AI Named Function Infrastructure and Methods

Methods, apparatus, systems, and articles of manufacture to manage an edge infrastructure including a plurality of artificial intelligence models are disclosed. An example edge infrastructure apparatus includes a model data structure to identify a plurality of models and associated meta-data from a plurality of circuitry connectable via the edge infrastructure apparatus. The example apparatus includes model inventory circuitry to manage the model data structure to at least one of query for one or more models, add a model, update a model, or remove a model from the model data structure. The example apparatus includes model discovery circuitry to select at least one selected model of the plurality of models identified in the model data structure in response to a query. The example apparatus includes execution logic circuitry to inference the selected model.

FIELD OF THE DISCLOSURE

This disclosure relates generally to artificial intelligence infrastructure, and, more particularly, to artificial intelligence named function infrastructure and associated methods.

BACKGROUND

Edge computing is emerging as a platform for ultra-low latency access to compute resources for a large emerging class of applications. However, current edge computing configurations lack an infrastructure to manage applications and access to compute resources. Additionally, cross-edge information, communication, and management is limited or even unavailable in current edge computing platforms. As such, there is a need for improved, cross-edge resource and application management.

The figures are not to scale. In general, the same reference numbers will be used throughout the drawing(s) and accompanying written description to refer to the same or like parts. Connection references (e.g., attached, coupled, connected, and joined) are to be construed broadly and may include intermediate members between a collection of elements and relative movement between elements unless otherwise indicated. As such, connection references do not necessarily infer that two elements are directly connected and in fixed relation to each other.

DETAILED DESCRIPTION

Descriptors “first,” “second,” “third,” etc. are used herein when identifying multiple elements or components which may be referred to separately. Unless otherwise specified or understood based on their context of use, such descriptors are not intended to impute any meaning of priority, physical order or arrangement in a list, or ordering in time but are merely used as labels for referring to multiple elements or components separately for ease of understanding the disclosed examples. In some examples, the descriptor “first” may be used to refer to an element in the detailed description, while the same element may be referred to in a claim with a different descriptor such as “second” or “third.” In such instances, it should be understood that such descriptors are used merely for ease of referencing multiple elements or components. 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.

Edge computing, at a general level, refers to the transition of compute and storage resources closer to endpoint devices (e.g., consumer computing devices, user equipment, etc.) in order to optimize total cost of ownership, reduce application latency, improve service capabilities, and improve compliance with security or data privacy requirements. Edge computing may, in some scenarios, provide a cloud-like distributed service that offers orchestration and management for applications among many types of storage and compute resources. As a result, some implementations of edge computing have been referred to as the “edge cloud” or the “fog”, as powerful computing resources previously available only in large remote data centers are moved closer to endpoints and made available for use by consumers at the “edge” of the network.

Edge computing use cases in mobile network settings have been developed for integration with Multi-access Edge Computing (MEC) approaches, also known as “mobile edge computing.” MEC approaches are designed to allow application developers and content providers to access computing capabilities and an information technology (IT) service environment in dynamic mobile network settings at the edge of the network. Limited standards have been developed by the European Telecommunications Standards Institute (ETSI) industry specification group (ISG) in an attempt to define common interfaces for operation of MEC systems, platforms, hosts, services, and applications.

Edge computing, satellite edge computing (e.g., edge nodes connected to the Internet via satellite), MEC, and related technologies attempt to provide reduced latency, increased responsiveness, and more available computing power than offered in traditional cloud network services and wide area network connections. However, the integration of mobility and dynamically launched services to some mobile use and device processing use cases has led to limitations and concerns with orchestration, functional coordination, and resource management, especially in complex mobility settings where many participants (e.g., devices, hosts, tenants, service providers, operators, etc.) are involved.

In a similar manner, Internet of Things (IoT) networks and devices are designed to offer a distributed compute arrangement from a variety of endpoints. IoT devices can be physical or virtualized objects that may communicate on a network, and can include sensors, actuators, and other input/output components, which may be used to collect data or perform actions in a real-world environment. For example, IoT devices can include low-powered endpoint devices that are embedded or attached to everyday things, such as buildings, vehicles, packages, etc., to provide an additional level of artificial sensory perception of those things. IoT devices have become more popular and thus applications using these devices have proliferated.

In some examples, an edge environment can include an enterprise edge in which communication with and/or communication within the enterprise edge can be facilitated via wireless and/or wired connectivity. The deployment of various Edge, Fog, MEC, and IoT networks, devices, and services have introduced a number of advanced use cases and scenarios occurring at and towards the edge of the network. However, these advanced use cases have also introduced a number of corresponding technical challenges relating to security, processing and network resources, service availability and efficiency, among many other issues. One such challenge is in relation to Edge, Fog, MEC, and IoT networks, devices, and services executing workloads on behalf of endpoint devices including establishing provenance to determine data integrity and/or data restrictions.

The present techniques and configurations may be utilized in connection with many aspects of current networking systems, but are provided with reference to Edge Cloud, IoT, MEC, and other distributed computing deployments. The following systems and techniques may be implemented in, or augment, a variety of distributed, virtualized, or managed edge computing systems. These include environments in which network services are implemented or managed using MEC, fourth generation (4G) or fifth generation (5G) wireless network configurations; or in wired network configurations involving fiber, copper, and/or other connections. Further, aspects of processing by the respective computing components may involve computational elements which are in geographical proximity of user equipment or other endpoint locations, such as a smartphone, vehicular communication component, IoT device, etc. Further, the presently disclosed techniques may relate to other Edge/MEC/IoT network communication standards and configurations, and other intermediate processing entities and architectures.

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 computing platform implemented at base stations, gateways, network routers, or other devices which are much closer to end point devices producing and consuming the data. For example, edge gateway servers may be equipped with pools of memory and storage resources (e.g., memory circuitry) to perform computations 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. As another example, central office network management hardware may be replaced with computing hardware that performs virtualized network functions and offers compute resources for the execution of services and consumer functions for connected devices.

Edge environments include networks and/or portions of networks that are located between a cloud environment and an endpoint environment. Edge environments enable computations of workloads at edges of a network. For example, an endpoint device (e.g., a user device) may request a nearby base station to compute a workload rather than a central server in a cloud environment. Edge environments include edge services (e.g., an edge platform for hire (EPH)), which include pools of memory, storage resources, and processing resources. In some examples, edge environments may include an edge as a service (EaaS), which may include one or more edge services. Edge services perform computations, such as an execution of a workload, on behalf of other edge services, edge nodes (e.g., EPH nodes), endpoint devices, etc. Edge environments facilitate connections between producers (e.g., workload executors, edge services) and consumers (e.g., other edge services, endpoint devices).

Because edge services may be closer in proximity to endpoint devices than centralized servers in cloud environments, edge services enable computations of workloads with a lower latency (e.g., response time) than cloud environments. Edge services may also enable a localized execution of a workload based on geographic locations or network topographies. For example, an endpoint device may require a workload to be executed in a first geographic area, but a centralized server may be located in a second geographic area. The endpoint device can request a workload execution by an edge service located in the first geographic area to comply with corporate or regulatory restrictions.

Examples of workloads to be executed in an edge environment (e.g., via an EaaS, via an edge service, on an EPH node, etc.) include autonomous driving computations, video surveillance monitoring, machine learning model executions, and real time data analytics. Additional examples of workloads include delivering and/or encoding media streams, measuring advertisement impression rates, object detection in media streams, speech analytics, asset and/or inventory management, and augmented reality processing.

In some examples, edge services enable both the execution of workloads and a return of a result of an executed workload to endpoint devices with a response time lower than the response time of a server in a cloud environment. For example, if an edge service is located closer to an endpoint device on a network than a cloud server, the edge service may respond to workload execution requests from the endpoint device faster than the cloud server. An endpoint device may request an execution of a time-constrained workload from an edge service rather than a cloud server.

In addition, edge services enable the distribution and decentralization of workload executions. For example, an endpoint device may request a first workload execution and a second workload execution. In some examples, a cloud server may respond to both workload execution requests. With an edge environment, however, a first edge service may execute the first workload execution request, and a second edge service may execute the second workload execution request.

Additional infrastructure may be included in an edge environment to facilitate the execution of workloads on behalf of endpoint devices. For example, an orchestrator may access a request to execute a workload from an endpoint device and provide offers to a plurality of edge nodes. The offers may include a description of the workload to be executed and terms regarding energy and resource constraints. An edge node (e.g., an EPH node) may accept the offer, execute the workload, and provide a result of the execution to infrastructure in the edge environment and/or to the endpoint device.

Delivery of services in an Edge as a Service (EaaS) ecosystem (e.g., in an edge environment, via an EPH, via an edge infrastructure element, etc.) may include a business model where subscribers to the EaaS service (e.g., endpoint devices, user devices, etc.) pay for access to edge services. In some examples, the endpoint devices may pay for edge services (such as an execution of a workload) via micro-payments, credits, tokens, e-currencies, etc. In some examples, revenue models may include mobile network operators (MNOs) that maintain subscriptions from a subscriber base (such as one or more networks) as a way to pay for edge services by entering into service-level agreement (SLA) contracts. An SLA can include one or more service level objectives (SLOs), for example. An SLO can include a metric such as uptime, response time, etc. In certain examples, SLA correspond to resources used to achieve a type of SLO. For example, SLA specifies a number of cores, memory bandwidth, etc., to achieve an SLO of 30 frames per second of an artificial intelligence model, etc. Accounting executed and/or managed by the MNO may determine billable services that are then applied to subscriber accounts.

In certain examples, a single resource or entity can negotiate and manage multiple SLA in an edge environment while managing its resources using one or more SLOs. A SLO is based in an edge cloud environment, for example. The SLO can be instantiated inside a service. The SLA is instantiated within an associated resource. Multiple SLA may compete for particular resources. In certain examples, SLAs can be grouped with an associated level of trust. Grouping of SLAs can be dynamic, based on requirement, trust, user/device type, etc. A group key can be associated with the SLAs, for example. In certain examples, different tenants, entities, resources, and/or other actors can work together to drive one or more SLOs, SLAs, etc.

The rapid growth of edge computing and associated IoT technology presents a challenge and an opportunity for trusted integration and/or interconnection of proprietary technologies, instructions, and/or data. Problems can be further complicated at the edge of the network, where the computing infrastructure is heterogeneous, different systems are maintained in different locations and subject to different failure profiles, timing anomalies are more likely due to non-uniform communication and non-localized placements, and other obfuscating factors, such as power-constrained and bandwidth-constrained distribution of tasks, exist. Adding to these complications is the problem that different edge locations can belong to different trust boundaries. As such, a chain of actions that spans different microservices in loosely-coupled interactions can also be transparent, semi-transparent, or opaque with respect to execution of software, for example. As used herein, the terms “microservice, “service”, “task”, “operation”, and “function” can be used interchangeably to indicate an application, a process, and/or other software code (also referred to as program code) for execution using computing infrastructure, such as an edge computing and/or other IoT environment.

Examples disclosed herein provide a compute infrastructure arranged with respect to one or more resource-constrained environments (e.g., base stations, central offices, etc.). Edge computing infrastructure (also referred to herein as edge infrastructure), taken alone or in combination with a cloud infrastructure, enables and supports a variety of applications and/or use cases, such as autonomous vehicles, drones, robotics, smart city cameras, etc. While an example cloud infrastructure has a latency of at least one hundred milliseconds (100+ ms), an example edge infrastructure provides lower latency (e.g., a few ms, etc.) and offers support for acceleration, training, inferencing, etc.

FIG. 1illustrates an example cloud-edge architecture100including an example cloud infrastructure110and an example edge infrastructure120in communication with one or more edge devices130(e.g., including on-device artificial intelligence (AI) models, etc.). AI algorithms can be deployed as models across the edge infrastructure120, for example. AI models are loaded into memory (e.g., dynamic random-access memory (DRAM), RAM, etc.), programmed into circuitry such as a field-programmable gate array (FPGA), etc., and/or otherwise made available for use on the edge infrastructure120.

In the example ofFIG. 1, the edge computing infrastructure120includes a regional data center130, an on-premise server140, and an aggregator gateway150. Different latency and different capabilities are associated with each of the edge computing infrastructure130-150. For example, the regional data center130forms an edge cloud with a latency of 5-10 ms and includes a networking system-on-a-chip (SOC)132, one or more AI accelerators134providing 128-512 trillion operations per second (TOPS), a server SOC136operating at approximately 100 Watts (˜100 W), and storage138. The example on-premise server140is a local server with a latency of 1-5 ms and includes a networking SOC142, one or more AI accelerators144providing 64-128 TOPS, a server SOC146operating ˜65 W, and storage148, for example. The example aggregator150forms a gateway and/or access point with a latency of 10 microseconds (us) to 1 ms and includes a networking SOC152, one or more AI accelerators154providing 16-64 TOPS, a gateway server SOC156operating at less than 30 W, and storage158, for example.

As shown in the example ofFIG. 1, one or more edge devices160-164are connected to the edge infrastructure120. For example, a television or other display160(e.g., 10-20 TOPS at less than 10 W, etc.) can be connected to the example edge infrastructure120. An example vehicle162(e.g., 30+ TOPS at less than 20 W, etc.) can be connected to the example edge infrastructure120. An example mobile device (e.g., phone, tablet, etc.)164(e.g., 4-20 TOPS at less than 5 W, etc.) can be connected to the example edge infrastructure120.

FIG. 2shows another example infrastructure200including a plurality of edge server circuitry210-220(also referred to herein as edge server apparatus), such as servers130-150, etc., interacting with a plurality of cloud infrastructure circuitry230-240via a network250. End devices260, such as vehicles, etc., can leverage edge server circuitry210-230and/or cloud circuitry230-240capabilities via the network250. The example edge server circuitry210-220include one or more processor circuitry (e.g., central processing units (CPUs), etc.)212,222and accelerator circuitry214,224(e.g., general processing units (GPUs), field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), smart network interface cards (NICs), etc.). The cloud platform circuitry230-240can also include one or more processors, accelerators, memory, etc.

One or more of the processor circuitry212,222and/or accelerator circuitry214,224can store one or more AI models270-273. One or more AI models274-279can also be available in the cloud circuitry230-240. However, the AI models270-279must be loaded into memory and prepared for the accelerator circuitry214,224. For example, a model270-279can be loaded into a GPU, programmed into an FPGA, stored in memory (e.g., DRAM, etc.), etc. Preparing and loading a model for an end device260can be a time-intensive process. As such, it would be beneficial to map one or more AI models270-279onto parts of the infrastructure circuitry210-240that is ready with the respective model(s)270-279. However, current edge infrastructures210,220are unable to track and maintain AI models270-279and associated mappings.

Certain examples provide an improved infrastructure for AI model processing, mapping, storage, and deployed. For example, the edge server circuitry210-220can determine which model(s)270-279to cache for reuse. The example edge server circuitry210-220can determine how to evolve models270-279included in the edge infrastructure formed by the example edge server circuitry210-220, for example. End user edge devices260can leverage the infrastructure to determine which AI models270-279are already available on the edge server circuitry210-220and/or the cloud platform circuitry230-240. End devices260can also determine current wait times to utilize AI model(s)270-279on the platforms210-240on which the model(s)270-279are available.

As such, certain examples improve the edge computing infrastructure by providing an ability to query and identify which AI models270-279are already available on which edge server circuitry210-220and/or cloud server circuitry230-240. Certain examples improve the edge computing infrastructure by determining a loaded on device queues in the edge server circuitry210-220. For example, a model may be loaded in an FPGA on one of the edge server circuitry210-220, but, if there are ten users ahead in the queue to use the same AI model270-279, the wait time may indicate that the model270-279on that server circuitry210-220is not a good choice of resource/server. A lack of information regarding wait time can defeat the purpose of using the edge infrastructure.

Additionally, certain examples improve the edge computing infrastructure by enabling a determine of how the AI models270-279are being used across the edge210-220and cloud230-240server infrastructure circuitry. Such model utilization can imply which model(s)270-279should be reused/prioritized to cache in memory, remain in FPGA logic, etc. As more end devices260contribute to a model270-279, certain examples identify which model(s)270-279should be evolved and maintained, for example.

