry830as an additional criteria in the HO decision process. For example, the gNB1320can start HO preparation with the target gNB1330at the time as per the recommendation message from the DT circuitry1320. Then, the source gNB1320may execute the HO procedure by considering measurement reports from the UE1310and the recommendation confidence from the DT circuitry830.

FIG. 14is a communication diagram illustrating proactive mobility management using a conditional handover recommendation. The 3GPP has specified an enhanced mobility management procedure called conditional handover (CHO) in which a HO is executed by a UE1410when one or more HO execution conditions are met. In this procedure, a source gNB1420sends CHO configuration to the UE1410which contains one or more CHO target cells and execution conditions. Then, the UE1410continuously evaluates CHO conditions and when a condition is met, the UE1410executes HO (without HO command from gNB1420) to the corresponding target cell1430,1435. The CHO has shown to reduce HOFs and RLFs, however, the main drawback of CHO procedure is that the source gNB1420needs to prepare one or more target cells1430,1435for HO of a UE1410and reserve resources (radio resources, UE identifiers, RACH resources, etc.) in those cell(s), resulting in inefficient usage of resources. Moreover, it is possible that the UE1410may not perform HO to one of these target cells. Hence, the gNB1420needs to choose the CHO target cells carefully to minimize redundancy and improve resource usage efficiency.

In examples disclosed herein, a DT-based proactive HO procedure is disclosed in which the DT circuitry830aides the gNBs1420,1430,1435by providing HO related suggestions based on the predictions generated through simulations. In examples disclosed herein, the DT circuitry830continuously synchronizes the digital models of UEs with the semantic and kinematic information from EPS, and the measurement reports from RNI service. The DT circuitry830performs simulations and predicts if any UEs might require HOs in the near future. Based on the predictions of the UEs, the DT circuitry830sends HO recommendation messages to the respective gNBs1420,1430,1435that contain the details for gNBs to take proactive HO decisions to avoid HOFs and RLFs.

FIG. 14shows the CHO procedure in 5G along with the proposed signaling from the DT circuitry830to the gNB1420. As mentioned earlier, during CHO decision the gNB1420chooses the CHO target cells carefully to minimize redundancy and improve resource usage efficiency. In examples disclosed herein, the DT recommendation message contains a list of recommended target cells/gNBs which can be used by the source gNB for CHO.

In examples disclosed herein, the DT based recommendation can additionally be used to avoid ping-pongs. If the DT circuitry830can simulate to sufficient future time, such simulations can be used to predict if there will be ping-pong HOs for a UE (UE HO to a target cell, and after a brief time HO back to the previous serving cell). In such a case, the DT circuitry830may recommend dual connectivity based HO for the UE. The serving gNB1420can then command the UE to initiate additional connection with target cell, without dropping the connection with serving cell. Note that the UE1410must support such dual connectivity in order for this approach to work. The serving gNB1410can then drop one connection, either with serving cell or with target cell, based on certain criteria. For example, based on a pre-defined duration and/or the trends of signal strengths from serving and target cells over time. The advantage of this approach is that when a UE adds a new connection with the target cell, and after a brief time drops the connection with the target cell (ping-pong), then there will not be overhead signaling necessary to switch back to the serving cell. This is unlike the typical HO procedure which requires additional signaling overheads when a UE switches back to the serving cell from the target cell during ping-pongs.

As noted above, modeling and/or simulation performed by the digital twin circuitry830may be used to generate recommendations for other types of systems and/or purposes.FIG. 15is a communication diagram illustrating use of the digital twin circuitry830for intelligent beam management. The above-6 GHz frequency bands supported in 5G-NR are sensitive to physical blockage of radio links (when an object blocks LoS path) due to the use of highly directional transmission beams. The blockage can be due to the moving objects in communication environment (dynamic blockage), or due to the static objects intercepting the beams because of UE's mobility (geometry-induced blockage). For a UE1510in connected mode, the blockage may result in beam failure and cause an abrupt interruption in communication. 3GPP has defined procedure for the detection of beam failure at the UE1510, and beam failure recovery (BFR) procedure through which the UE1510attempts to reestablish connection to the same cell via an alternative beam. This process can take several 10's of ms, and in some scenarios the alternative beams may also be affected resulting in further delays in beam failure recovery process due to multiple attempts, and the success is not guaranteed. Upon failure of the recovery process, the UE1510will be forced to initiate RLF procedure and cell reselection, which will induce significant duration of interruption in communication.

