Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to schedule workloads based on secure edge to device telemetry by calculating a difference between a first telemetric data received from a first hardware device and an operating parameter and computing an adjustment for a second hardware device based on the difference between the first telemetric data and the operating parameter.

Abstract: Methods, apparatus, systems, and articles of manufacture to orchestrate execution of services in a mesh of orchestrators of an edge cloud computing environment are disclosed. An example orchestrator apparatus includes an interface to receive a service to be executed and to monitor execution of the service. The example apparatus includes an orchestration delegate to select resources and orchestrate deployment of the service to execute using the selected resources. The example apparatus includes a delegated orchestration manager to manage the orchestration delegate based on information from the interface and the orchestration delegate regarding execution of the service.

Abstract: Embodiments include apparatuses, systems, and methods for a computer-aided or autonomous driving (CA/AD) system to transmit to a remote service scheduling server, a request message for a service slot at a service provider. In embodiments, the remote service scheduling server may assign the service slot for a semi-autonomous or autonomous driving (SA/AD) vehicle including the CA/AD system. In embodiments, the service scheduling server may perform analysis or machine learning on data received from the CA/AD system as well as from a plurality of service stations in order to assign the service provider and/or service slot to the SA/AD vehicle. In embodiments, the CA/AD system may receive an assignment message and control driving elements of the SA/AD vehicle to autonomously or semi-autonomously drive the SA/AD vehicle to the service provider to receive the service at the assigned service slot. Other embodiments may also be described and claimed.

Abstract: In embodiments, one or more non-transitory computer-readable storage media comprise a set of instructions, which, when executed on a processor of a server, causes the server to receive sensor data from at least one sensor proximate to an entity, the entity is a human under care of at least one temporary guardian (TG) pursuant to a set of guardianship rules, the guardianship rules including a pre-defined geographic boundary in which the entity is to remain while under the care of the at least one TG. When executed, the instructions further cause the server to extract location metadata of the entity from the sensor data, and based at least in part on the metadata, send notifications to the TG and to a primary guardian (PG) of the entity when the entity is outside of the pre-defined boundary.

Abstract: Technologies for context-aware dynamic bandwidth allocation include a network compute device configured to collect context inputs from a plurality of compute devices communicatively coupled to the network compute device. The network compute device is further configured to identify a context of each compute device based on the collected context inputs and determine a bandwidth priority for each compute device based on the identified context. Additionally, the network compute device is configure to determine an amount of bandwidth from a total available bandwidth to allocate to the compute device based on the determined bandwidth priority and update a moderated bandwidth allocation policy to reflect the determined amount of bandwidth allocated to the compute device. Other embodiments are described herein.

Abstract: Methods and apparatus to execute a workload in an edge environment are disclosed. An example apparatus includes a node scheduler to accept a task from a workload scheduler, the task including a description of a workload and tokens, a workload executor to execute the workload, the node scheduler to access a result of execution of the workload and provide the result to the workload scheduler, and a controller to access the tokens and distribute at least one of the tokens to at least one provider, the provider to provide a resource to the apparatus to execute the workload.

Abstract: A compute system that includes an Internet of things (IoT) device is provided. The IoT device includes a common services interface (CSI) to create a self-managing network of devices with other nodes comprising the CSI.

Abstract: In embodiments, one or more non-transitory computer-readable storage media comprise a set of instructions, which, when executed on a processor of a server, causes the server to receive sensor data from at least one sensor proximate to an entity, the entity is a human under care of at least one temporary guardian (TG) pursuant to a set of guardianship rules, the guardianship rules including a pre-defined geographic boundary in which the entity is to remain while under the care of the at least one TG. When executed, the instructions further cause the server to extract location metadata of the entity from the sensor data, and based at least in part on the metadata, send notifications to the TG and to a primary guardian (PG) of the entity when the entity is outside of the pre-defined boundary.

Abstract: Embodiments include apparatuses, systems, and methods for a computer-aided or autonomous driving (CA/AD) system to transmit to a remote service scheduling server, a request message for a service slot at a service provider. In embodiments, the remote service scheduling server may assign the service slot for a semi-autonomous or autonomous driving (SA/AD) vehicle including the CA/AD system. In embodiments, the service scheduling server may perform analysis or machine learning on data received from the CA/AD system as well as from a plurality of service stations in order to assign the service provider and/or service slot to the SA/AD vehicle. In embodiments, the CA/AD system may receive an assignment message and control driving elements of the SA/AD vehicle to autonomously or semi-autonomously drive the SA/AD vehicle to the service provider to receive the service at the assigned service slot. Other embodiments may also be described and claimed.

Abstract: Systems, methods, and computer-readable media are provided for managing mutual and transitive trust relationships between resources, such as Fog/Edge nodes, autonomous devices (e.g., IoT devices), and/or analog/biological resources to provide collaborative, trusted communication over a network for service delivery. Disclosed embodiments include a subject resource configured to assign an observed resource to a trust zone based on situational and contextual information. The situational information may indicate a vector of the observed resource with respect to the subject resource. The contextual information may be based in part on whether a relationship exists between the subject resource and the observed resource. The subject resource is configured to determine a trust level of the observed resource based on the determined trust zone. Other embodiments are disclosed and/or claimed.

Abstract: Technologies for deploying virtual machines (VMs) in a virtual network function (VNF) infrastructure include a compute device configured to collect a plurality of performance metrics based on a set of key performance indicators, determine a key performance indicator value for each of the set of key performance indicators based on the collected plurality of performance metrics, and determine a service quality index for a virtual machine (VM) instance of a plurality of VM instances managed by the compute as a function each key performance indicator value. Additionally, the compute device is configured to determine whether the determined service quality index is acceptable and perform, in response to a determination that the determined service quality index is not acceptable, an optimization action to ensure the VM instance is deployed on an acceptable host of the compute device. Other embodiments are described herein.

Abstract: Technologies for context-aware dynamic bandwidth allocation include a network compute device configured to collect context inputs from a plurality of compute devices communicatively coupled to the network compute device. The network compute device is further configured to identify a context of each compute device based on the collected context inputs and determine a bandwidth priority for each compute device based on the identified context. Additionally, the network compute device is configure to determine an amount of bandwidth from a total available bandwidth to allocate to the compute device based on the determined bandwidth priority and update a moderated bandwidth allocation policy to reflect the determined amount of bandwidth allocated to the compute device. Other embodiments are described herein.

Abstract: In some embodiments, the disclosed subject matter is an assistance system with a wearable assistive device that mitigates the effects of neuro-muscular ailments such as unintended motion, or loss of strength. The assistance system uses predictive analysis based on situational, operational, and historical contexts, when in active/predictive mode. When in reactive triode, the assistive device mitigates unintended motion without altering the strength of the user. The assistance system may have an exercise mode to both assess the user's strength and flexibility of various muscles and joints, and promote exercises to either avoid further losses, or to maintain current strength and flexibility. The assistance system utilizes sensor data from sensors coupled to the assistive device, and optionally from sensors coupled to mobile devices and in the environment. Actuators on the assistive device control movement of the device based on inferred intended actions or reactive to unintended movement.

Abstract: System and techniques for radio link quality prediction are described herein. A mobile device may receive a device registration. Motion data for the mobile device may then be obtained. A predicted path for the mobile device may be derived from the motion data. A set of predicted radio metrics for the mobile device along the predicted path mat be produced via a dynamic coverage map. The set of predicted radio metrics may then be transmitted.