FIG. 3illustrates another example infrastructure300, which improves upon the example infrastructure200to solve the problems and provide the technical improvements described above. In the example infrastructure300ofFIG. 3, the edge server circuitry210-220and the cloud circuitry230-240communicate via interface circuitry310-340, respectively, which provide an indication of available AI models270-279and accelerator queue wait/latency for the respective edge and/or cloud circuitry210-240. Such information (e.g., model availability, accelerator latency, etc.) is provided by the edge server circuitry210-220and the cloud circuitry230-240via their respective interface310-340to a tablet or other data store350. The example table350can store information for one or more AI models270-279such as associated computing device (e.g., edge server circuitry210-220, cloud circuitry230-240, etc.), wait time, popularity, evolution status, etc.

As shown in the example ofFIG. 3, a first type of AI model270,273,278is found at the edge server circuitry210, the edge server circuitry220, and the cloud circuitry240. Each of those devices has a different degree or level of wait time (e.g., high, medium, very high, etc.) associated with access to the AI model270,273,278. As indicated in the example table350, this AI model270,273,278is cached due to a history of high usage by many users. The example table350also indicates that the AI model270,273,278has evolved as the model270,273,278has been synchronized across multiple nodes. This information is gathered from the edge server circuitry210-220and cloud circuitry240storing the AI model270,273,278. Similarly, a second type of AI model271,274can be found on the edge server circuitry210and the cloud circuitry230, both with very high wait time. Due to low reuse, the example table350indicates that this AI model271,274is not cached due to low or infrequent reuse and is not evolved.

The plurality of end user devices260can access the table350via an edge infrastructure interface circuitry360. The edge infrastructure interface circuitry360can be a machine-to-machine interface and/or a user interface, for example. The example interface circuitry360allows a device260to access and query the table350to identify an AI model and determine a location from which to access that AI model. Information, such as wait time, cached/not cached, evolved/not evolved, etc., can factor into selecting a source210-240for a particular AI model270-279, where that model is located at multiple sources. Such information can be referred to as “cost”, and a requesting device260can evaluate costs associated with different sources210-240of an AI model270-279to determine from which source210-240to select and utilize the model270-279, for example.

As such, telemetry (e.g., network telemetry, edge appliance telemetry, etc.) can be provided via the interface circuitry310-340to sample device queues and estimate wait times across the infrastructure300for one or more AI models270-279. The telemetry information can be represented as options in the table350, to end devices260. The table350can be used to specify costs as one option, for various model/infrastructure choices. Further, usage of models270-279can be aggregated across all users260and used by the edge circuitry210-220to make decisions as to which models270-279to cache or prioritize for FPGA real estate, for example, as well as which models270-279to evolve, maintain, and propagate across the infrastructure300, for example.

In certain examples, the table350can identify which models270-279are proprietary and, therefore, limited to use by certain users260. In some examples, one or more models270-279may be hybrid models with certain public or general attribute(s), layer(s), etc., accessible to all users260and certain proprietary attribute(s), layer(s), etc., only accessible to certain users260(e.g., with a certain ownership, authorization, security level, etc.).

Certain examples provide information-centric networking (ICN) to facilitate and abstract or mask AI inferencing for edge applications with a function-as-a-service model. That is, inferencing or execution of AI models can be hidden, masked, or abstracted as a function call by the edge application to an edge appliance (e.g., an edge server, other edge device, etc.). Using an ICN communication model, content names, rather than network node location addresses (e.g., Internet Protocol (IP) addresses, etc.), are using for identification and communication, for example. Certain examples provide an infrastructure, such as the example infrastructure300, to name models270-279for training, including mutation and self-evolution of one or more of the models270-279. In certain examples, the edge computing infrastructure300caches the named models270-279(e.g., in the table350) and associated information for evaluation, selection, etc.

Certain examples enable edge devices or appliances (e.g., the example edge service circuitry210-220, etc.) to provide named annotated AI model inferencing (e.g., with an associated confidence level or score in the result or other output). The example infrastructure300uses output of the inferences to search for instances of the models with different properties. As such, the example infrastructure300manages an inventory for various AI instances of a plurality of AI named models (e.g., models270-279) that are hosted in various appliances (e.g., the edge server circuitry210-220, the cloud circuitry230-240, etc.) that belong to the edge infrastructure300. Each named model instance (e.g., a speech to text neural network (NN), etc.) has a set of meta-data fields that defines characteristics of the model instance (e.g. accuracy, latency etc.). In some examples, the edge infrastructure300includes a cache of AI named instances that may not currently be available in any edge appliance but that have been generated during a lifetime of the example edge infrastructure300.

In certain examples, as described further below, the edge infrastructure300is configured as an AI-named function (AI-NF) infrastructure. An AI-NF infrastructure organizes AI models according to name. Names for AI models, as well as particular instances of AI or AI-NF models, can be determined in a variety of ways. For example, metadata associated with a model can be used by another AI model (e.g., treated as a dataset with x datapoints, y labels, and associated categories) to train the AI model to derive names for particular AI models.

Example edge services can trigger AI-NF logic hosted on the infrastructure to execute an AI-NF model with a set of requirements or parameters (e.g., accuracy, latency, etc.). The AI-NF logic can discover and identify one or more AI-NF model instances based on an associated name and/or other identifier (e.g., according to an ICN mechanism, ICN-like mechanism, hybrid ICN mechanism, a transmission control protocol (TCP) mechanism, using other data communication construct, etc.). The AI-NF logic translates the requirements/parameters into a determination of which available AI model instance is suited (e.g., best suited, better suited, etc.) for the particular request. The AI-NF infrastructure can execute the determined/selected AI model instance in an associated edge appliance (e.g., the edge server circuitry210,220, the cloud circuitry230,240, etc.), for example. The AI-NF infrastructure can alternatively or additional provide an AI model implementation that satisfies the provided requirements/parameters and that can run on a requestor edge appliance. Selection of an appropriate edge appliance can be based on processing of information such as infrastructure (e.g., network, edge appliance, etc.) telemetry data (e.g., latency, utilization, input/output load, bandwidth, number of open connections, availability, etc.).

In certain examples, edge appliances can provide annotated inferences to the AI-NF infrastructure along with a corresponding confidence score/level/indicator forming one or more named, annotated data sets. The AI-NF infrastructure uses the named, annotated data sets (e.g., per domain, per type of model, etc.) to evaluate variants of AI model topologies to discover new models with new properties. For example, NN topologies can be evaluated for changing size of layers, introducing more convolutional (CONV) layers, etc. Once new models are discovered, the new models can be announced to the edge appliances and/or stored locally, for example.

In certain examples, each named AI model instance can be tagged with other data that can be used to manage processes, rules, etc., such as general data protection regulation (GDPR), data sovereignty, proprietary ownership, restricted access/security, etc., that can be used for the AI-NF infrastructure to decide which AI model instance(s) can be used and where such AI model instance(s) can be used. Certain examples add contextual information so that the AI-NF infrastructure can apply automatic policies to evaluate and use AI model instances.

FIG. 4illustrates an example configuration of the example edge computing infrastructure300in which the example interface circuitry310,320is combined into an AI-NF infrastructure circuitry410. The example edge server circuitry210,220,420includes AI-NF logic circuitry430-434and an AI-NF local inventory circuitry440-444for the respective edge server circuitry or platform210,220,420. The example edge server circuitry210,220,420leverages its AI-NF logic circuitry430-434to provide access to the AI-NF infrastructure circuitry410and expose AI model instances stored in the AI-NF local inventory circuitry440-444as discoverable by the AI-NF infrastructure circuitry410, for example.

As shown in the example ofFIG. 4, an edge service405can request that the AI-NF logic circuitry430execute a given AI model according to a set of parameters or requirements. The request for the AI model from the example service405can include information such as an AI-NF inference with a named model, accuracy threshold/requirement, recall, latency/time, conference vector, local resource availability, previously requested model identifier, etc. Results of the AI-NF inference can be used to examine or search the AI-NF local inventory circuitry440of the example edge server circuitry210. The AI-NF inference can also be used to query AI-NF model discovery and offering circuitry450of the AI-NF infrastructure circuitry410. The AI-NF local inventory circuitry440can also provide an AI-NF registry entry to the AI-NF model discovery and offering circuitry450of the example AI-NF infrastructure circuitry410. Results from the AI-NF model discovery and offering circuitry450and/or from the AI-NF local inventory circuitry440-444can be stored in the AI-NF model cache460, for example.

As such, a query or instruction call for execution from the service405can result in a new AI-NF multicast message from the AI-NF infrastructure circuitry410to the AI-NF local inventory circuitry440with a named model, associated accuracy, other parameter, etc. An AI-NF annotated data set including data named type, inferenced annotation, conference vector, etc., can also be provided by the AI-NF infrastructure circuitry410to the AI-NF local inventory circuitry444, for example. One or more of the AI-NF local inventory circuitry440-444can produce set(s) of one or more models470-475including associated parameters/characteristics such as accuracy, latency, recall, etc. As models are added, modified, removed, etc., their location and status can be updated with the AI-NF infrastructure circuitry410.

FIG. 5illustrates an example implementation of the edge computing infrastructure300ofFIG. 4. As shown in the example ofFIG. 5, the example service405queries the AI-NF logic circuitry430of the edge server circuitry210for a particular AI model. For example, the service405can pass an AI-NF inference for a named model with a desired accuracy, recall, end-to-end (E2E) latency, local resource availability, prior usage by the service405, conference vector, etc., to the AI-NF logic circuitry430of the edge server circuitry210. The AI-NF logic circuitry430manages E2E execution of AI-NF models. The example AI-NF logic circuitry430can query the AI-NF local inventory circuitry440for the named model requested by the service405and/or can query the AI-NF model offering and discovery circuitry450of the example AI-NF infrastructure circuitry410to locate the model.

For example, the AI-NF logic circuitry430tracks AI-NF model instances that are available in the AI-NF local inventory circuitry440and registers the models to keep information consistent between the AI-NF local inventory circuitry440and the AI-NF infrastructure circuitry410. Each instance of an AI-NF model can be associated with metadata such as accuracy, recall, latency, tenant provider, and/or other data that can be mapped into an AI-NF model.

In certain examples, the AI-NF logic circuitry430determines a load on the edge server circuitry210and a capacity to accommodate execution of one or more AI model instances by the edge server circuitry210. The example AI-NF logic circuitry430serves as a platform interface that one or more applications can use to request the AI-NF infrastructure410to execute a particular AI-NF Model (e.g., person detection, etc.).

The example AI-NF logic circuitry430manages requests to execute an AI-NF model. As such, the AI-NF logic circuitry430processes a request to execute an AI-NF model according to one or more specified parameters. For example, the AI-NF logic circuitry430allows the service405and/or other application to form a query including information such as AI-Named Function (AI-NF), payload (or a pointer to the payload), restriction, etc. The AI-NF is a global unique identifier (GUID) that identifies the Named Function (e.g., person detection, etc.). The GUID is managed consistently by the AI-NF infrastructure410and is discoverable. The payload or pointer to the payload (e.g., global memory address, etc.) specifies content of the function and/or associated query beyond the name, for example. The one or more restrictions indicate one or more constraints, parameters, settings, etc., associated with execution of the AI-NF model (e.g., end-to-end latency, recall, required accuracy for the selected model etc.). The AI-NF logic circuitry430can provide available compute capabilities including a list of accelerators and/or other computing circuitry to execute the designed model, for example.

As illustrated inFIG. 5, the example AI-NF model offering and discovery circuitry450includes an interface circuitry510. The example interface circuitry510accepts a query (e.g., a query for an AI model from the AI-NF logic circuitry430, etc.) and/or a response (e.g., providing an AI model from the AI-NF local inventory440, etc.) from the example edge server circuitry210and can route a response (e.g., providing an AI model instance, etc.) to the edge server circuitry210. For example, the AI-NF logic circuitry430can query for an AI model via the interface circuitry510, and, in response to the query, the AI-NF local inventory440can receive an AI-NF annotated data set including data named type, inferenced annotation, conference vector, etc.

The example AI-NF infrastructure circuitry410processes the query from the AI-NF logic circuitry430to identify and facilitate execution of an AI model (e.g., an AI-NF model, etc.) to return an outcome/result to the service405. The AI-NF infrastructure circuitry410can identify a local edge server circuitry210,220,420, a cloud circuitry230-240, a portion of the infrastructure circuitry410, etc., for execution of an instance of an AI-NF model, for example.

In certain examples, the AI-NF logic circuitry430from the example edge server circuitry210periodically sends results of AI/AI-NF model instances executing on the edge server circuitry210to the AI-NF infrastructure circuitry410. Results can be used for model training, naming of AI-NF models, and/or association of data type(s) with AI model inferences, for example. Results sent to the AI-NF infrastructure circuitry410may be selected by the AI-NF logic circuitry430based on one or more criterion including a level of confidence associated with the generated prediction, sensitivity (or lack of sensitivity) of the data, data generation condition, etc. In certain examples, the AI-NF logic circuitry430instead creates a new model on the edge server circuitry210.

As shown in the example ofFIG. 5, the AI-NF infrastructure circuitry410includes an example interface510, which facilitates interaction between the infrastructure circuitry410and one or more circuits such as the edge server circuitry210,220,420, etc. The example AI-NF infrastructure circuitry410includes AI-NF model offering circuitry520, network and edge appliance telemetry circuitry530, AI-NF execution logic circuitry540, model inventory circuitry550, AI-NF model discovery circuitry560, and one more training entities570, in addition to the AI-NF model cache460, for example. The example AI-NF model offering circuitry520provides one or more AI models (e.g., to the example AI-NF local inventory440, etc.) in response to a query. The example AI-NF model discovery circuitry560processes a query to identify such model(s).

The example AI-NF model inventory circuitry550manages an inventor of AI instances for AI named models that are hosted in the various appliances210-240,420, etc., that belong to the example edge infrastructure300. Each named model instance (e.g., speech to text NN,) has a set of meta-data fields that defines characteristics of the respective model instance (e.g., accuracy, recall, latency, etc.). Each named model instance can be tagged with other data that can be used for data privacy, management, etc., such as general data protection regulation (GDPR, data sovereignty, etc., that can be used by the AI-NF infrastructure circuitry410to determine which instance(s) can be used and where such instance(s) can be used. Contextual information can be used to apply one or more policies to AI model instances, for example.

The example AI-NF infrastructure circuitry410includes a cache460of AI models as well named instances that may not be available in any edge appliance210-220,420but that have been generated over the lifetime of the edge infrastructure300. Contextual data can be stored with the models in the cache460for rapid filtering, for example.

The example AI-NF execution logic circuitry540provides infrastructure support to execute and/or route an AI-NF model instance. Similar to the AI-NF logic circuitry430, the AI-NF execution logic circuitry540provides an interface that can be called in order to manage an AI-NF model execution, for example. Parameters associated with the example AI-NF execution logic circuitry540can include: AI-Named Function, payload/point, restriction, etc. As described in connection with the example AI-NF logic circuitry430, the AI-NF is a GUID that identifies an associated named function (e.g., person detection, etc.). The payload or pointer to the payload (e.g., a global memory address, etc.) provides a location of a model/model data in memory. Restriction(s) can be associated with AI-NF execution (e.g., E2E latency, model accuracy, recall, etc.).

The AI-NF execution logic circuitry540can include circuitry programmed with logic (e.g., instructions, gates, other circuitry, etc.) to implement an interface that can filter to determine which edge appliances (e.g., the example edge server circuitry210,220,420, etc.) satisfy provided requirements, for example. For example, the AI-NF execution logic circuitry540can drive (or help drive) the example interface510. The interface510can filter edge appliances for AI model instances that satisfy meta-data requirements that are not latency- or compute-related (e.g., accuracy, recall, privacy, ownership, proprietary access, etc.), a prescribed E2E latency limit, network latency, compute capacity, etc. For example, network latency can be estimated using telemetry data captured by the network and edge appliance telemetry circuitry530(e.g., network telemetry information, edge appliance telemetry information, etc.), historical data on jitter, etc. Compute capacity can be determined for a selected AI model instance and can be based on a current processing load and estimated latency.