In examples disclosed herein, the DT circuitry830can be used to enable a robust and intelligent beam management procedure, through which the gNB1520can proactively reconfigure a UE to minimize BFR duration; instruct a UE to switch to an alternative beam before a blockage occurs, thereby beam failures can be avoided proactively; and/or instruct a UE to handover to a different TRP (transmission/reception point) of same cell, or handover to a different cell, before a blockage occurs to avoid potential beam failures.

The details of DT-based proactive reconfiguration of a UE to minimize BFR duration are illustrated inFIG. 15. As the DT module continuously monitors the kinematic parameters of the UEs and other objects in the communication environment, it can predict the future movements of mobile objects and UEs in the scenario, and then determine if any of the beams would potentially get blocked by the objects in near future. Based on the simulated predictions, the DT sends beam management message to the gNB which contains information about the potential blockage and the candidate beams for the BFR.

Using the information sent by the DT circuitry830, the gNB1520has different options to mitigate the potential beam failure, as depicted inFIG. 15. In a first option [Option-1], the gNB1520sends a RRC message to the UE1510with a BeamFailureRecoveryConfig information element which includes a list of candidate beams for the recovery. In a second option [Option-2], the gNB1520sends a command to the UE1510(e.g., via a MAC CE, for example) to switch to an alternative candidate beam before the current beam failure occurs. In a third option [Option-3], (e.g., if there are no suitable candidate beams expected), the gNB1520initiates a handover of the UE1510to a different TRP or to a different cell before the potential beam failure.

In examples disclosed herein, the beam management message from the DT circuitry830to the gNB1520can include the following information including a list of beam management structures, each corresponding to a UE that require BFR reconfiguration in near future, and a beam recommendation structure. The beam recommendation structure can contain, for example, a Beam management message ID, a network ID of a UE, a message type (e.g., new, update, or revoke), a timestamp indicating a recommended future time at which the gNB may start HO BFR configuration, a timestamp indicating a future time before which the BFR should be completed to minimize the BFR duration, one or more candidate beams information for BFR, each including one or more of a Candidate cell and TRP IDs (in case handover is required), beam identification information, a confidence of the recommendation, etc.

FIG. 16is an example communication diagram1600illustrating the use of the digital twin circuitry830for beam management. Unlike the case of a gNB where the beam management messages pass through the 5G core functions (e.g., as shown inFIGS. 13, 14, and 15), in the illustrated example ofFIG. 16, the beam management messages can be sent to the RSUs directly, using management plane messages. Also, the DT circuitry830can obtain the information of the UEs and measurement reports from RSUs via the management plane messages. Using the information sent by the DT circuitry830, the RSU865has different options to mitigate the potential beam failure, as depicted inFIG. 16. In a first option [Option-1], the RSU865sends a message to the UE1610to initiate a beam failure recovery process. In a second option [Option-2], the RSU865sends a command to the UE1610to switch to an alternative candidate beam before the current beam failure occurs. In a third option [Option-3], (e.g., if there are no suitable candidate beams expected), the RSU865initiates a handover of the UE1510to a different RSU before the potential beam failure.

Application mobility is a unique feature of the MEC system820in which the application instance that is serving a mobile user may be relocated to different MEC hosts dynamically to keep it near the user. Such application mobility ensures that the application requirements are met in a mobile environment. Relocating an application from a source MEC host to a target MEC host typically involves creation of a new application instance at the target MEC host. If the application is a stateful application, then the newly created application instance needs to be synchronized with the original application instance by transferring its current service state (context) to the target application instance.

FIG. 17is an example communication diagram illustrating the use of the digital twin circuitry for application mobility. In state-of-the-art design, the trigger for application mobility is based on the information of UE movement to a new serving cell provided by network functions. In such a design, the application mobility would always be delayed behind the user's mobility since the application relocation takes certain time duration to complete. To keep up the application mobility with the user's mobility in real time, example approaches disclosed herein utilize a DT-based application mobility initiation procedure through which the preparation for application mobility can be started ahead of a movement of a UE into the target cell.

FIG. 17illustrates the use of an API based framework for application mobility service (AMS)1730, in which the application relocation and context transfer (e.g., movement of an app from a source1710to a target1760) can be performed via MEC platform managers (MEPMs1720,1750), and MEC orchestrator (MEO)1740.

First, the DT circuitry830sends a message to the AMS1730for subscribing to the change notifications for all the UEs within its coverage. This way, the DT circuitry830keeps track of all the application instances associated with the UEs of interest. In some examples, the subscription to AMS notifications may instead be made by the EPS845. When a new application is instantiated in serving MEC platform (S-MEP)1720of a UE, the DT circuitry830receives a notification from AMS about this information. As noted above, the DT circuitry830performs simulations and predicts if a UE might require HO in the near future. Based on these predictions and the information about the associated application instances, the DT circuitry830sends application mobility preparation messages to the MEC orchestrator (MEC)1740. The application mobility preparation message can include, for example, a network ID of the UE, IDs of the associated application instances, identifiers of one or more target MEPs, etc.

After receiving the preparation message from DT circuitry830, the MEO1740can execute an application mobility preparation procedure, which may include instantiating applications in one or more target MEPs and synchronizing the context information with the original application instances. However, the original application instance continues serving the UE, until the HO occurs. After the HO to a target cell, the application configured trigger mechanism initiates the application mobility request to the MEO1740. For example, inFIG. 17, the RNI cell change notification received at S-MEP1720triggers the application mobility request. Then, the MEO can perform a fast application relocation and context transfer to the T-MEP1750. Because the MEO1740had already instantiated the application based on the recommendation message provided by the DT circuitry830. If the MEO1740had instantiated the application at multiple T-MEPs1750, then the other application instances will to be terminated by the MEO1740.

In the MEC system, there can be several MEC hosts/platforms, each covering a small geo-area. Hence, there can be several instances of DT application/service, each instance in a MEC host. Hence, the context of actors can be transferred between the DT instances based on the mobility of the associated users. This process is similar to the application mobility process. However, the difference is that all the users served by a MEC host can share a single DT instance in the MEC host. Hence, only the context data of actors need to be transferred to the target MEC host, if it already has DT circuitry830instantiated. Otherwise, the target host may need to instantiate DT circuitry830and then transfer the context(s) of the actors into the newly instantiated DT circuitry830.

In some examples, the DT circuitry830can continuously train AI models to learn the patterns of the service demands of the users as a function of geo-area and time of the day, using information including, for example, the types of services requested by UEs, their locations, and times of the requests, etc. as received by the DT circuitry830. Then, the DT circuitry830may then generate messages to pre-emptively configure the gNBs (and/or perform other mobility tasks) for spectrum resources and load balancing appropriately for different times of a day. In some examples, different gNBs can be pre-emptively configured with different transmit powers to adjust their cell sizes for appropriate load balancing between the cells based on the expected traffic and service types from the UEs, for the given time of the day. As another example, in each cell, the spectrum resources can be pre-emptively allocated to different network slices based on the expected traffic and service types from the UEs, for the given time of the day. In another example, depending on the expected traffic distribution over the geo-area during certain time of a day, some gNBs/RSUs can be pre-emptively turned OFF to improve energy efficiency of the network.

FIG. 18is a block diagram of an example processor platform1800structured to execute and/or instantiate some or all of the machine readable instructions and/or operations ofFIG. 11to implement the digital twin circuitry830ofFIG. 10. The processor platform1800can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network), a mobile device (e.g., a cell phone, a smart phone, a tablet such as an iPad™), a personal digital assistant (PDA), an Internet appliance, or any other type of computing device.

The processor platform1800of the illustrated example includes processor circuitry1812. The processor circuitry1812of the illustrated example is hardware. For example, the processor circuitry1812can be implemented by one or more integrated circuits, logic circuits, FPGAs microprocessors, CPUs, GPUs, DSPs, and/or microcontrollers from any desired family or manufacturer. The processor circuitry1812may be implemented by one or more semiconductor based (e.g., silicon based) devices. In this example, the processor circuitry1812implements the example information accessor circuitry1010, the example virtual environment management circuitry1020, the example simulation circuitry1040, the example recommendation generator1050, the example recommendation manager circuitry1060, and the example recommendation provider circuitry1070.

The processor circuitry1812of the illustrated example includes a local memory1813(e.g., a cache, registers, etc.). The processor circuitry1812of the illustrated example is in communication with a main memory including a volatile memory1814and a non-volatile memory1816by a bus1818. The volatile memory1814may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory (RDRAM®), and/or any other type of RAM device. The non-volatile memory1816may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory1814,1816of the illustrated example is controlled by a memory controller1817.

The processor platform1800of the illustrated example also includes interface circuitry1820. The interface circuitry1820may be implemented by hardware in accordance with any type of interface standard, such as an Ethernet interface, a universal serial bus (USB) interface, a Bluetooth® interface, a near field communication (NFC) interface, a PCI interface, and/or a PCIe interface.