For example, telemetry data (e.g., real-time or substantially real-time telemetry data, etc.) from one or more computing elements (e.g., processors, accelerators, etc.) capable of executing a particular model or function (e.g., obstacle detection, person detection, road segmentation, etc.) can be gathered with associated hardware properties to identify and prioritize AI model instances based on one or more criterion such as latency, accuracy, recall, throughput, power, cost, ownership, prior usage, etc. For example, a plurality of NN models for video analytics may be available but behave differently with different resources and different loads. Different NN models to implement the same function in different ways may provide a trade-off between accuracy and latency, for example. Different hardware available to execute different models may also provide trade-off(s) between latency, power, throughput, etc. For example, different types of hardware have different latency behavior depending on the associated load. An FPGA provides a constant latency to perform a model inference while the latency for an accelerator depends on its associated load (e.g., latency increases exponentially with load, etc.). As such, telemetry information (e.g., related to the network, one or more edge servers/appliances, other infrastructure telemetry information, etc.) can be used as part of a model query to identify an available model instance to select. Telemetry information can be used to organize models in the example table350according to use case, type of function, accuracy, recall, latency, etc. Such information can also be stored as meta-data associated with the respective model, for example. In certain examples, proprietary or limited access can also be identified with respect to certain models to limit and/or encourage their usage depending the requestor, circumstances, etc.

The AI-NF execution logic circuitry540can leverage the telemetry circuitry530to determine whether one or more models available in the table350satisfy requirements and/or other parameters provided in a query for a model/model type. If no AI model instance satisfies the requirements, the AI-NF execution logic circuitry540may perform a lookup internally in the AI-NF model cache460. A selected model instance can be returned to the example edge server circuitry210via the interface510, the AI-NF model offering circuitry520, etc., for execution according to one or more provided functional requirements, etc.

In certain examples, the AI-NF model discovery circuitry560processes annotated inferences and associated confidence level/score from the edge nodes210-240,420, etc. The AI-NF model discovery circuitry560uses named annotated data sets (e.g., per domain, per type of model, etc.) to evaluate model variants (e.g., variants of NN topologies, etc.) with different characteristics (e.g., changing size of layers, introducing more CONV layers, etc.) to discover new models with new properties. Once new models are discovered, the models may be announced to the edge appliances and/or stored locally.

In certain examples, the AI-NF infrastructure circuitry410includes a list or other set of training entities570that can be used to explore mutation of AI models. For example, one or more of the training entities570can include models executable to search model inventories, caches, other storage, etc., to inference and identify one or more models and associated properties, characteristics, configuration, etc. Such model(s) can be referred to as query models or search models, for example. Once new models are identified with new properties (e.g., better accuracy, better performance/watt, etc.), the AI-NF infrastructure circuitry410may advertise the model to edge nodes210-240,420in the architecture300. Identified model(s) can be stored in the model cache460and indexed in the associated model inventory circuitry550.

As shown in the example ofFIG. 5, the model inventory circuitry550can storage identified model information in the example data structure or table350. The example table350, stored by the model inventory circuitry550, in the model cache460, and/or in another memory, for example, can organize a plurality of AI models by identifier (ID) (e.g., numeric identifier, alphanumeric identifier, code, etc.), name, provider/source (e.g., name, address, etc.), meta-data (e.g., accuracy, recall, latency, etc.), etc. As shown in the example ofFIG. 5, the table350can store an identifier OX2for a road-segmentation AI model at an Internet Protocol (IP) and/or media access control (MAC) address. The example model has certain accuracy, latency, recall, etc., indicated in meta-data stored in the table350and associated with the model.

In certain examples, hierarchical caching can be used to populate the table350and/or store associated model(s) in the model cache460. Meta-data and/or a profile definition associated with an AI model (e.g., an AI-NF model, etc.) can be used to identify, classify, and store the AI model in the table350, the cache460, etc. For example, hierarchical caching and/or other caching mechanism can be used to organize and store models and associated meta-data in the model cache460. Meta-data stored in the hierarchical cache can be used to determine whether or not a model is a match or fit for a request/query, for example. Information such as a key performance indicator (KPI), service level agreement (SLA), etc., can be used to help ensure that a selected model not only satisfies the request from the requestor (e.g., the service405, etc.) but satisfies the request within parameters provided such as speed, latency, accuracy, recall, precision, allowed error, quality, etc. Meta-data, parameters, etc., can be used to define layers or levels in a hierarchy of the example cache460, which, in some examples, can be scalable (e.g., based on available size, number of queries, variety of models, variety of edge devices, setting, etc.).

In certain examples, an evolution of a given AI model can be tracked across one or more edge nodes and stored in the table350and/or the associated model cache460. The meta-data allows the model to be tracked as it evolves and stored based on name. The history and evolution of the model can be logged and made searchable according to model name (e.g., an AI-NF model).

In certain examples, before a model is used, added to the table350, etc., the model can be validated. An attestation can be associated with the model to enable that model to be stored, tracked, shared, etc. For example, a test inference of the model can be evaluated against a threshold or score to attest to the validity of the model.

In certain examples, a distributed ledger, such as a blockchain, can be used to track evolution of an attested model over time. Such a distributed ledger can be used in conjunction with a vehicle network, for example, to make a model accessible/executable to one or more edge vehicle systems260in communication with the example infrastructure300(e.g., as part of a vehicle-to-everything (V2X) network, etc.). For example, one or more edge devices (e.g., connected vehicle systems, etc.)260may attest a model and coordinate via the distributed ledger to sign or validate a block or entry in the ledger to enable access to execute an instance of the model. In some examples, a model may be customized, proprietary, or otherwise tailored to a vehicle (e.g., a BMW® model, an Audi® model, etc.). In some examples, vehicle-specific models can be organized in the ledger according to type. In some examples, a hybrid model can be organized with a general portion and a vehicle-specific portion, etc.

In certain examples, the table350, alone or in combination with the model inventory circuitry550and the AI-NF model cache460, can serve as a new model registry. Logic circuitry430of the edge server circuitry210,220,420can interact with the AI-NF model discovery circuitry560to register models with the infrastructure circuitry410. Using the example model inventory circuitry550and the model table350, the infrastructure410can be made aware of a model, its function, and meta-data such as accuracy (e.g., percentage, etc.), latency (e.g., milliseconds, etc.), recall (e.g., a number of relevant elements detected (e.g., true positives divided by a number of relevant elements), etc.), etc. The infrastructure410can then support and provide the model, which can be organized individually, grouped with others of the same type, linked to prior/other instances of the same model, based on access/authorization, etc. If the edge server circuitry210,220,420removes a model, the logical circuitry430also interacts with the model inventory circuitry550to delete an entry for the removed model from the table350, etc. By providing tags, meta-data, etc., the table350enables the AI-NF infrastructure circuitry410to automatically apply one or more policies to identify, manage, and deploy models for execution. The table350and associated meta-data allows the example AI-NF logic circuitry430to estimate wait times, monitor/manage deployed model(s), etc. Such functionality is important when a large number of users are connected to the edge infrastructure300with a large number of models deployed for use.

As such, the example AI-NF infrastructure circuitry410acts as a switch to connect the requesting service405and/or one or more external devices260to a model for execution/inference with respect to a particular instance of the model stored on the infrastructure circuitry410or on a connected edge server circuitry210,220,420, cloud circuitry230,240, etc. In certain examples, rather than employing services to execute AI model inferencing in a particular way, the example AI-NF infrastructure circuitry410and its interface510provide a switch to dynamically identify and execute a particular model. When the service405requests to perform a particular inference on a particular model with a particular payload, the infrastructure circuitry410translates and connects the service405(e.g., via the edge server circuitry210, etc.) to the platform or accelerator hosting the model to perform the inferencing.

For example, the service405sends a request to the edge server circuitry210. The request includes a model or model type as well as certain requirements or parameters (e.g., requesting a pedestrian detection model with an accuracy of at least 80% and a response time of no longer than 10 milliseconds, etc.). In certain examples, the request can include information regarding prior usage (e.g., name, location, etc.). In other examples, the request does not include prior usage information. The AI-NF logic circuitry430of the edge server circuitry210can search locally in its AI-NF local inventory440to determine whether the local inventor440holds a model that satisfies the specified constraints. The AI-NF logic circuitry430can also communicate with the AI-NF infrastructure410to provide the model and associated requirement(s) via the interface circuitry510. The AI-NF model offering circuitry520and/or the AI-NF model discovery circuitry560identifies a model instance (e.g., using the AI-NF model inventory circuitry550and/or the model table350, etc.) that satisfies the requirements or constraints specified by the service405and where that model is located (e.g., hosted by the AI-NF infrastructure circuitry410, located at one of the edge server circuitry210,220,420, hosted in the cloud circuitry230,240, etc.). The query can be based on name, a description of the model function, another identifier, etc. The AI-NF infrastructure circuitry410connects the requesting service405to the circuitry hosting the selected model for inferencing/execution. The model can be uni-cast to a single requesting service405, multi-cast to multiple requesting/related service405, device(s)260, etc., for example.

While pedestrian detection was used as an example above, the example system300can be applied to a variety of AI models (named or otherwise identified), associated functions, etc. For example, other video analytics models can be organized, identified, and deployed using the example infrastructure300. The example infrastructure300can organize and deploy models related to defect detection (e.g., sensor data to be analyzed to detect an anomaly, etc.), factory floor analysis, robot control (e.g., to identify an object and that object's role or function, etc.), smart transportation, retail, other edge-based learning, etc.

FIG. 6illustrates an example implementation of the AI-NF logic circuitry430. A similar configuration can be used to implement the example AI-NF logic circuitry432,434, and/or the example AI-NF execution logic540, for example. The implementation of the AI-NF logic circuitry430is provided in connection withFIG. 6for purposes of illustration only. As shown in the example ofFIG. 6, the AI-NF logic circuitry430includes query processing circuitry610, comparison circuitry620, selection circuitry630, inference engine circuitry640, and output circuitry650.

The example query processing circuitry610processes an incoming query or request to locate an AI model corresponding to an identifier, a name, a specified function, etc. The query processing circuitry610leverages information provided in the request including one or more requirements, constraints, and/or parameters provided in association with the model/function request. For example, the request may include a required minimum accuracy, recall, latency/responsiveness, etc., that factors into which model instance, among a plurality of the same or similar AI models, can perform the function and satisfy the constraints specified in the request.

The example comparison circuitry620compares and/or otherwise evaluates available AI models (e.g., that appear to satisfy the one or more requirements, constraints, parameters, etc., specified in the model request. The comparison circuitry620generates a comparison or comparative output from an evaluation of two or more models from the example AI-NF local inventory circuitry440, the model inventory circuitry550, the AI-NF model cache460, and/or the model table350, etc. The comparison circuitry620can compare meta-data associated with models, leverage the inference engine circuitry640to evaluate model output, and/or otherwise evaluate two or more models to provide a quantitative comparison output (e.g., a score or other number, etc.), a qualitative comparison output (e.g., which model satisfies more requirements and/or better satisfies requirements, etc.), etc., for the selector circuitry630to select one of the AI models evaluated by the comparison circuitry620.

The example selector circuitry630can trigger the output circuitry to provide a selected AI model instance (e.g., an AI-NF model instance, etc.) to a requestor (e.g., the service405, an edge device260, the edge server circuitry210,220,420, etc.). Alternatively or additionally, the selector circuitry630can trigger the inference engine circuitry640to execute or inference the selected model and provide an output to the output circuitry650for output to the requestor. The output circuitry650can output an identification of the selected AI model, deploy a selected AI model instance, providing a result of model inference/execution, etc.

Thus, the example AI-NF logic circuitry430can facilitate name-based and/or other identifier-based model searching and sharing across the edge computing infrastructure300. The example AI-NF infrastructure circuitry410facilitates model searching and sharing among multiple edge server circuitry210,220,420, cloud circuitry230,240, connected devices260, etc. The example model table350is populated by the AI-NF infrastructure circuitry410in communication with the edge server circuitry210,220,420, etc., and organizes available AI models for query by the AI-NF logic circuitry430-434on the edge server circuitry210,220,420via the AI-NF infrastructure circuitry410, for example.

While example implementations of the example cloud infrastructure110, the example edge computing infrastructure120, the example edge devices160-164,260, the example edge server circuitry210,220,420, the example cloud circuitry230-240, the example network250, the example interfaces310-340,360, the example AI-NF infrastructure circuitry410, and/or, more generally, the example edge computing infrastructure300are illustrated inFIGS. 1-6, one or more of the elements, processes and/or devices illustrated inFIGS. 1-6can be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the example cloud infrastructure110, the example edge computing infrastructure120, the example edge devices160-164,260, the example edge server circuitry210,220,420, the example cloud circuitry230-240, the example network250, the example interfaces310-340,360, the example AI-NF infrastructure circuitry410, and/or, more generally, the example edge computing infrastructure300can be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the example cloud infrastructure110, the example edge computing infrastructure120, the example edge devices160-164,260, the example edge server circuitry210,220,420, the example cloud circuitry230-240, the example network250, the example interfaces310-340,360, the example AI-NF infrastructure circuitry410, and/or, more generally, the example edge computing infrastructure300can be implemented by one or more analog or digital circuit(s), logic circuits, programmable processor(s), programmable controller(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)). 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 cloud infrastructure110, the example edge computing infrastructure120, the example edge devices160-164,260, the example edge server circuitry210,220,420, the example cloud circuitry230-240, the example network250, the example interfaces310-340,360, the example AI-NF infrastructure circuitry410, and/or, more generally, the example edge computing infrastructure300is/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 computing architectures/apparatus300ofFIGS. 1-6can include one or more elements, processes and/or devices in addition to, or instead of, those illustrated inFIGS. 1-6, and/or can include more than one of any or all of the illustrated elements, processes and devices. As used herein, the phrase “in communication,” including variations thereof, encompasses direct communication and/or indirect communication through one or more intermediary components, and does not require direct physical (e.g., wired) communication and/or constant communication, but rather additionally includes selective communication at periodic intervals, scheduled intervals, aperiodic intervals, and/or one-time events.

In certain examples, a model data structure can be implemented using one or more of the example table350, a distributed ledger, the example model cache460, etc.

In certain examples, model inventory circuitry can be implemented using one or more of example inventory circuitry440-444,550, etc. In certain examples, model discovery circuitry can be implemented using one or more of the example model offering and discovery circuitry450, the example model offering circuitry520, the example model discovery circuitry560, etc.

In certain examples, execution logic circuitry can be implemented using one or more of the example AI-NF logic circuitry430-434, the example AI-NF execution logic circuitry550, etc.

In certain examples, other elements of the example infrastructure circuitry410help to implement one or more of these circuitry.

In certain examples, means for managing a model data structure can be implemented using one or more of the example inventory circuitry440-444,550, etc.

In certain examples, means for processing a query can be implemented using one or more of the example AI-NF logic circuitry430-434, the example AI-NF execution logic circuitry550, the example model offering and discovery circuitry450, the example model offering circuitry520, the example model discovery circuitry560, etc.

In certain examples, means for outputting can be implemented using one or more of the example AI-NF logic circuitry430-434, the example AI-NF execution logic circuitry550, etc.

In certain examples, means for identifying can be implemented using one or more of the example inventory circuitry440-444,550, the example model offering and discovery circuitry450, the example model offering circuitry520, the example model discovery circuitry560, etc.

In certain examples, means for processing can be implemented using one or more of the example AI-NF logic circuitry430-434, the example AI-NF execution logic circuitry550, the example model offering and discovery circuitry450, the example model offering circuitry520, the example model discovery circuitry560, etc.