In the illustrated example, one or more input devices1822are connected to the interface circuitry1820. The input device(s)1822permit(s) a user to enter data and/or commands into the processor circuitry1812. The input device(s)1822can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, an isopoint device, and/or a voice recognition system.

One or more output devices1824are also connected to the interface circuitry1820of the illustrated example. The output devices1824can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display (LCD), a cathode ray tube (CRT) display, an in-place switching (IPS) display, a touchscreen, etc.), a tactile output device, a printer, and/or speaker. The interface circuitry1820of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip, and/or graphics processor circuitry such as a GPU.

The processor platform1800of the illustrated example also includes one or more mass storage devices1828to store software and/or data. Examples of such mass storage devices1828include magnetic storage devices, optical storage devices, floppy disk drives, HDDs, CDs, Blu-ray disk drives, redundant array of independent disks (RAID) systems, solid state storage devices such as flash memory devices, and DVD drives.

The machine executable instructions1832, which may be implemented by the machine readable instructions ofFIG. 11, may be stored in the mass storage device1828, in the volatile memory1814, in the non-volatile memory1816, and/or on a removable non-transitory computer readable storage medium such as a CD or DVD.

FIG. 15is a block diagram of an example implementation of the processor circuitry1812ofFIG. 18. In this example, the processor circuitry1812ofFIG. 18is implemented by a microprocessor1900. For example, the microprocessor1900may implement multi-core hardware circuitry such as a CPU, a DSP, a GPU, an XPU, etc. Although it may include any number of example cores1902(e.g., 1 core), the microprocessor1900of this example is a multi-core semiconductor device including N cores. The cores1902of the microprocessor1900may operate independently or may cooperate to execute machine readable instructions. For example, machine code corresponding to a firmware program, an embedded software program, or a software program may be executed by one of the cores1902or may be executed by multiple ones of the cores1902at the same or different times. In some examples, the machine code corresponding to the firmware program, the embedded software program, or the software program is split into threads and executed in parallel by two or more of the cores1902. The software program may correspond to a portion or all of the machine readable instructions and/or operations represented by the flowchart ofFIG. 19.

The cores1902may communicate by an example bus1904. In some examples, the bus1904may implement a communication bus to effectuate communication associated with one(s) of the cores1902. For example, the bus1904may implement at least one of an Inter-Integrated Circuit (I2C) bus, a Serial Peripheral Interface (SPI) bus, a PCI bus, or a PCIe bus. Additionally or alternatively, the bus1904may implement any other type of computing or electrical bus. The cores1902may obtain data, instructions, and/or signals from one or more external devices by example interface circuitry1906. The cores1902may output data, instructions, and/or signals to the one or more external devices by the interface circuitry1906. Although the cores1902of this example include example local memory1920(e.g., Level 1 (L1) cache that may be split into an L1 data cache and an L1 instruction cache), the microprocessor1900also includes example shared memory1910that may be shared by the cores (e.g., Level 2 (L2_cache)) for high-speed access to data and/or instructions. Data and/or instructions may be transferred (e.g., shared) by writing to and/or reading from the shared memory1910. The local memory1920of each of the cores1902and the shared memory1910may be part of a hierarchy of storage devices including multiple levels of cache memory and the main memory (e.g., the main memory1814,1816ofFIG. 18). Typically, higher levels of memory in the hierarchy exhibit lower access time and have smaller storage capacity than lower levels of memory. Changes in the various levels of the cache hierarchy are managed (e.g., coordinated) by a cache coherency policy.

Each core1902may be referred to as a CPU, DSP, GPU, etc., or any other type of hardware circuitry. Each core1902includes control unit circuitry1914, arithmetic and logic (AL) circuitry (sometimes referred to as an ALU)1916, a plurality of registers1918, the L1 cache1920, and an example bus1922. Other structures may be present. For example, each core1902may include vector unit circuitry, single instruction multiple data (SIMD) unit circuitry, load/store unit (LSU) circuitry, branch/jump unit circuitry, floating-point unit (FPU) circuitry, etc. The control unit circuitry1914includes semiconductor-based circuits structured to control (e.g., coordinate) data movement within the corresponding core1902. The AL circuitry1916includes semiconductor-based circuits structured to perform one or more mathematic and/or logic operations on the data within the corresponding core1902. The AL circuitry1916of some examples performs integer based operations. In other examples, the AL circuitry1916also performs floating point operations. In yet other examples, the AL circuitry1916may include first AL circuitry that performs integer based operations and second AL circuitry that performs floating point operations. In some examples, the AL circuitry1916may be referred to as an Arithmetic Logic Unit (ALU). The registers1918are semiconductor-based structures to store data and/or instructions such as results of one or more of the operations performed by the AL circuitry1916of the corresponding core1902. For example, the registers1918may include vector register(s), SIMD register(s), general purpose register(s), flag register(s), segment register(s), machine specific register(s), instruction pointer register(s), control register(s), debug register(s), memory management register(s), machine check register(s), etc. The registers1918may be arranged in a bank as shown inFIG. 19. Alternatively, the registers1918may be organized in any other arrangement, format, or structure including distributed throughout the core1902to shorten access time. The bus1920may implement at least one of an I2C bus, a SPI bus, a PCI bus, or a PCIe bus