Flowcharts representative of example hardware logic, machine readable instructions, hardware implemented state machines, and/or any combination thereof for implementing the example cloud infrastructure110, the example edge computing infrastructure120, the example edge devices160-164,260, the example edge server circuitry210,220,420, the example cloud circuitry230-240, the example network250, the example interfaces310-340,360, the example AI-NF infrastructure circuitry410, and/or, more generally, the example edge computing infrastructure300is/are shown inFIGS. 7-9. The machine readable instructions can be one or more executable programs or portion(s) of an executable program for execution by a computer processor and/or processor circuitry, such as the processor1012shown in the example processor platform1000discussed below in connection withFIG. 10. The program may be embodied in software stored on a non-transitory computer readable storage medium such as a CD-ROM, a floppy disk, a hard drive, a DVD, a Blu-ray disk, or a memory/memory circuitry associated with the processor1012, but the entire program and/or parts thereof could alternatively be executed by a device other than the processor1012and/or embodied in firmware or dedicated hardware. The instructions stored on the non-transitory computer readable storage medium can cause at least one processor to perform at least one action or operation, implement at least one function, etc. Further, although the example program is described with reference to the flowcharts illustrated inFIGS. 7-9, many other methods of implementing the example cloud infrastructure110, the example edge computing infrastructure120, the example edge devices160-164,260, the example edge server circuitry210,220,420, the example cloud circuitry230-240, the example network250, the example interfaces310-340,360, the example AI-NF infrastructure circuitry410, and/or, more generally, the example edge computing infrastructure300can alternatively be used. For example, the order of execution of the blocks can be changed, and/or some of the blocks described can be changed, eliminated, or combined. Additionally or alternatively, any or all of the blocks can be implemented by one or more hardware circuits (e.g., discrete and/or integrated analog and/or digital circuitry, an FPGA, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware. The processor circuitry can be distributed in different network locations and/or local to one or more devices (e.g., a multi-core processor in a single machine, multiple processors distributed across a server rack, etc.).

FIG. 7is a flowchart representative of example machine-readable instructions that can be executed to implement the example cloud infrastructure110, the example edge computing infrastructure120, the example edge devices160-164,260, the example edge server circuitry210,220,420, the example cloud circuitry230-240, the example network250, the example interfaces310-340,360, the example AI-NF infrastructure circuitry410, and/or, more generally, the example edge computing infrastructure300ofFIGS. 1-6. The example process700of the illustrated example ofFIG. 7begins with formation and/or update of the model table and/or other data structure350. (Block710). The example table350can be generated by querying the local inventory circuitry440-444of each connected edge server circuitry210,220,420, querying the AI-NF logic circuitry430-434of each connected edge server circuitry210,220,420, querying each connected cloud circuitry230-240, triggering the AI-NF model discovery circuitry560, etc. The table350and the model inventory circuitry550can be generated and/or updated based on the query results. The model inventory circuitry550can drive the query and maintain the table, for example, which can be initiated by the AI-NF logic circuitry430-434(e.g., in response to a query or change at the edge server circuitry210,220,420, etc.), the AI-NF model discovery circuitry560(e.g., in response to an initialization, a request from the edge server circuitry210,220,420, etc.), etc.). The table350identifies available models by identifier, name, type, source, and associated meta-data, for example. Meta-data can be used to specify characteristics of the model and its source or host such as accuracy, recall, latency, power, etc. The AI-NF local inventory circuitry440and/or the model inventory circuitry550can also be updated based on the content of the table350, for example.

In certain examples, AI models can be organized in a blockchain or other distributed ledger instead of or in addition to the example table350. Using the blockchain/distributed ledger provides a level of attestation to track the status and evolution of models in the blockchain or other ledger.

The table350can be queried such as by the AI-NF logic circuitry430-434, the AI-NF model discovery circuitry560, the AI-NF model offering circuitry520, the AI-NF execution logic circuitry540, etc. (Block720). For example, in response to a request for a model or associated function from the service405, the AI-NF logic circuitry430of the edge server circuitry210,220,420initiates a query of the model table350via the interface510of the AI-NF infrastructure circuitry410. The AI-NF logic circuitry430-434can also query its local AI-NF inventory circuitry440-444and/or the model inventory circuitry550, the model cache460, etc., in response to a request from the service405. One or more edge devices260can also initiate a query via the AI-NF infrastructure circuitry410as well. The query identifies one or more models from the table350, cache460, etc., for comparison to one or more requirements/criterion/constraints provided as part of the query. The comparison (e.g., by the AI-NF logic circuitry430-434, the AI-NF execution logic circuitry540, the AI-NF model offering circuitry520, and/or the AI-NF model discovery circuitry560generates a ranking of models with respect to the one or more requirements/criterion/constraints and/or with respect to each other, a score and/or confidence level associated with each model with respect to the one or more requirements/criterion/constraints and/or with respect to each other, a binary indication of satisfactory or unsatisfactory with respect to the one or more requirements/criterion/constraints, etc. As such, the logic circuitry430-434,540, etc., and/or other circuitry of the example infrastructure410can evaluate one or more available models with respect to specified requirements/criterion/constraints/etc., and/or with respect to each other based on processing of associated meta-data, inferencing of the model(s), etc. For example, the query processing circuitry610of the example AI-NF logic circuitry430can break down the query and identify applicable model(s) and associated meta-data, output, etc.

The comparative query results are then evaluated for a best fit based on the one or more requirements, criteria, constraints, etc. (Block730). For example, the comparison circuitry620of the example AI-NF logic circuitry430compares output of the identified models, scores and/or confidence level associated with each of the identified models, other indications provided by the query processing circuitry610. The selection circuitry630selects a model that is determined to be a best fit to the query based on results from the comparison circuitry620, for example.

The selected AI model (or an instance of the selected AI model) is then made available to the requestor. (Block740). For example, the output circuitry640of the example AI-NF logic circuitry430deploys an instance of the selected model to the requestor and/or makes the selected model available for execution to provide an output/outcome to the requestor. In certain examples, the selected AI model (e.g., an AI-NF model, etc.) is stored locally at the edge server circuitry210,220,420. In other examples, the AI-NF infrastructure410enables a requestor at one edge server circuitry210to be connected to a model identified at another edge server circuitry220via the table350and associated model inventory circuitry550.

FIG. 8is a flowchart providing further example detail regarding populating the model data structure, such as described above in connection with Block710of the example ofFIG. 7. The example table or data structure350can be built and/or otherwise maintained by polling and/or otherwise gathering information from connected servers and/or other devices to populate the table350. (Block810). For example, each of the edge server circuitry210,220,420can be polled and/or otherwise queried to identify model(s) stored in their local inventory circuitry440-444. Cloud circuitry230,240can also be queried to identify model(s) stored in the cloud. The model inventory circuitry550of the AI-NF infrastructure410can also be polled and/or indexed to identify model(s) stored in the model cache460, etc., for example. Polling and/or indexing can also identify models that have been changed, removed, etc. In certain examples, a publish/subscribe model can be established with connected circuitry such that the edge server circuitry210,220,420messages the model discovery circuitry560and/or the model offering circuitry420to notify the AI-NF infrastructure410of a change such as an added model, a removed model, a changed model, etc.

Gathered model information is then evaluated to determine whether one or more models is to be added to the table350, removed from the table350, or modified in the table350. (Block820). When a model has been identified to be added to the table350, an entry associated with the model is added to the table data structure350. (Block830). For example, information such as a model name (e.g., AI-NF, etc.), identifier (e.g., a numeric or alphanumeric identifier, etc.), type, source/provider, characteristics or meta-data, etc., can be added as an entry to the table data structure350. In certain examples, an associated model instance may be copied to the AI-NF model cache460.

When a model in the table350is to be removed (e.g., because that model is no longer stored in the edge server circuitry210,220,420, etc.), then the entry associated with that model in the table350is removed. (Block840). In certain examples, if the model is stored in the AI-NF model cache460, that model can be purged or otherwise removed from the cache460.

When a model already identified in the table350is to be updated, then the entry associated with that model is updated based on reported change(s). (Block850). For example, one or more of the model name (e.g., AI-NF, etc.), identifier (e.g., a numeric or alphanumeric identifier, etc.), type, source/provider, characteristics or meta-data, etc., can be updated in the entry associated with the model in the table data structure350. In certain examples, an associated model instance may be updated in the AI-NF model cache460.

The gathered model information is evaluated to determine whether further changes to the table350(e.g., add, remove, update, etc.) are to be made. (Block860). If further changes are to be made based on the gathered information (e.g., one or more models to add, remove, and/or update), then control reverts to Block820to process the information and trigger a next action with respect to the table350. If no further changes are to be made, then the data model table350is made available for query. (Block870).

FIG. 9is a flowchart providing further example detail regarding processing a query for a model or function, such as described above in connection with Block720of the example ofFIG. 7. For example, a query is received by the edge server circuitry210from a service405and is evaluated to determine content of the query. (Block910). For example, the AI-NF logic circuitry430processes the request/query to determine whether the request is for a named or otherwise identified model, for a function, etc. The AI-NF logic circuitry430transforms the request into a query for the associated model resource. The AI-NF logic circuitry430can first search locally (e.g., in the AI-NF local inventory440, etc.) to determine whether it holds a model that satisfies the request. (Block920). If a local search is to be performed, for example, the AI-NF logic circuitry430determines whether one or more models in the AI-NF local inventory circuitry440correspond to the requested model name and/or function and also satisfy (e.g., based on an evaluation of meta-data) one or more requirements, constraints, criterion, other parameters, etc., specified in the request (e.g., accuracy, recall, latency, etc.). (Block930).

Then, a query to the AI-NF infrastructure circuitry410is performed. (Block940). For example, the table350can be queried such as by the AI-NF logic circuitry430-434, the AI-NF model discovery circuitry560, the AI-NF model offering circuitry520, the AI-NF execution logic circuitry540, etc., to identify one or more models from the table350, cache460, etc., for comparison to one or more requirements/criterion/constraints provided as part of the query.

Search results from the local and/or infrastructure searches can be compared with respect to each other and with respect to the one or more requirements, constraints, criterion, other parameters, etc., specified in the request (e.g., accuracy, recall, latency, etc.). (Block950). The comparison (e.g., by the AI-NF logic circuitry430-434, the AI-NF execution logic circuitry540, the AI-NF model offering circuitry520, and/or the AI-NF model discovery circuitry560generates a ranking of models with respect to the one or more requirements/criterion/constraints and/or with respect to each other, a score and/or confidence level associated with each model with respect to the one or more requirements/criterion/constraints and/or with respect to each other, a binary indication of satisfactory or unsatisfactory with respect to the one or more requirements/criterion/constraints, etc. As such, the logic circuitry430-434,540, etc., and/or other circuitry of the example infrastructure410can evaluate one or more available models with respect to specified requirements/criterion/constraints/etc., and/or with respect to each other based on processing of associated meta-data, inferencing of the model(s), etc. For example, the query processing circuitry610of the example AI-NF logic circuitry430can break down the query and identify applicable model(s) and associated meta-data, output, etc. Result(s) of the comparison can be returned to enable selection of a best fit model, etc. (Block960).

Thus, certain examples facilitate name-based model and/or function searching across multiple circuits in an edge infrastructure. The example infrastructure enables coordination, consistency, and access across multiple edge servers, cloud servers, and connected edge devices, for example. Certain examples generate a table data structure that is populated by the infrastructure in communication with the edge servers, etc., and organizes available AI models for query by logic on the edge server, etc., via the infrastructure.

FIG. 10is a block diagram of an example processor platform1100structured to execute the instructions ofFIGS. 7-9to implement the example cloud infrastructure110, the example edge computing infrastructure120, the example edge devices160-164,260, the example edge server circuitry210,220,420, the example cloud circuitry230-240, the example network250, the example interfaces310-340,360, the example AI-NF infrastructure circuitry410, and/or, more generally, the example edge computing infrastructure300ofFIGS. 1-6. The processor platform1000can 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™), an Internet appliance, a gaming console, a headset or other wearable device, or other type of computing device.

The processor platform1000of the illustrated example includes a processor1012. The processor1012of the illustrated example is hardware. For example, the processor1012can be implemented by one or more integrated circuits, logic circuits, microprocessors, GPUs, DSPs, or controllers from any desired family or manufacturer. The hardware processor may be a semiconductor based (e.g., silicon based) device. In this example, the processor1012implements the example AI-NF infrastructure circuitry410. The example processor1012can similarly implement the example edge server circuitry210,220,420, the example cloud circuitry230,240, the example edge device260, etc.

The processor1012of the illustrated example includes a local memory1013(e.g., a cache). The processor1012of the illustrated example is in communication with a main memory including a volatile memory1014and a non-volatile memory1016via a bus1018. The volatile memory1014can 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 random access memory device. The non-volatile memory1016can be implemented by flash memory and/or any other desired type of memory device. Access to the main memory1014,1016is controlled by a memory controller.

The processor platform1000of the illustrated example also includes an interface circuit1020. The interface circuit1020can be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), a Bluetooth® interface, a near field communication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices1022are connected to the interface circuit1020. The input device(s)1022permit(s) a user to enter data and/or commands into the processor1012. The input device(s) can 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, isopoint and/or a voice recognition system.

The processor platform1000of the illustrated example also includes one or more mass storage devices1028for storing software and/or data. Examples of such mass storage devices1028include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, redundant array of independent disks (RAID) systems, and digital versatile disk (DVD) drives.

The machine executable instructions1032ofFIGS. 7-9can be stored in the mass storage device1028, in the volatile memory1014, in the non-volatile memory1016, and/or on a removable non-transitory computer readable storage medium such as a CD or DVD.

In certain examples, the above example computing apparatus300, etc., ofFIGS. 1-6can be implemented on an edge node of an edge network. The edge node, for example, is a computing endpoint that is deployed by an edge computing environment such as a base station that has a computing rack integrated into the base of a tower or a regional office that may have server racks as well as a mobile edge node such as a cell phone, smart automobile, drone etc. In an IoT network, mobile edge nodes can be considered as part of the IoT network as well as part of an edge network. IoT and industrial internet of things (IIoT) nodes may include stationary IoT nodes including an IoT network in which the main focus of the IoT node is to host a particular type of sensing technology or cyber-physical automation technology such as factory automation, robotics, autonomics, etc.

In certain examples, chiplets can be composed in various combinations in ASICs, FPGA, SoC, etc., on an IoT or other edge node to provide flexible configuration within a chiplet layout geometry. Security attestation and access regulation can then be dynamically determined based on configuration, task, other usage, location, etc.

FIG. 11is a block diagram of an example implementation of the processor circuitry1000ofFIG. 10. In this example, the processor circuitry1000ofFIG. 10is implemented by a microprocessor1100. For example, the microprocessor1100may implement multi-core hardware circuitry such as a CPU, a DSP, a GPU, an XPU, etc. Although it may include any number of example cores1102(e.g.,1core), the microprocessor1100of this example is a multi-core semiconductor device including N cores. The cores1102of the microprocessor1100may 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 cores1102or may be executed by multiple ones of the cores1102at 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 cores1102. The software program may correspond to a portion or all of the machine readable instructions and/or operations represented by the flowcharts ofFIGS. 7-9.