FIG. 20is a block diagram of another example implementation of the processor circuitry1812ofFIG. 18. In this example, the processor circuitry1812is implemented by FPGA circuitry2000. The FPGA circuitry2000can be used, for example, to perform operations that could otherwise be performed by the example microprocessor1800ofFIG. 18executing corresponding machine readable instructions. However, once configured, the FPGA circuitry2000instantiates the machine readable instructions in hardware and, thus, can often execute the operations faster than they could be performed by a general purpose microprocessor executing the corresponding software.

In the example ofFIG. 20, the FPGA circuitry2000is structured to be programmed (and/or reprogrammed one or more times) by an end user by a hardware description language (HDL) such as Verilog. The FPGA circuitry2000ofFIG. 20, includes example input/output (I/O) circuitry2002to obtain and/or output data to/from example configuration circuitry2004and/or external hardware (e.g., external hardware circuitry)2006. For example, the configuration circuitry2004may implement interface circuitry that may obtain machine readable instructions to configure the FPGA circuitry2000, or portion(s) thereof. In some such examples, the configuration circuitry2004may obtain the machine readable instructions from a user, a machine (e.g., hardware circuitry (e.g., programmed or dedicated circuitry) that may implement an Artificial Intelligence/Machine Learning (AI/ML) model to generate the instructions), etc. In some examples, the external hardware2006may implement the microprocessor1900ofFIG. 9. The FPGA circuitry2000also includes an array of example logic gate circuitry2008, a plurality of example configurable interconnections2010, and example storage circuitry2012. The logic gate circuitry2008and interconnections2010are configurable to instantiate one or more operations that may correspond to at least some of the machine readable instructions ofFIG. 11and/or other desired operations. The logic gate circuitry2008shown inFIG. 20is fabricated in groups or blocks. Each block includes semiconductor-based electrical structures that may be configured into logic circuits. In some examples, the electrical structures include logic gates (e.g., And gates, Or gates, Nor gates, etc.) that provide basic building blocks for logic circuits. Electrically controllable switches (e.g., transistors) are present within each of the logic gate circuitry2008to enable configuration of the electrical structures and/or the logic gates to form circuits to perform desired operations. The logic gate circuitry2008may include other electrical structures such as look-up tables (LUTs), registers (e.g., flip-flops or latches), multiplexers, etc.

The interconnections2010of the illustrated example are conductive pathways, traces, vias, or the like that may include electrically controllable switches (e.g., transistors) whose state can be changed by programming (e.g., using an HDL instruction language) to activate or deactivate one or more connections between one or more of the logic gate circuitry2008to program desired logic circuits.

The storage circuitry2012of the illustrated example is structured to store result(s) of the one or more of the operations performed by corresponding logic gates. The storage circuitry2012may be implemented by registers or the like. In the illustrated example, the storage circuitry2012is distributed amongst the logic gate circuitry2008to facilitate access and increase execution speed.

The example FPGA circuitry2000ofFIG. 20also includes example Dedicated Operations Circuitry2014. In this example, the Dedicated Operations Circuitry2014includes special purpose circuitry2016that may be invoked to implement commonly used functions to avoid the need to program those functions in the field. Examples of such special purpose circuitry2016include memory (e.g., DRAM) controller circuitry, PCIe controller circuitry, clock circuitry, transceiver circuitry, memory, and multiplier-accumulator circuitry. Other types of special purpose circuitry may be present. In some examples, the FPGA circuitry2000may also include example general purpose programmable circuitry2018such as an example CPU2020and/or an example DSP2022. Other general purpose programmable circuitry2018may additionally or alternatively be present such as a GPU, an XPU, etc., that can be programmed to perform other operations.

AlthoughFIGS. 19 and 20illustrate two example implementations of the processor circuitry1812ofFIG. 18, many other approaches are contemplated. For example, as mentioned above, modern FPGA circuitry may include an on-board CPU, such as one or more of the example CPU2020ofFIG. 20. Therefore, the processor circuitry1812ofFIG. 18may additionally be implemented by combining the example microprocessor1900ofFIG. 19and the example FPGA circuitry2000ofFIG. 20. In some such hybrid examples, a first portion of the machine readable instructions represented by the flowchart ofFIG. 1may be executed by one or more of the cores1902ofFIG. 19and a second portion of the machine readable instructions represented by the flowchart ofFIG. 11may be executed by the FPGA circuitry2000ofFIG. 20.

In some examples, the processor circuitry1812ofFIG. 18may be in one or more packages. For example, the processor circuitry500ofFIG. 5and/or the FPGA circuitry _00ofFIG. 5may be in one or more packages. In some examples, an XPU may be implemented by the processor circuitry1812ofFIG. 18, which may be in one or more packages. For example, the XPU may include a CPU in one package, a DSP in another package, a GPU in yet another package, and an FPGA in still yet another package.

A block diagram illustrating an example software distribution platform1805to distribute software such as the example machine readable instructions1832ofFIG. 18to hardware devices owned and/or operated by third parties is illustrated inFIG. 21. The example software distribution platform2105may be implemented by any computer server, data facility, cloud service, etc., capable of storing and transmitting software to other computing devices. The third parties may be customers of the entity owning and/or operating the software distribution platform2105. For example, the entity that owns and/or operates the software distribution platform2105may be a developer, a seller, and/or a licensor of software such as the example machine readable instructions1832ofFIG. 18. The third parties may be consumers, users, retailers, OEMs, etc., who purchase and/or license the software for use and/or re-sale and/or sub-licensing. In the illustrated example, the software distribution platform2105includes one or more servers and one or more storage devices. The storage devices store the machine readable instructions1832, which may correspond to the example machine readable instructions ofFIG. 11, as described above. The one or more servers of the example software distribution platform2105are in communication with a network2110, which may correspond to any one or more of the Internet and/or any of the example networks1826described above. In some examples, the one or more servers are responsive to requests to transmit the software to a requesting party as part of a commercial transaction. Payment for the delivery, sale, and/or license of the software may be handled by the one or more servers of the software distribution platform and/or by a third party payment entity. The servers enable purchasers and/or licensors to download the machine readable instructions1832from the software distribution platform2105. For example, the software, which may correspond to the example machine readable instructionsFIG. 11, may be downloaded to the example processor platform1800, which is to execute the machine readable instructions1832to implement the digital twin circuitry830. In some example, one or more servers of the software distribution platform2105periodically offer, transmit, and/or force updates to the software (e.g., the example machine readable instructions1832ofFIG. 18) to ensure improvements, patches, updates, etc., are distributed and applied to the software at the end user devices.

From the foregoing, it will be appreciated that example systems, methods, apparatus, and articles of manufacture have been disclosed that enable a digital twin to be utilized to enhance resiliency and/or reliability of a communications network. The disclosed systems, methods, apparatus, and articles of manufacture improve the efficiency of using a computing device by ensuring that networks with which such computing devices communicate are more robust. The disclosed systems, methods, apparatus, and articles of manufacture are accordingly directed to one or more improvement(s) in the operation of a machine such as a computer or other electronic and/or mechanical device.

Example methods, apparatus, systems, and articles of manufacture for digital twin aided resiliency are disclosed herein. Further examples and combinations thereof include the following:

Example 1 includes an apparatus for digital twin aided resiliency, the apparatus comprising interface circuitry, processor circuitry including one or more of at least one of a central processing unit, a graphic processing unit or a digital signal processor, the at least one of the central processing unit, the graphic processing unit or the digital signal processor having control circuitry to control data movement within the processor circuitry, arithmetic and logic circuitry to perform one or more first operations corresponding to instructions, and one or more registers to store a result of the one or more first operations, the instructions in the apparatus, a Field Programmable Gate Array (FPGA), the FPGA including logic gate circuitry, a plurality of configurable interconnections, and storage circuitry, the logic gate circuitry and interconnections to perform one or more second operations, the storage circuitry to store a result of the one or more second operations, or Application Specific Integrated Circuitry (ASIC) including logic gate circuitry to perform one or more third operations, the processor circuitry to perform at least one of the first operations, the second operations or the third operations to instantiate information accessor circuitry to access operational statistics corresponding to one or more physical entities, the one or more physical entities including user equipment and network equipment, virtual environment management circuitry to update one or more virtual entities within a virtual environment that correspond, respectively, to the one or more physical entities with the operational statistics, simulation circuitry to simulate a change to the virtual environment based on the operational statistics, the simulated change to the virtual environment representing a future state, recommendation generator circuitry to generate a recommendation for the network equipment to perform a task based on the simulated change, and recommendation provider circuitry to, in response to determining at least one of a confidence of the recommendation meets a threshold confidence or a predefined condition is met, provide the recommendation to the network equipment.