The cores1102may communicate by an example bus1104. In some examples, the bus1104may implement a communication bus to effectuate communication associated with one(s) of the cores1102. For example, the bus1104may 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 bus1104may implement any other type of computing or electrical bus. The cores1102may obtain data, instructions, and/or signals from one or more external devices by example interface circuitry1106. The cores1102may output data, instructions, and/or signals to the one or more external devices by the interface circuitry1106. Although the cores1102of this example include example local memory1120(e.g., Level 1 (L1) cache that may be split into an L1 data cache and an L1 instruction cache), the microprocessor1100also includes example shared memory1110that 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 memory1110. The local memory1120of each of the cores1102and the shared memory1110may be part of a hierarchy of storage devices including multiple levels of cache memory and the main memory (e.g., the main memory1014,1016ofFIG. 10). 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 core1102may be referred to as a CPU, DSP, GPU, etc., or any other type of hardware circuitry. Each core1102includes control unit circuitry1114, arithmetic and logic (AL) circuitry (sometimes referred to as an ALU)1116, a plurality of registers1118, the L1 cache1120, and an example bus1122. Other structures may be present. For example, each core1102may 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 circuitry1114includes semiconductor-based circuits structured to control (e.g., coordinate) data movement within the corresponding core1102. The AL circuitry1116includes semiconductor-based circuits structured to perform one or more mathematic and/or logic operations on the data within the corresponding core1102. The AL circuitry1116of some examples performs integer based operations. In other examples, the AL circuitry1116also performs floating point operations. In yet other examples, the AL circuitry1116may include first AL circuitry that performs integer based operations and second AL circuitry that performs floating point operations. In some examples, the AL circuitry1116may be referred to as an Arithmetic Logic Unit (ALU). The registers1118are semiconductor-based structures to store data and/or instructions such as results of one or more of the operations performed by the AL circuitry1116of the corresponding core1102. For example, the registers1118may 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 registers1118may be arranged in a bank as shown inFIG. 11. Alternatively, the registers1118may be organized in any other arrangement, format, or structure including distributed throughout the core1102to shorten access time. The bus1122may implement at least one of an I2C bus, a SPI bus, a PCI bus, or a PCIe bus.

FIG. 12is a block diagram of another example implementation of the processor circuitry1000ofFIG. 10. In this example, the processor circuitry1012is implemented by FPGA circuitry1200. The FPGA circuitry1200can be used, for example, to perform operations that could otherwise be performed by the example microprocessor1100ofFIG. 11executing corresponding machine readable instructions. However, once configured, the FPGA circuitry1200instantiates 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. 12, the FPGA circuitry1200is 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 circuitry1200ofFIG. 12, includes example input/output (I/O) circuitry1202to obtain and/or output data to/from example configuration circuitry1204and/or external hardware (e.g., external hardware circuitry)1206. For example, the configuration circuitry1204may implement interface circuitry that may obtain machine readable instructions to configure the FPGA circuitry1200, or portion(s) thereof. In some such examples, the configuration circuitry1204may 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 hardware1206may implement the microprocessor1100ofFIG. 11. The FPGA circuitry1200also includes an array of example logic gate circuitry1208, a plurality of example configurable interconnections1210, and example storage circuitry1212. The logic gate circuitry1208and interconnections1210are configurable to instantiate one or more operations that may correspond to at least some of the machine readable instructions ofFIGS. 7-9and/or other desired operations. The logic gate circuitry1208shown inFIG. 12is 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 circuitry1208to enable configuration of the electrical structures and/or the logic gates to form circuits to perform desired operations. The logic gate circuitry1208may include other electrical structures such as look-up tables (LUTs), registers (e.g., flip-flops or latches), multiplexers, etc.

The interconnections1210of 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 circuitry1208to program desired logic circuits.

The storage circuitry1212of the illustrated example is structured to store result(s) of the one or more of the operations performed by corresponding logic gates. The storage circuitry1212may be implemented by registers or the like. In the illustrated example, the storage circuitry1212is distributed amongst the logic gate circuitry1208to facilitate access and increase execution speed.

The example FPGA circuitry1200ofFIG. 12also includes example Dedicated Operations Circuitry1214. In this example, the Dedicated Operations Circuitry1214includes special purpose circuitry1216that may be invoked to implement commonly used functions to avoid the need to program those functions in the field. Examples of such special purpose circuitry1216include 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 circuitry1200may also include example general purpose programmable circuitry1218such as an example CPU1220and/or an example DSP1222. Other general purpose programmable circuitry1218may additionally or alternatively be present such as a GPU, an XPU, etc., that can be programmed to perform other operations.

AlthoughFIGS. 11 and 12illustrate two example implementations of the processor circuitry1012ofFIG. 10, 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 CPU1220ofFIG. 12. Therefore, the processor circuitry1012ofFIG. 10may additionally be implemented by combining the example microprocessor1100ofFIG. 11and the example FPGA circuitry1200ofFIG. 12. In some such hybrid examples, a first portion of the machine readable instructions represented by the flowcharts ofFIGS. 7-9may be executed by one or more of the cores1102ofFIG. 11and a second portion of the machine readable instructions represented by the flowchart ofFIGS. 7-9may be executed by the FPGA circuitry1200ofFIG. 12.

In some examples, the processor circuitry1012ofFIG. 10may be in one or more packages. For example, the processor circuitry1100ofFIG. 11and/or the FPGA circuitry1200ofFIG. 12may be in one or more packages. In some examples, an XPU may be implemented by the processor circuitry1012ofFIG. 10, 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.

FIG. 13is a block diagram1300showing 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 cloud1310is co-located at an edge location, such as an access point or base station1340, a local processing hub1350, or a central office1320, and thus may include multiple entities, devices, and equipment instances. The edge cloud1310is located much closer to the endpoint (consumer and producer) data sources1360(e.g., autonomous vehicles1361, user equipment1362, business and industrial equipment1363, video capture devices1364, drones1365, smart cities and building devices1366, sensors and IoT devices1367, etc.) than the cloud data center1330. Compute, memory, and storage resources which are offered at the edges in the edge cloud1310are critical to providing ultra-low latency response times for services and functions used by the endpoint data sources1360as well as reduce network backhaul traffic from the edge cloud1310toward cloud data center1330thus 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. 14illustrates operational layers among endpoints, an edge cloud, and cloud computing environments. Specifically,FIG. 14depicts examples of computational use cases1405, utilizing the edge cloud1310among multiple illustrative layers of network computing. The layers begin at an endpoint (devices and things) layer1400, which accesses the edge cloud1310to conduct data creation, analysis, and data consumption activities. The edge cloud1310may span multiple network layers, such as an edge devices layer1410having gateways, on-premise servers, or network equipment (nodes1415) located in physically proximate edge systems; a network access layer1420, encompassing base stations, radio processing units, network hubs, regional data centers (DC), or local network equipment (equipment1425); and any equipment, devices, or nodes located therebetween (in layer1412, not illustrated in detail). The network communications within the edge cloud1310and 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 layer1400, under 5 ms at the edge devices layer1410, to even between 10 to 40 ms when communicating with nodes at the network access layer1420. Beyond the edge cloud1310are core network1430and cloud data center1440layers, each with increasing latency (e.g., between 50-60 ms at the core network layer1430, to 100 or more ms at the cloud data center layer). As a result, operations at a core network data center1435or a cloud data center1445, with latencies of at least 50 to 100 ms or more, will not be able to accomplish many time-critical functions of the use cases1405. 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 center1435or a cloud data center1445, 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 cases1405), 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 cases1405). 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 layers1400-1440.

The various use cases1405may 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 cloud1310balance 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).

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 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 cloud1310may provide the ability to serve and respond to multiple applications of the use cases1405(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 (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 cloud1310in 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 cloud1310(network layers1400-1440), 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 cloud1310is formed from network components and functional features operated by and within edge gateway nodes, edge aggregation nodes, or other edge compute nodes among network layers1410-1430. The edge cloud1310thus 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 cloud1310may 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) may also be utilized in place of or in combination with such 3GPP carrier networks.

The network components of the edge cloud1310may be servers, multi-tenant servers, appliance computing devices, and/or any other type of computing devices. For example, the edge cloud1310may 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., EMI, vibration, extreme temperatures), and/or enable submergibility. Example housings may include power circuitry to provide power for stationary and/or portable implementations, such as AC power inputs, DC power inputs, AC/DC or DC/AC 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, 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, 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, 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, LEDs, speakers, I/O ports (e.g., 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. 20B. The edge cloud1310may 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, destroying, 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. 15, various client endpoints1510(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 endpoints1510may obtain network access via a wired broadband network, by exchanging requests and responses1522through an on-premise network system1532. Some client endpoints1510, such as mobile computing devices, may obtain network access via a wireless broadband network, by exchanging requests and responses1524through an access point (e.g., cellular network tower)1534. Some client endpoints1510, such as autonomous vehicles may obtain network access for requests and responses1526via a wireless vehicular network through a street-located network system1536. However, regardless of the type of network access, the TSP may deploy aggregation points1542,1544within the edge cloud1310to aggregate traffic and requests. Thus, within the edge cloud1310, the TSP may deploy various compute and storage resources, such as at edge aggregation nodes1540, to provide requested content. The edge aggregation nodes1540and other systems of the edge cloud1610are connected to a cloud or data center1560, which uses a backhaul network1550to fulfill higher-latency requests from a cloud/data center for websites, applications, database servers, etc. Additional or consolidated instances of the edge aggregation nodes1540and the aggregation points1542,1544, including those deployed on a single server framework, may also be present within the edge cloud1610or other areas of the TSP infrastructure.

FIG. 16illustrates deployment and orchestration for virtualized and container-based edge configurations across an edge computing system operated among multiple edge nodes and multiple tenants (e.g., users, providers) which use such edge nodes. Specifically,FIG. 16depicts coordination of a first edge node1622and a second edge node1624in an edge computing system1600, to fulfill requests and responses for various client endpoints1610(e.g., smart cities/building systems, mobile devices, computing devices, business/logistics systems, industrial systems, etc.), which access various virtual edge instances. Here, the virtual edge instances1632,1634provide edge compute capabilities and processing in an edge cloud, with access to a cloud/data center1640for higher-latency requests for websites, applications, database servers, etc. However, the edge cloud enables coordination of processing among multiple edge nodes for multiple tenants or entities.

In the example ofFIG. 16, these virtual edge instances include: a first virtual edge1632, offered to a first tenant (Tenant 1), which offers a first combination of edge storage, computing, and services; and a second virtual edge1634, offering a second combination of edge storage, computing, and services. The virtual edge instances1632,1634are distributed among the edge nodes1622,1624, and may include scenarios in which a request and response are fulfilled from the same or different edge nodes. The configuration of the edge nodes1622,1624to operate in a distributed yet coordinated fashion occurs based on edge provisioning functions1650. The functionality of the edge nodes1622,1624to provide coordinated operation for applications and services, among multiple tenants, occurs based on orchestration functions1660.

It should be understood that some of the devices in1610are multi-tenant devices where Tenant 1 may function within a tenant1 ‘slice’ while a Tenant 2 may function within a tenant2 slice (and, in further examples, additional or sub-tenants may exist; and each tenant may even be specifically entitled and transactionally tied to a specific set of features all the way day to specific hardware features). A trusted multi-tenant device may further contain a tenant specific cryptographic key such that the combination of key and slice may be considered a “root of trust” (RoT) or tenant specific RoT. A RoT may further be computed dynamically composed using a DICE (Device Identity Composition Engine) architecture such that a single DICE hardware building block may be used to construct layered trusted computing base contexts for layering of device capabilities (such as a Field Programmable Gate Array (FPGA)). The RoT may further be used for a trusted computing context to enable a “fan-out” that is useful for supporting multi-tenancy. Within a multi-tenant environment, the respective edge nodes1622,1624may operate as security feature enforcement points for local resources allocated to multiple tenants per node. Additionally, tenant runtime and application execution (e.g., in instances1632,1634) may serve as an enforcement point for a security feature that creates a virtual edge abstraction of resources spanning potentially multiple physical hosting platforms. Finally, the orchestration functions1660at an orchestration entity may operate as a security feature enforcement point for marshalling resources along tenant boundaries.

Edge computing nodes may partition resources (memory, central processing unit (CPU), graphics processing unit (GPU), interrupt controller, input/output (I/O) controller, memory controller, bus controller, etc.) where respective partitionings may contain a RoT capability and where fan-out and layering according to a DICE model may further be applied to Edge Nodes. Cloud computing nodes often use containers, FaaS engines, Servlets, servers, or other computation abstraction that may be partitioned according to a DICE layering and fan-out structure to support a RoT context for each. Accordingly, the respective RoT spanning devices1610,1622, and1640may coordinate the establishment of a distributed trusted computing base (DTCB) such that a tenant-specific virtual trusted secure channel linking all elements end to end can be established.

Further, it will be understood that a container may have data or workload specific keys protecting its content from a previous edge node. As part of migration of a container, a pod controller at a source edge node may obtain a migration key from a target edge node pod controller where the migration key is used to wrap the container-specific keys. When the container/pod is migrated to the target edge node, the unwrapping key is exposed to the pod controller that then decrypts the wrapped keys. The keys may now be used to perform operations on container specific data. The migration functions may be gated by properly attested edge nodes and pod managers (as described above).

In further examples, an edge computing system is extended to provide for orchestration of multiple applications through the use of containers (a contained, deployable unit of software that provides code and needed dependencies) in a multi-owner, multi-tenant environment. A multi-tenant orchestrator may be used to perform key management, trust anchor management, and other security functions related to the provisioning and lifecycle of the trusted ‘slice’ concept inFIG. 16. For instance, an edge computing system may be configured to fulfill requests and responses for various client endpoints from multiple virtual edge instances (and, from a cloud or remote data center). The use of these virtual edge instances may support multiple tenants and multiple applications (e.g., augmented reality (AR)/virtual reality (VR), enterprise applications, content delivery, gaming, compute offload) simultaneously. Further, there may be multiple types of applications within the virtual edge instances (e.g., normal applications; latency sensitive applications; latency-critical applications; user plane applications; networking applications; etc.). The virtual edge instances may also be spanned across systems of multiple owners at different geographic locations (or respective computing systems and resources which are co-owned or co-managed by multiple owners).

For instance, each edge node1622,1624may implement the use of containers, such as with the use of a container “pod”1626,1628providing a group of one or more containers. In a setting that uses one or more container pods, a pod controller or orchestrator is responsible for local control and orchestration of the containers in the pod. Various edge node resources (e.g., storage, compute, services, depicted with hexagons) provided for the respective edge slices1932,1934are partitioned according to the needs of each container.

With the use of container pods, a pod controller oversees the partitioning and allocation of containers and resources. The pod controller receives instructions from an orchestrator (e.g., orchestrator1660) that instructs the controller on how best to partition physical resources and for what duration, such as by receiving key performance indicator (KPI) targets based on SLA contracts. The pod controller determines which container requires which resources and for how long in order to complete the workload and satisfy the SLA. The pod controller also manages container lifecycle operations such as: creating the container, provisioning it with resources and applications, coordinating intermediate results between multiple containers working on a distributed application together, dismantling containers when workload completes, and the like. Additionally, a pod controller may serve a security role that prevents assignment of resources until the right tenant authenticates or prevents provisioning of data or a workload to a container until an attestation result is satisfied.

Also, with the use of container pods, tenant boundaries can still exist but in the context of each pod of containers. If each tenant specific pod has a tenant specific pod controller, there will be a shared pod controller that consolidates resource allocation requests to avoid typical resource starvation situations. Further controls may be provided to ensure attestation and trustworthiness of the pod and pod controller. For instance, the orchestrator1660may provision an attestation verification policy to local pod controllers that perform attestation verification. If an attestation satisfies a policy for a first tenant pod controller but not a second tenant pod controller, then the second pod could be migrated to a different edge node that does satisfy it. Alternatively, the first pod may be allowed to execute, and a different shared pod controller is installed and invoked prior to the second pod executing.

FIG. 17illustrates additional compute arrangements deploying containers in an edge computing system. As a simplified example, system arrangements1710,1720depict settings in which a pod controller (e.g., container managers1711,1721, and container orchestrator1731) is adapted to launch containerized pods, functions, and functions-as-a-service instances through execution via compute nodes (1715in arrangement1710), or to separately execute containerized virtualized network functions through execution via compute nodes (1723in arrangement1720). This arrangement is adapted for use of multiple tenants in system arrangement1730(using compute nodes1737), where containerized pods (e.g., pods1712), functions (e.g., functions1713, VNFs1722,1736), and functions-as-a-service instances (e.g., FaaS instance1714) are launched within virtual machines (e.g., VMs1734,1735for tenants1732,1733) specific to respective tenants (aside the execution of virtualized network functions). This arrangement is further adapted for use in system arrangement1740, which provides containers1742,1743, or execution of the various functions, applications, and functions on compute nodes1744, as coordinated by an container-based orchestration system1741.