Example 2 includes the apparatus of example 1, wherein the operational statistics correspond to semantic and kinematic information of the one or more physical entities.

Example 3 includes the apparatus of example 1, wherein the operational statistics correspond to network information and measurement reports of the one or more physical entities.

Example 4 includes the apparatus of example 3, wherein the operational statistics correspond to local environment conditions.

Example 5 includes the apparatus of example 4, wherein the local environment conditions include a local weather condition.

Example 6 includes the apparatus of example 1, wherein the network equipment is a roadside unit (RSU), and the recommendation for the network equipment to perform the task is a recommendation to mitigate a potential beam failure.

Example 7 includes the apparatus of example 1, wherein the processor circuitry is to perform at least one of the first operations, the second operations or the third operations to instantiate a recommendation service to convey the recommendation to a 5G network.

Example 8 includes the apparatus of example 1, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to perform a handover.

Example 9 includes the apparatus of example 8, wherein the recommendation provider circuitry is to provide the recommendation for the network equipment to perform the handover to the network equipment prior to the user equipment requesting the handover.

Example 10 includes the apparatus of example 8, wherein the handover is a conditional handover.

Example 11 includes the apparatus of example 1, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to perform beam failure recovery.

Example 12 includes the apparatus of example 11, wherein the beam failure recovery is a proactive beam failure recovery.

Example 13 includes the apparatus of example 1, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to perform an application mobility preparation procedure.

Example 14 includes the apparatus of example 1, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to utilize dual connectivity to avoid a ping pong effect.

Example 15 includes the apparatus of example 1, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to initiate a new connection with a target cell, without dropping an existing connection with a serving cell.

Example 16 includes at least one non-transitory computer readable medium comprising instructions that, when executed, cause at least one processor to at least access operational statistics corresponding to one or more physical entities, the one or more physical entities including user equipment and network equipment, update one or more virtual entities within a virtual environment that correspond, respectively, to the one or more physical entities with the operational statistics, simulate a change to the virtual environment based on the operational statistics, the simulated change to the virtual environment representing a future state, generate a recommendation for the network equipment to perform a task based on the simulated change, and in response to determining a confidence of the recommendation meets a threshold confidence, provide the recommendation to the network equipment.

Example 17 includes the at least one non-transitory computer readable medium of example 16, wherein the operational statistics correspond to semantic and kinematic information of the one or more physical entities.

Example 18 includes the at least one non-transitory computer readable medium of example of example 16, wherein the operational statistic corresponds to a network information and measurement report of the one or more physical entities.

Example 19 includes the at least one non-transitory computer readable medium of example 18, wherein the operational statistics correspond to local environment conditions.

Example 20 includes the at least one non-transitory computer readable medium of example 19, wherein the local environment conditions include a local weather condition.

Example 21 includes the at least one non-transitory computer readable medium of example 16, wherein the network equipment is a roadside unit (RSU), and the recommendation for the network equipment to perform the task is a recommendation to mitigate a potential beam failure.

Example 22 includes the at least one non-transitory computer readable medium of example 16, wherein the instructions, when executed, further cause the at least one processor to execute a recommendation service to convey the recommendation to a 5G network.

Example 23 includes the at least one non-transitory computer readable medium of example of example 16, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to perform a handover.

Example 24 includes the at least one non-transitory computer readable medium of example of example 23, wherein the recommendation for the network equipment to perform the handover is provided to the network equipment prior to the user equipment requesting the handover.

Example 25 includes the at least one non-transitory computer readable medium of example of example 23, wherein handover is a conditional handover.

Example 26 includes the at least one non-transitory computer readable medium of example of example 16, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to perform beam failure recovery.

Example 27 includes the at least one non-transitory computer readable medium of example 26, wherein the beam failure recovery is a preemptive beam failure recovery.

Example 28 includes the at least one non-transitory computer readable medium of example 16, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to perform an application mobility preparation procedure.