The system arrangements of depicted inFIG. 17provides an architecture that treats VMs, Containers, and Functions equally in terms of application composition (and resulting applications are combinations of these three ingredients). Each ingredient may involve use of one or more accelerator (FPGA, ASIC) components as a local backend. In this manner, applications can be split across multiple edge owners, coordinated by an orchestrator.

In the context ofFIG. 17, the pod controller/container manager, container orchestrator, and individual nodes may provide a security enforcement point. However, tenant isolation may be orchestrated where the resources allocated to a tenant are distinct from resources allocated to a second tenant, but edge owners cooperate to ensure resource allocations are not shared across tenant boundaries. Or, resource allocations could be isolated across tenant boundaries, as tenants could allow “use” via a subscription or transaction/contract basis. In these contexts, virtualization, containerization, enclaves, and hardware partitioning schemes may be used by edge owners to enforce tenancy. Other isolation environments may include: bare metal (dedicated) equipment, virtual machines, containers, virtual machines on containers, or combinations thereof.

In further examples, aspects of software-defined or controlled silicon hardware, and other configurable hardware, may integrate with the applications, functions, and services an edge computing system. Software defined silicon (SDSi) may be used to ensure the ability for some resource or hardware ingredient to fulfill a contract or service level agreement, based on the ingredient's ability to remediate a portion of itself or the workload (e.g., by an upgrade, reconfiguration, or provision of new features within the hardware configuration itself).

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. 18shows a simplified vehicle compute and communication use case involving mobile access to applications in an edge computing system1800that implements an edge cloud1310. In this use case, respective client compute nodes1810may 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 nodes1820during traversal of a roadway. For instance, the edge gateway nodes1820may 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 node1810and a particular edge gateway device1820may propagate so as to maintain a consistent connection and context for the client compute node1810. 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 devices1820include an amount of processing and storage capabilities and, as such, some processing and/or storage of data for the client compute nodes1810may be performed on one or more of the edge gateway devices1820.

The edge gateway devices1820may communicate with one or more edge resource nodes1840, which are illustratively embodied as compute servers, appliances or components located at or in a communication base station1842(e.g., a base station of a cellular network). As discussed above, the respective edge resource nodes1840include an amount of processing and storage capabilities and, as such, some processing and/or storage of data for the client compute nodes1810may be performed on the edge resource node1840. For example, the processing of data that is less urgent or important may be performed by the edge resource node1840, while the processing of data that is of a higher urgency or importance may be performed by the edge gateway devices1820(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)1840also communicate with the core data center1850, 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 center1850may provide a gateway to the global network cloud1860(e.g., the Internet) for the edge cloud1610operations formed by the edge resource node(s)1840and the edge gateway devices1820. Additionally, in some examples, the core data center1850may 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 center1850(e.g., processing of low urgency or importance, or high complexity).

The edge gateway nodes1820or the edge resource nodes1840may offer the use of stateful applications1832and a geographic distributed database1834. Although the applications1832and database1834are illustrated as being horizontally distributed at a layer of the edge cloud1610, 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 node1810, other parts at the edge gateway nodes1820or the edge resource nodes1840, 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 container1836(or pod of containers) may be flexibly migrated from an edge node1820to other edge nodes (e.g.,1820, 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 node1840may differ from edge gateway node1820and 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. 18may utilize various types of mobile edge nodes, such as an edge node hosted in a vehicle (car/truck/tram/train) 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 nodes1820, some others at the edge resource node1840, and others in the core data center1850or global network cloud1860.

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, container is “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).

The edge computing system1800can include or be in communication with an edge provisioning node1844. The edge provisioning node1844can distribute software such as the example computer readable instructions2082ofFIG. 20B, to various receiving parties for implementing any of the methods described herein. The example edge provisioning node1844may be implemented by any computer server, home server, content delivery network, virtual server, software distribution system, central facility, storage device, storage node, data facility, cloud service, etc., capable of storing and/or transmitting software instructions (e.g., code, scripts, executable binaries, containers, packages, compressed files, and/or derivatives thereof) to other computing devices. Component(s) of the example edge provisioning node1844may be located in a cloud, in a local area network, in an edge network, in a wide area network, on the Internet, and/or any other location communicatively coupled with the receiving party(ies). The receiving parties may be customers, clients, associates, users, etc. of the entity owning and/or operating the edge provisioning node1844. For example, the entity that owns and/or operates the edge provisioning node1844may be a developer, a seller, and/or a licensor (or a customer and/or consumer thereof) of software instructions such as the example computer readable instructions2082ofFIG. 20B. The receiving parties may be consumers, service providers, users, retailers, OEMs, etc., who purchase and/or license the software instructions for use and/or re-sale and/or sub-licensing.

In an example, edge provisioning node1844includes one or more servers and one or more storage devices. The storage devices host computer readable instructions such as the example computer readable instructions2082ofFIG. 20B, as described below. Similar to the edge gateway devices1920described above, the one or more servers of the edge provisioning node1844are in communication with a base station1842or 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 instructions2082from the edge provisioning node1844. For example, the software instructions, which may correspond to the example computer readable instructions2082ofFIG. 20B, may be downloaded to the example processor platform/s, which is to execute the computer readable instructions2082to implement the methods described herein.

In some examples, the processor platform(s) that execute the computer readable instructions2082can be physically located in different geographic locations, legal jurisdictions, etc. In some examples, one or more servers of the edge provisioning node1844periodically offer, transmit, and/or force updates to the software instructions (e.g., the example computer readable instructions2082ofFIG. 20B) 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 instructions2082can 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. 19illustrates a mobile edge system reference architecture (or MEC architecture)1900, such as is indicated by ETSI MEC specifications.FIG. 19specifically illustrates a MEC architecture1900with MEC hosts1902and1904providing functionalities in accordance with the ETSI GS MEC-003 specification. In some aspects, enhancements to the MEC platform1932and the MEC platform manager1906may be used for providing specific computing functions within the MEC architecture1900.

Referring toFIG. 19, the MEC network architecture1900can include MEC hosts1902and1904, a virtualization infrastructure manager (VIM)1908, an MEC platform manager1906, an MEC orchestrator1910, an operations support system1912, a user app proxy1914, a UE app1918running on UE1920, and CFS portal1916. The MEC host1902can include a MEC platform1932with filtering rules control component1940, a DNS handling component1942, a service registry1938, and MEC services1936. The MEC services1936can include at least one scheduler, which can be used to select resources for instantiating MEC apps (or NFVs)1926,1927, and1928upon virtualization infrastructure1922. The MEC apps1926and1928can be configured to provide services1930and1931, 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 app1905instantiated within MEC host1904can be similar to the MEC apps1926-1928instantiated within MEC host1902. The virtualization infrastructure1922includes a data plane1924coupled to the MEC platform via an MP2 interface. Additional interfaces between various network entities of the MEC architecture1900are illustrated inFIG. 19.

The MEC platform manager1906can include MEC platform element management component1944, MEC app rules and requirements management component1946, and MEC app lifecycle management component1948. The various entities within the MEC architecture1900can perform functionalities as disclosed by the ETSI GS MEC-003 specification.

In some aspects, the remote application (or app)1950is configured to communicate with the MEC host1902(e.g., with the MEC apps1926-1928) via the MEC orchestrator1910and the MEC platform manager1906.

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. 20A and 20B. 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. 20A, an edge compute node2000includes a compute engine (also referred to herein as “compute circuitry”)2002, an input/output (I/O) subsystem2008, data storage2010, a communication circuitry subsystem2012, and, optionally, one or more peripheral devices2014. 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 node2000may be embodied as any type of engine, device, or collection of devices capable of performing various compute functions. In some examples, the compute node2000may 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 node2000includes or is embodied as a processor2004and a memory2006. The processor2004may be embodied as any type of processor capable of performing the functions described herein (e.g., executing an application). For example, the processor2004may 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 processor2004may 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, or AI hardware (e.g., GPUs or programmed FPGAs). Such an xPU may be designed to receive 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 a xPU, a SOC, a CPU, and other variations of the processor2004may work in coordination with each other to execute many types of operations and instructions within and on behalf of the compute node2000.

The memory2006may 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 is a block addressable memory device, such as those based on NAND or NOR technologies. 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 comprise 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 memory2006may be integrated into the processor2004. The memory2006may store various software and data used during operation such as one or more applications, data operated on by the application(s), libraries, and drivers.

The compute circuitry2002is communicatively coupled to other components of the compute node2000via the I/O subsystem2008, which may be embodied as circuitry and/or components to facilitate input/output operations with the compute circuitry2002(e.g., with the processor2004and/or the main memory2006) and other components of the compute circuitry2002. For example, the I/O subsystem2008may 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 subsystem2008may form a portion of a system-on-a-chip (SoC) and be incorporated, along with one or more of the processor2004, the memory2006, and other components of the compute circuitry2002, into the compute circuitry2002.

The one or more illustrative data storage devices2010may be embodied as any type of devices configured for short-term or long-term storage of data such as, for example, memory devices and circuits, memory cards, hard disk drives, solid-state drives, or other data storage devices. Individual data storage devices2010may include a system partition that stores data and firmware code for the data storage device2010. Individual data storage devices2010may 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 node2000.

The communication circuitry2012may be embodied as any communication circuit, device, or collection thereof, capable of enabling communications over a network between the compute circuitry2002and another compute device (e.g., an edge gateway of an implementing edge computing system). The communication circuitry2012may 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 circuitry2012includes a network interface controller (NIC)2020, which may also be referred to as a network interconnect card or a host fabric interface (HFI). The NIC2020may 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 node2000to connect with another compute device (e.g., an edge gateway node). In some examples, the NIC2020may 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 NIC2020may include a local processor (not shown) and/or a local memory (not shown) that are both local to the NIC2020. In such examples, the local processor of the NIC2020may be capable of performing one or more of the functions of the compute circuitry2002described herein. Additionally, or alternatively, in such examples, the local memory of the NIC2020may 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 node2000may include one or more peripheral devices2014. Such peripheral devices2014may 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 node2000. In further examples, the compute node2000may 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. 20Billustrates a block diagram of an example of components that may be present in an edge computing node2050for implementing the techniques (e.g., operations, processes, methods, and methodologies) described herein. This edge computing node2050provides a closer view of the respective components of node2000when implemented as or as part of a computing device (e.g., as a mobile device, a base station, server, gateway, etc.). The edge computing node2050may include any combinations 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 node2050, or as components otherwise incorporated within a chassis of a larger system.

The edge computing device2050may include processing circuitry in the form of a processor2052, 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 processor2052may be a part of a system on a chip (SoC) in which the processor2052and 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 processor2052may 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-A13 processor from Apple® Inc., a Snapdragon™ processor from Qualcomm® Technologies, Inc., or an OMAP™ processor from Texas Instruments, Inc. The processor2052and 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. 20B.

The processor2052may communicate with a system memory2054over an interconnect2056(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 storage2058may also couple to the processor2052via the interconnect2056. In an example, the storage2058may be implemented via a solid-state disk drive (SSDD). Other devices that may be used for the storage2058include 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 storage2058may be on-die memory or registers associated with the processor2052. However, in some examples, the storage2058may be implemented using a micro hard disk drive (HDD). Further, any number of new technologies may be used for the storage2058in 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 interconnect2056. The interconnect2056may 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 interconnect2056may 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 interconnect2056may couple the processor2052to a transceiver2066, for communications with the connected edge devices2062. The transceiver2066may 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 devices2062. 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 transceiver2066(or multiple transceivers) may communicate using multiple standards or radios for communications at a different range. For example, the edge computing node2050may 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 devices2062, 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 transceiver2066, as described herein. For example, the transceiver2066may 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 transceiver2066may 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)2068may be included to provide a wired communication to nodes of the edge cloud2095or to other devices, such as the connected edge devices2062(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 NIC2068may be included to enable connecting to a second network, for example, a first NIC2068providing communications to the cloud over Ethernet, and a second NIC2068providing 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 components2064,2066,2068, or2070. Accordingly, in various examples, applicable means for communicating (e.g., receiving, transmitting, etc.) may be embodied by such communications circuitry.

The edge computing node2050may include or be coupled to acceleration circuitry2064, 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 interconnect2056may couple the processor2052to a sensor hub or external interface2070that is used to connect additional devices or subsystems. The devices may include sensors2072, 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 interface2070further may be used to connect the edge computing node2050to actuators2074, 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 node2050. For example, a display or other output device2084may be included to show information, such as sensor readings or actuator position. An input device2086, such as a touch screen or keypad may be included to accept input. An output device2084may 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 node2050. 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 battery2076may power the edge computing node2050, although, in examples in which the edge computing node2050is 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 battery2076may 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/charger2078may be included in the edge computing node2050to track the state of charge (SoCh) of the battery2076, if included. The battery monitor/charger2078may be used to monitor other parameters of the battery2076to provide failure predictions, such as the state of health (SoH) and the state of function (SoF) of the battery2076. The battery monitor/charger2078may include a battery monitoring integrated circuit, such as an LTC4020 or an LTC2990 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/charger2078may communicate the information on the battery2076to the processor2052over the interconnect2056. The battery monitor/charger2078may also include an analog-to-digital (ADC) converter that enables the processor2052to directly monitor the voltage of the battery2076or the current flow from the battery2076. The battery parameters may be used to determine actions that the edge computing node2050may perform, such as transmission frequency, mesh network operation, sensing frequency, and the like.

A power block2080, or other power supply coupled to a grid, may be coupled with the battery monitor/charger2078to charge the battery2076. In some examples, the power block2080may be replaced with a wireless power receiver to obtain the power wirelessly, for example, through a loop antenna in the edge computing node2050. 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/charger2078. The specific charging circuits may be selected based on the size of the battery2076, 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 storage2058may include instructions2082in the form of software, firmware, or hardware commands to implement the techniques described herein. Although such instructions2082are shown as code blocks included in the memory2054and the storage2058, 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 instructions2082provided via the memory2054, the storage2058, or the processor2052may be embodied as a non-transitory, machine-readable medium2060including code to direct the processor2052to perform electronic operations in the edge computing node2050. The processor2052may access the non-transitory, machine-readable medium2060over the interconnect2056. For instance, the non-transitory, machine-readable medium2060may be embodied by devices described for the storage2058or may include specific storage units such as optical disks, flash drives, or any number of other hardware devices. The non-transitory, machine-readable medium2060may include instructions to direct the processor2052to 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.

Also in a specific example, the instructions2082on the processor2052(separately, or in combination with the instructions2082of the machine readable medium2060) may configure execution or operation of a trusted execution environment (TEE)2090. In an example, the TEE2090operates as a protected area accessible to the processor2052for secure execution of instructions and secure access to data. Various implementations of the TEE2090, and an accompanying secure area in the processor2052or the memory2054may 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 device2050through the TEE2090and the processor2052.

FIG. 21illustrates an example domain topology for respective internet-of-things (IoT) networks coupled through links to respective gateways. The internet of things (IoT) is a concept in which a large number of computing devices are interconnected to each other and to the Internet to provide functionality and data acquisition at very low levels. Thus, as used herein, an IoT device may include a semiautonomous device performing a function, such as sensing or control, among others, in communication with other IoT devices and a wider network, such as the Internet.

Often, IoT devices are limited in memory, size, or functionality, allowing larger numbers to be deployed for a similar cost to smaller numbers of larger devices. However, an IoT device may be a smart phone, laptop, tablet, or PC, or other larger device. Further, an IoT device may be a virtual device, such as an application on a smart phone or other computing device. IoT devices may include IoT gateways, used to couple IoT devices to other IoT devices and to cloud applications, for data storage, process control, and the like.

Networks of IoT devices may include commercial and home automation devices, such as water distribution systems, electric power distribution systems, pipeline control systems, plant control systems, light switches, thermostats, locks, cameras, alarms, motion sensors, and the like. The IoT devices may be accessible through remote computers, servers, and other systems, for example, to control systems or access data.

The future growth of the Internet and like networks may involve very large numbers of IoT devices. Accordingly, in the context of the techniques discussed herein, a number of innovations for such future networking will address the need for all these layers to grow unhindered, to discover and make accessible connected resources, and to support the ability to hide and compartmentalize connected resources. Any number of network protocols and communications standards may be used, wherein each protocol and standard is designed to address specific objectives. Further, the protocols are part of the fabric supporting human accessible services that operate regardless of location, time, or space. The innovations include service delivery and associated infrastructure, such as hardware and software; security enhancements; and the provision of services based on Quality of Service (QoS) terms specified in service level and service delivery agreements. As will be understood, the use of IoT devices and networks, such as those introduced inFIGS. 21 and 22, present a number of new challenges in a heterogeneous network of connectivity comprising a combination of wired and wireless technologies.

FIG. 21specifically provides a simplified drawing of a domain topology that may be used for a number of internet-of-things (IoT) networks comprising IoT devices2104, with the IoT networks2156,2158,2160,2162, coupled through backbone links2102to respective gateways2154. For example, a number of IoT devices2104may communicate with a gateway2154, and with each other through the gateway2154. To simplify the drawing, not every IoT device2104, or communications link (e.g., link2116,2122,2128, or2132) is labeled. The backbone links2102may include any number of wired or wireless technologies, including optical networks, and may be part of a local area network (LAN), a wide area network (WAN), or the Internet. Additionally, such communication links facilitate optical signal paths among both IoT devices2104and gateways2154, including the use of MUXing/deMUXing components that facilitate interconnection of the various devices.

The network topology may include any number of types of IoT networks, such as a mesh network provided with the network2156using Bluetooth low energy (BLE) links2122. Other types of IoT networks that may be present include a wireless local area network (WLAN) network2158used to communicate with IoT devices2104through IEEE 802.11 (Wi-Fi®) links2128, a cellular network2160used to communicate with IoT devices2104through an LTE/LTE-A (4G) or 5G cellular network, and a low-power wide area (LPWA) network2162, for example, a LPWA network compatible with the LoRaWan specification promulgated by the LoRa alliance, or a IPv6 over Low Power Wide-Area Networks (LPWAN) network compatible with a specification promulgated by the Internet Engineering Task Force (IETF). Further, the respective IoT networks may communicate with an outside network provider (e.g., a tier 2 or tier 3 provider) using any number of communications links, such as an LTE cellular link, an LPWA link, or a link based on the IEEE 802.15.4 standard, such as Zigbee®. The respective IoT networks may also operate with use of a variety of network and internet application protocols such as Constrained Application Protocol (CoAP). The respective IoT networks may also be integrated with coordinator devices that provide a chain of links that forms cluster tree of linked devices and networks.

Each of these IoT networks may provide opportunities for new technical features, such as those as described herein. The improved technologies and networks may enable the exponential growth of devices and networks, including the use of IoT networks into “fog” devices or integrated into “edge” computing systems. As the use of such improved technologies grows, the IoT networks may be developed for self-management, functional evolution, and collaboration, without needing direct human intervention. The improved technologies may even enable IoT networks to function without centralized controlled systems. Accordingly, the improved technologies described herein may be used to automate and enhance network management and operation functions far beyond current implementations.

In an example, communications between IoT devices2104, such as over the backbone links2102, may be protected by a decentralized system for authentication, authorization, and accounting (AAA). In a decentralized AAA system, distributed payment, credit, audit, authorization, and authentication systems may be implemented across interconnected heterogeneous network infrastructure. This allows systems and networks to move towards autonomous operations. In these types of autonomous operations, machines may even contract for human resources and negotiate partnerships with other machine networks. This may allow the achievement of mutual objectives and balanced service delivery against outlined, planned service level agreements as well as achieve solutions that provide metering, measurements, traceability, and trackability. The creation of new supply chain structures and methods may enable a multitude of services to be created, mined for value, and collapsed without any human involvement.

Such IoT networks may be further enhanced by the integration of sensing technologies, such as sound, light, electronic traffic, facial and pattern recognition, smell, vibration, into the autonomous organizations among the IoT devices. The integration of sensory systems may allow systematic and autonomous communication and coordination of service delivery against contractual service objectives, orchestration, and quality of service (QoS) based swarming and fusion of resources. Some of the individual examples of network-based resource processing include the following.

The mesh network2156, for instance, may be enhanced by systems that perform inline data-to-information transforms. For example, self-forming chains of processing resources comprising a multi-link network may distribute the transformation of raw data to information in an efficient manner, and the ability to differentiate between assets and resources and the associated management of each. Furthermore, the proper components of infrastructure and resource based trust and service indices may be inserted to improve the data integrity, quality, assurance and deliver a metric of data confidence.

The WLAN network2158, for instance, may use systems that perform standards conversion to provide multi-standard connectivity, enabling IoT devices2104using different protocols to communicate. Further systems may provide seamless interconnectivity across a multi-standard infrastructure comprising visible Internet resources and hidden Internet resources.

Communications in the cellular network2160, for instance, may be enhanced by systems that offload data, extend communications to more remote devices, or both. The LPWA network2162may include systems that perform non-Internet protocol (IP) to IP interconnections, addressing, and routing. Further, each of the IoT devices2104may include the appropriate transceiver for wide area communications with that device. Further, each IoT device2104may include other transceivers for communications using additional protocols and frequencies. This is discussed further with respect to the communication environment and hardware of an IoT processing device depicted inFIGS. 23 and 24.

Finally, clusters of IoT devices may be equipped to communicate with other IoT devices as well as with a cloud network. This may allow the IoT devices to form an ad-hoc network between the devices, allowing them to function as a single device, which may be termed a fog device, fog platform, or fog network. This configuration is discussed further with respect toFIG. 22below.

FIG. 22illustrates a cloud computing network in communication with a mesh network of IoT devices (devices2202) operating as a fog platform in a networked scenario. The mesh network of IoT devices may be termed a fog network2220, established from a network of devices operating at the edge of the cloud2200. To simplify the diagram, not every IoT device2202is labeled.

The fog network2220may be considered to be a massively interconnected network wherein a number of IoT devices2202are in communications with each other, for example, by radio links2222. The fog network2220may establish a horizontal, physical, or virtual resource platform that can be considered to reside between IoT edge devices and cloud or data centers. A fog network, in some examples, may support vertically-isolated, latency-sensitive applications through layered, federated, or distributed computing, storage, and network connectivity operations. However, a fog network may also be used to distribute resources and services at and among the edge and the cloud. Thus, references in the present document to the “edge”, “fog”, and “cloud” are not necessarily discrete or exclusive of one another.

As an example, the fog network2220may be facilitated using an interconnect specification released by the Open Connectivity Foundation™ (OCF). This standard allows devices to discover each other and establish communications for interconnects. Other interconnection protocols may also be used, including, for example, the optimized link state routing (OLSR) Protocol, the better approach to mobile ad-hoc networking (B.A.T.M.A.N.) routing protocol, or the OMA Lightweight M2M (LWM2M) protocol, among others.

Three types of IoT devices2202are shown in this example, gateways2204, data aggregators2226, and sensors2228, although any combinations of IoT devices2202and functionality may be used. The gateways2204may be edge devices that provide communications between the cloud2200and the fog network2220, and may also provide the backend process function for data obtained from sensors2228, such as motion data, flow data, temperature data, and the like. The data aggregators2226may collect data from any number of the sensors2228and perform the back end processing function for the analysis. The results, raw data, or both may be passed along to the cloud2200through the gateways2204. The sensors2228may be full IoT devices2202, for example, capable of both collecting data and processing the data. In some cases, the sensors2228may be more limited in functionality, for example, collecting the data and allowing the data aggregators2226or gateways2204to process the data.

Communications from any IoT device2202may be passed along a convenient path between any of the IoT devices2202to reach the gateways2204. In these networks, the number of interconnections provide substantial redundancy, allowing communications to be maintained, even with the loss of a number of IoT devices2202. Further, the use of a mesh network may allow IoT devices2202that are very low power or located at a distance from infrastructure to be used, as the range to connect to another IoT device2202may be much less than the range to connect to the gateways2204.

The fog network2220provided from these IoT devices2202may be presented to devices in the cloud2200, such as a server2206, as a single device located at the edge of the cloud2200, e.g., a fog network operating as a device or platform. In this example, the alerts coming from the fog platform may be sent without being identified as coming from a specific IoT device2202within the fog network2220. In this fashion, the fog network2220may be considered a distributed platform that provides computing and storage resources to perform processing or data-intensive tasks such as data analytics, data aggregation, and machine-learning, among others.

In some examples, the IoT devices2202may be configured using an imperative programming style, e.g., with each IoT device2202having a specific function and communication partners. However, the IoT devices2202forming the fog platform may be configured in a declarative programming style, enabling the IoT devices2202to reconfigure their operations and communications, such as to determine needed resources in response to conditions, queries, and device failures. As an example, a query from a user located at a server2206about the operations of a subset of equipment monitored by the IoT devices2202may result in the fog network2220device the IoT devices2202, such as particular sensors2228, needed to answer the query. The data from these sensors2228may then be aggregated and analyzed by any combination of the sensors2228, data aggregators2226, or gateways2204, before being sent on by the fog network2220to the server2206to answer the query. In this example, IoT devices2202in the fog network2220may select the sensors2228used based on the query, such as adding data from flow sensors or temperature sensors. Further, if some of the IoT devices2202are not operational, other IoT devices2202in the fog network2220may provide analogous data, if available.

In other examples, the operations and functionality described herein may be embodied by an IoT or edge compute device in the example form of an electronic processing system, within which a set or sequence of instructions may be executed to cause the electronic processing system to perform any one of the methodologies discussed herein, according to an example embodiment. The device may be an IoT device or an IoT gateway, including a machine embodied by aspects of a personal computer (PC), a tablet PC, a personal digital assistant (PDA), a mobile telephone or smartphone, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine.

Further, while only a single machine may be depicted and referenced in the examples above, such machine shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. Further, these and like examples to a processor-based system shall be taken to include any set of one or more machines that are controlled by or operated by a processor, set of processors, or processing circuitry (e.g., a computer) to individually or jointly execute instructions to perform any one or more of the methodologies discussed herein. Accordingly, in various examples, applicable means for processing (e.g., processing, controlling, generating, evaluating, etc.) may be embodied by such processing circuitry.

FIG. 23illustrates a drawing of a cloud computing network, or cloud2300, in communication with a number of Internet of Things (IoT) devices. The cloud2300may represent the Internet, or may be a local area network (LAN), or a wide area network (WAN), such as a proprietary network for a company. The IoT devices may include any number of different types of devices, grouped in various combinations. For example, a traffic control group2306may include IoT devices along streets in a city. These IoT devices may include stoplights, traffic flow monitors, cameras, weather sensors, and the like. The traffic control group2306, or other subgroups, may be in communication with the cloud2300through wired or wireless links2308, such as LPWA links, and the like. Further, a wired or wireless sub-network2312may allow the IoT devices to communicate with each other, such as through a local area network, a wireless local area network, and the like. The IoT devices may use another device, such as a gateway2310or2328to communicate with remote locations such as the cloud2300; the IoT devices may also use one or more servers2330to facilitate communication with the cloud2300or with the gateway2310. For example, the one or more servers2330may operate as an intermediate network node to support a local edge cloud or fog implementation among a local area network. Further, the gateway2328that is depicted may operate in a cloud-to-gateway-to-many edge devices configuration, such as with the various IoT devices2314,2320,2324being constrained or dynamic to an assignment and use of resources in the cloud2300.

Other example groups of IoT devices may include remote weather stations2314, local information terminals2316, alarm systems2318, automated teller machines2320, alarm panels2322, or moving vehicles, such as emergency vehicles2324or other vehicles2326, among many others. Each of these IoT devices may be in communication with other IoT devices, with servers2304, with another IoT fog device or system (not shown, but depicted inFIG. 25), or a combination therein. The groups of IoT devices may be deployed in various residential, commercial, and industrial settings (including in both private or public environments).

As may be seen fromFIG. 23, a large number of IoT devices may be communicating through the cloud2300. This may allow different IoT devices to request or provide information to other devices autonomously. For example, a group of IoT devices (e.g., the traffic control group2306) may request a current weather forecast from a group of remote weather stations2314, which may provide the forecast without human intervention. Further, an emergency vehicle2324may be alerted by an automated teller machine2320that a burglary is in progress. As the emergency vehicle2324proceeds towards the automated teller machine2320, it may access the traffic control group2306to request clearance to the location, for example, by lights turning red to block cross traffic at an intersection in sufficient time for the emergency vehicle2324to have unimpeded access to the intersection.

Clusters of IoT devices, such as the remote weather stations2314or the traffic control group2306, may be equipped to communicate with other IoT devices as well as with the cloud2300. This may allow the IoT devices to form an ad-hoc network between the devices, allowing them to function as a single device, which may be termed a fog device or system (e.g., as described above with reference toFIG. 22).

FIG. 24is a block diagram of an example of components that may be present in an IoT device2450for implementing the techniques described herein. The IoT device2450may include any combinations of the components shown in the example or referenced in the disclosure above. The components may be implemented as ICs, portions thereof, discrete electronic devices, or other modules, logic, hardware, software, firmware, or a combination thereof adapted in the IoT device2450, or as components otherwise incorporated within a chassis of a larger system. Additionally, the block diagram ofFIG. 24is intended to depict a high-level view of components of the IoT device2450. However, some of the components shown may be omitted, additional components may be present, and different arrangement of the components shown may occur in other implementations.

The IoT device2450may include processing circuitry in the form of a processor2452, which may be a microprocessor, a multi-core processor, a multithreaded processor, an ultra-low voltage processor, an embedded processor, or other known processing elements. The processor2452may be a part of a system on a chip (SoC) in which the processor2452and other components are formed into a single integrated circuit, or a single package, such as the Edison™ or Galileo™ SoC boards from Intel. As an example, the processor2452may include an Intel® Architecture Core™ based processor, such as a Quark™, an Atom™, an i3, an i5, an i7, or an MCU-class processor, or another such processor available from Intel® Corporation, Santa Clara, Calif. 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 customer thereof, or their licensees or adopters. The processors may include units such as an A5-A14 processor from Apple® Inc., a Snapdragon™ processor from Qualcomm® Technologies, Inc., or an OMAP™ processor from Texas Instruments, Inc.

The processor2452may communicate with a system memory2454over an interconnect2456(e.g., a bus). Any number of memory devices may be used to provide for a given amount of system memory. As examples, the memory may 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 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 storage2458may also couple to the processor2452via the interconnect2456. In an example the storage2458may be implemented via a solid state disk drive (SSDD). Other devices that may be used for the storage2458include flash memory cards, such as SD cards, microSD cards, xD picture cards, and the like, and USB flash drives. In low power implementations, the storage2458may be on-die memory or registers associated with the processor2452. However, in some examples, the storage2458may be implemented using a micro hard disk drive (HDD). Further, any number of new technologies may be used for the storage2458in 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 interconnect2456. The interconnect2456may 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 interconnect2456may be a proprietary bus, for example, used in a SoC based system. Other bus systems may be included, such as an I2C interface, an SPI interface, point to point interfaces, and a power bus, among others.

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 components2462,2466,2468, or2470. Accordingly, in various examples, applicable means for communicating (e.g., receiving, transmitting, etc.) may be embodied by such communications circuitry.

The interconnect2456may couple the processor2452to a mesh transceiver2462, for communications with other mesh devices2464. The mesh transceiver2462may 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 mesh devices2464. For example, a 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 WWAN unit.