Example 29 includes the at least one non-transitory computer readable medium of example 16, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to utilize dual connectivity to avoid a ping pong effect.

Example 30 includes the at least one non-transitory computer readable medium of example 16, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to initiate a new connection with a target cell, without dropping an existing connection with a serving cell.

Example 31 includes an apparatus for digital twin aided resiliency, the apparatus comprising means for accessing operational statistics corresponding to one or more physical entities, the one or more physical entities including user equipment and network equipment, means for updating one or more virtual entities within a virtual environment that correspond, respectively, to the one or more physical entities with the operational statistics, means for simulating a change to the virtual environment based on the operational statistics, the simulated change the virtual environment representing a figure state, means for generating a recommendation for the network equipment to perform a task based on the simulated change, and means for providing, in response to determining a confidence of the recommendation meets a threshold confidence, the recommendation to the network equipment.

Example 32 includes the apparatus of example 31, wherein the operational statistics correspond to semantic and kinematic information of the one or more physical entities.

Example 33 includes the apparatus of example 31, wherein the operational statistics correspond to network information and measurement reports of the one or more physical entities.

Example 34 includes the apparatus of example 33, wherein the operational statistics correspond to local environment conditions.

Example 35 includes the apparatus of example 34, wherein the local environment conditions include a local weather condition.

Example 36 includes the apparatus of example 31, wherein the network equipment is a roadside unit (RSU), and the recommendation for the network equipment to perform the task is a recommendation to mitigate a potential beam failure.

Example 37 includes the apparatus of example 31, further including means for conveying the recommendation to a 5G network.

Example 38 includes the apparatus of example 31, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to perform a handover.

Example 39 includes the apparatus of example 38, wherein the recommendation for the network equipment to perform the handover is provided to the network equipment prior to the user equipment requesting the handover.

Example 40 includes the apparatus of example 38, wherein the handover is a conditional handover.

Example 41 includes the apparatus of example 31, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to perform beam failure recovery.

Example 42 includes the apparatus of example 41, wherein the beam failure recovery is a proactive beam failure recovery.

Example 43 includes the apparatus of example 31, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to perform an application mobility preparation procedure.

Example 44 includes the apparatus of example 31, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to utilize dual connectivity to avoid a ping pong effect.

Example 45 includes the apparatus of example 31, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to initiate a new connection with a target cell, without dropping an existing connection with a serving cell.

Example 46 includes a method for digital twin aided resiliency, the method comprising accessing operational statistics corresponding to one or more physical entities, the one or more physical entities including user equipment and network equipment, updating one or more virtual entities within a virtual environment that correspond, respectively, to the one or more physical entities with the operational statistics, simulating a change to the virtual environment based on the operational statistics, the simulated change to the virtual environment representing a future state, generating a recommendation for the network equipment to perform a task based on the simulated change, and in response to determining a confidence of the recommendation meets a threshold confidence, provide the recommendation to the network equipment.

Example 47 includes the method of example 46, wherein the operational statistics correspond to semantic and kinematic information of the one or more physical entities.

Example 48 includes the method of example 46, wherein the operational statistics correspond to network information and measurement reports of the one or more physical entities.

Example 49 includes the method of example 48, wherein the operational statistics correspond to local environment conditions.

Example 50 includes the method of example 49, wherein the local environment conditions include a local weather condition.

Example 51 includes the method of example 46, wherein the network equipment is a roadside unit (RSU), and the recommendation for the network equipment to perform the task is a recommendation to mitigate a potential beam failure.

Example 52 includes the method of example 46, wherein the instructions, when executed, further cause the at least one processor to execute a recommendation service to convey the recommendation to a 5G network.

Example 53 includes the method of example 46, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to perform a handover.

Example 54 includes the method of example 53, wherein the recommendation for the network equipment to perform the handover is provided to the network equipment prior to the user equipment requesting the handover.

Example 55 includes the method of example 53, wherein the handover is a conditional handover.

Example 56 includes the method of example 46, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to perform beam failure recovery.

Example 57 includes the method of example 56, wherein the beam failure recovery is a preemptive beam failure recovery.

Example 58 includes the method of example 46, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to perform an application mobility preparation procedure.

Example 59 includes the method of example 46, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to utilize dual connectivity to avoid a ping pong effect.

Example 60 includes the method of example 46, wherein the recommendation for the network equipment to perform the task is a recommendation for the network equipment to initiate a new connection with a target cell, without dropping an existing connection with a serving cell.