The mesh transceiver2462may communicate using multiple standards or radios for communications at different range. For example, the IoT device2450may communicate with close devices, e.g., within about 10 meters, using a local transceiver based on BLE, or another low power radio, to save power. More distant mesh devices2464, 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 mesh transceiver2462and wireless network transceiver2466, as described herein. For example, the radio transceivers2462and2466may include an LTE or other 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 radio transceivers2462and2466may include radios that are compatible with any number of 3GPP (Third Generation Partnership Project) specifications, notably Long Term Evolution (LTE), Long Term Evolution-Advanced (LTE-A), and Long Term Evolution-Advanced Pro (LTE-A Pro). It may be noted that radios compatible with any number of other fixed, mobile, or satellite communication technologies and standards may be selected. These may include, for example, any Cellular Wide Area radio communication technology, which may include e.g. a 5th Generation (5G) communication systems, a Global System for Mobile Communications (GSM) radio communication technology, a General Packet Radio Service (GPRS) radio communication technology, or an Enhanced Data Rates for GSM Evolution (EDGE) radio communication technology, a UMTS (Universal Mobile Telecommunications System) communication technology, In addition to the standards listed above, any number of satellite uplink technologies may be used for the wireless network transceiver2466, including, for example, radios compliant with standards issued by the ITU (International Telecommunication Union), or the ETSI (European Telecommunications Standards Institute), among others. The examples provided herein are thus understood as being applicable to various other communication technologies, both existing and not yet formulated.

A network interface controller (NIC)2468may be included to provide a wired communication to the cloud2400or to other devices, such as the mesh devices2464. 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 NIC2468may be included to allow connect to a second network, for example, a NIC2468providing communications to the cloud over Ethernet, and a second NIC2468providing communications to other devices over another type of network.

The interconnect2456may couple the processor2452to an external interface2470that is used to connect external devices or subsystems. The external devices may include sensors2472, such as accelerometers, level sensors, flow sensors, optical light sensors, camera sensors, temperature sensors, a global positioning system (GPS) sensors, pressure sensors, barometric pressure sensors, and the like. The external interface2470further may be used to connect the IoT device2450to actuators2474, 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 IoT device2450. For example, a display or other output device2484may be included to show information, such as sensor readings or actuator position. An input device2486, such as a touch screen or keypad may be included to accept input. An output device2486may include any number of forms of audio or visual display, including simple visual outputs such as binary status indicators (e.g., LEDs) and multi-character visual outputs, or more complex outputs such as display screens (e.g., LCD screens), with the output of characters, graphics, multimedia objects, and the like being generated or produced from the operation of the IoT device2450.

A battery2476may power the IoT device2450, although in examples in which the IoT device2450is mounted in a fixed location, it may have a power supply coupled to an electrical grid. The battery2476may 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/charger2478may be included in the IoT device2450to track the state of charge (SoCh) of the battery2476. The battery monitor/charger2478may be used to monitor other parameters of the battery2476to provide failure predictions, such as the state of health (SoH) and the state of function (SoF) of the battery2476. The battery monitor/charger2478may include a battery monitoring integrated circuit, such as an LTC4020 or an LTC2990 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/charger2478may communicate the information on the battery2476to the processor2452over the interconnect2456. The battery monitor/charger2478may also include an analog-to-digital (ADC) convertor that allows the processor2452to directly monitor the voltage of the battery2476or the current flow from the battery2476. The battery parameters may be used to determine actions that the IoT device2450may perform, such as transmission frequency, mesh network operation, sensing frequency, and the like.

A power block2480, or other power supply coupled to a grid, may be coupled with the battery monitor/charger2478to charge the battery2476. In some examples, the power block2480may be replaced with a wireless power receiver to obtain the power wirelessly, for example, through a loop antenna in the IoT device2450. 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/charger2478. The specific charging circuits chosen depending on the size of the battery2476, 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 storage2458may include instructions2482in the form of software, firmware, or hardware commands to implement the techniques described herein. Although such instructions2482are shown as code blocks included in the memory2454and the storage2458, 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 instructions2482provided via the memory2454, the storage2458, or the processor2452may be embodied as a non-transitory, machine readable medium2460including code to direct the processor2452to perform electronic operations in the IoT device2450. The processor2452may access the non-transitory, machine readable medium2460over the interconnect2456. For instance, the non-transitory, machine readable medium2460may be embodied by devices described for the storage2458ofFIG. 24or may include specific storage units such as optical disks, flash drives, or any number of other hardware devices. The non-transitory, machine readable medium2460may include instructions to direct the processor2452to 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.

Also in a specific example, the instructions2488on the processor2452(separately, or in combination with the instructions2488of the machine readable medium2460) may configure execution or operation of a trusted execution environment (TEE)2490. In an example, the TEE2490operates as a protected area accessible to the processor2452for secure execution of instructions and secure access to data. Various implementations of the TEE2490, and an accompanying secure area in the processor2452or the memory2454may 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 device2450through the TEE2490and the processor2452.

At a more generic level, an edge computing system may be described to encompass any number of deployments operating in an edge cloud1310, which provide coordination from client and distributed computing devices.FIG. 25provides a further abstracted overview of layers of distributed compute deployed among an edge computing environment for purposes of illustration.

FIG. 25generically depicts an edge computing system for providing edge services and applications to multi-stakeholder entities, as distributed among one or more client compute nodes2502, one or more edge gateway nodes2512, one or more edge aggregation nodes2522, one or more core data centers2532, and a global network cloud2542, as distributed across layers of the network. The implementation of the edge computing system may be provided at 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.

Each node or device of the edge computing system is located at a particular layer corresponding to layers2510,2520,2530,2540,2550. For example, the client compute nodes2502are each located at an endpoint layer2510, while each of the edge gateway nodes2512are located at an edge devices layer2520(local level) of the edge computing system. Additionally, each of the edge aggregation nodes2522(and/or fog devices2524, if arranged or operated with or among a fog networking configuration2526) are located at a network access layer2530(an intermediate level). Fog computing (or “fogging”) generally refers to extensions of cloud computing to the edge of an enterprise's network, typically in a coordinated distributed or multi-node network. Some forms of fog computing provide the deployment of compute, storage, and networking services between end devices and cloud computing data centers, on behalf of the cloud computing locations. Such forms of fog computing provide operations that are consistent with edge computing as discussed herein; many of the edge computing aspects discussed herein are applicable to fog networks, fogging, and fog configurations. Further, aspects of the edge computing systems discussed herein may be configured as a fog, or aspects of a fog may be integrated into an edge computing architecture.

The core data center2532is located at a core network layer2540(e.g., a regional or geographically-central level), while the global network cloud2542is located at a cloud data center layer2550(e.g., a national or global layer). The use of “core” is provided as a term for a centralized network location—deeper in the network—which is accessible by multiple edge nodes or components; however, a “core” does not necessarily designate the “center” or the deepest location of the network. Accordingly, the core data center2532may be located within, at, or near the edge cloud1310.

Although an illustrative number of client compute nodes2502, edge gateway nodes2512, edge aggregation nodes2522, core data centers2532, global network clouds2542are shown inFIG. 25, it should be appreciated that the edge computing system may include more or fewer devices or systems at each layer. Additionally, as shown inFIG. 25, the number of components of each layer2510,2520,2530,2540,2550generally increases at each lower level (i.e., when moving closer to endpoints). As such, one edge gateway node2512may service multiple client compute nodes2502, and one edge aggregation node2522may service multiple edge gateway nodes2512.

Consistent with the examples provided herein, each client compute node2502may be embodied as any type of end point component, device, appliance, or “thing” capable of communicating as a producer or consumer of data. Further, the label “node” or “device” as used in the edge computing system2500does not necessarily mean that such node or device operates in a client or agent/minion/follower role; rather, any of the nodes or devices in the edge computing system2500refer to individual entities, nodes, or subsystems which include discrete or connected hardware or software configurations to facilitate or use the edge cloud1310.

As such, the edge cloud1310is formed from network components and functional features operated by and within the edge gateway nodes2512and the edge aggregation nodes2522of layers2520,2530, respectively. The edge cloud1310may 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 shown inFIG. 25as the client compute nodes2502. In other words, the edge cloud1310may be envisioned as an “edge” which connects the endpoint devices and traditional mobile network access points that serves as an ingress point into service provider core networks, including carrier networks (e.g., Global System for Mobile Communications (GSM) networks, Long-Term Evolution (LTE) networks, 5G networks, etc.), while also providing storage and/or compute capabilities. Other types and forms of network access (e.g., Wi-Fi, long-range wireless networks) may also be utilized in place of or in combination with such 3GPP carrier networks.

In some examples, the edge cloud1310may form a portion of or otherwise provide an ingress point into or across a fog networking configuration2526(e.g., a network of fog devices2524, not shown in detail), which may be embodied as a system-level horizontal and distributed architecture that distributes resources and services to perform a specific function. For instance, a coordinated and distributed network of fog devices2524may perform computing, storage, control, or networking aspects in the context of an IoT system arrangement. Other networked, aggregated, and distributed functions may exist in the edge cloud1310between the cloud data center layer2550and the client endpoints (e.g., client compute nodes2502). Some of these are discussed in the following sections in the context of network functions or service virtualization, including the use of virtual edges and virtual services which are orchestrated for multiple stakeholders.

The edge gateway nodes2512and the edge aggregation nodes2522cooperate to provide various edge services and security to the client compute nodes2502. Furthermore, because each client compute node2502may be stationary or mobile, each edge gateway node2512may cooperate with other edge gateway devices to propagate presently provided edge services and security as the corresponding client compute node2502moves about a region. To do so, each of the edge gateway nodes2512and/or edge aggregation nodes2522may support multiple tenancy and multiple stakeholder configurations, in which services from (or hosted for) multiple service providers and multiple consumers may be supported and coordinated across a single or multiple compute devices.

From the foregoing, it will be appreciated that example methods, apparatus, systems, and articles of manufacture have been disclosed that enable identification, organization, management, querying, and deployment of AI-NF and/or other AI models via an edge computing infrastructure. As such, the disclosed methods, apparatus, systems, and articles of manufacture improve the security, attestability, reliability, and effectiveness of using a computing device in an edge computing infrastructure to leverage the best models available from different sources in different domains, connected via the edge computing infrastructure. Cross-domain interaction is achieved while safeguarding the integrity of the source device. Disclosed methods, apparatus and articles of manufacture are accordingly directed to one or more improvement(s) in the functioning of a computer or computing device/circuitry.

Example 1 is an edge infrastructure apparatus including: a model data structure to identify a plurality of models and associated meta-data from a plurality of circuitry connectable via the edge infrastructure apparatus; model inventory circuitry to manage the model data structure to at least one of query for one or more models, add a model, update a model, or remove a model from the model data structure; model discovery circuitry to select a selected model of the plurality of models identified in the model data structure in response to a query; and execution logic circuitry to inference the selected model.

Example 2 includes the apparatus of example 1, further including an interface to receive a request and to output at least one of an instance of the selected model or an outcome of the inference of the selected model.

Example 3 includes the apparatus of example 1, further including a training entity to train a query model to query the model data structure and evaluate the at least one selected model.

Example 4 includes the apparatus of example 1, further including a model cache to store an instance of at least a subset of the plurality of models identified in the model data structure.

Example 5 includes the apparatus of example 1, further include telemetry circuitry to provide at least one of network telemetry or edge appliance telemetry information for selection of the at least one selected model.

Example 6 includes the apparatus of example 1, wherein the model data structure is a table stored in memory identifying the plurality of models by: a) at least one of name or identifier, b) source, and c) meta-data.

Example 7 includes the apparatus of example 6, wherein the meta-data includes at least one of an accuracy, a recall, or a latency associated with the respective model.

Example 8 includes the apparatus of example 6, wherein the model discovery circuitry is to compare at least two of the plurality of models based on their associated meta-data.

Example 9 includes the apparatus of example 1, wherein the plurality of models includes artificial intelligence named function models.

Example 10 includes the apparatus of example 1, wherein an output of the inference of the selected model is a score.

Example 11 includes the apparatus of example 1, wherein the execution logic circuitry is to output a prediction based on the selected model.

Example 12 is at least one non-transitory computer readable storage medium including instructions that, when executed, cause at least one processor to at least: manage a model data structure, the model data structure identifying a plurality of models and associated meta-data from a plurality of circuitry connectable via an edge infrastructure apparatus; process a query to at least one of identify a model, add a model, update a model, or remove a model from the model data structure; select a selected model of the plurality of models identified in the model data structure in response to a query; and output at least one of an instance of the selected model or an inference of the selected model.

Example 13 includes the at least one non-transitory computer readable storage medium of example 12, wherein the instructions, when executed, cause the at least one processor to receive, via an interface, a request and to output, via the interface, at least one of an instance of the selected model or an outcome of the inference of the selected model.

Example 14 includes the at least one non-transitory computer readable storage medium of example 12, wherein the instructions, when executed, cause the at least one processor to store an instance of at least a subset of the plurality of models identified in the model data structure in a cache.

Example 15 includes the at least one non-transitory computer readable storage medium of example 12, wherein the model data structure is a table stored in memory identifying the plurality of models by: a) at least one of name or identifier, b) source, and c) meta-data, wherein the meta-data includes at least one of an accuracy, a recall, or a latency associated with the respective model, and wherein the instructions, when executed, cause the at least one processor to compare at least two of the plurality of models based on their associated meta-data.

Example 16 is a method including: managing, by executing an instruction using at least one processor, a model data structure, the model data structure identifying a plurality of models and associated meta-data from a plurality of circuitry connectable via an edge infrastructure apparatus; processing, by executing an instruction using the at least one processor, a query to at least one of identify a model, add a model, update a model, or remove a model from the model data structure; selecting, by executing an instruction using the at least one processor, a selected model of the plurality of models identified in the model data structure in response to a query; and outputting, by executing an instruction using the at least one processor, at least one of an instance of the selected model or an inference of the selected model.

Example 17 includes the method of example 16, further including receiving a request and outputting at least one of an instance of the selected model or an outcome of the inference of the selected model.

Example 18 includes the method of example 16, further including storing an instance of at least a subset of the plurality of models identified in the model data structure in a cache.

Example 19 includes the method of example 16, wherein the model data structure is a table stored in memory identifying the plurality of models by: a) at least one of name or identifier, b) source, and c) meta-data, wherein the meta-data includes at least one of an accuracy, a recall, or a latency associated with the respective model, and wherein the method further includes comparing at least two of the plurality of models based on their associated meta-data.

Example 20 is an apparatus including: memory circuitry to include instructions; and at least one processor to execute the instructions to at least: manage a model data structure, the model data structure identifying a plurality of models and associated meta-data from a plurality of circuitry connectable via an edge infrastructure apparatus; process a query to at least one of identify a model, add a model, update a model, or remove a model from the model data structure; select a selected model of the plurality of models identified in the model data structure in response to a query; and output at least one of an instance of the selected model or an inference of the selected model.

Example 21 is an edge server apparatus including: local inventory circuitry to identify at least one artificial intelligence model and associated meta-data; and logic circuitry to process a request to query for a first model, the logic circuitry to query the local inventory circuitry and to query edge infrastructure circuitry for the first model, the logic circuitry to select the first model from a plurality of results.

Example 22 includes the apparatus of example 21, wherein the query is based on at least one of a named function, an identifier, or meta-data associated with the first model.

Example 23 includes the apparatus of example 22, wherein the meta-data includes at least one of an accuracy, a recall, or a latency for the first model.

Example 24 is an apparatus including: means for managing a model data structure, the model data structure identifying a plurality of models and associated meta-data from a plurality of circuitry connectable via an edge infrastructure apparatus; means for processing a query to at least one of identify a model, add a model, update a model, or remove a model from the model data structure; select a selected model of the plurality of models identified in the model data structure in response to a query; and means for outputting at least one of an instance of the selected model or an inference of the selected model.

Example 25 is an apparatus including: means for identifying at least one artificial intelligence model and associated meta-data; and means for processing a request to query for a first model, means for processing to query for the first model and to select the first model from a plurality of results.

Example 26 includes any of examples 1-25, wherein the model data structure includes a distributed ledger.

Example 27 includes example 26, wherein the distributed ledger includes a blockchain.

Example 28 includes any of examples 1-27 implemented in an edge cloud infrastructure.

Example 29 includes any of examples 1-28 implemented with a vehicle-to-everything network.

Example 30 includes any of examples 1-20. wherein the model is a proprietary model.

Example 31 includes any of examples 1-30, wherein the model is a hybrid model including a general portion and a proprietary portion.