Abstract: Technologies for managing telemetry and sensor data on an edge networking platform are disclosed. According to one embodiment disclosed herein, a device monitors telemetry data associated with multiple services provided in the edge networking platform. The device identifies, for each of the services and as a function of the associated telemetry data, one or more service telemetry patterns. The device generates a profile including the identified service telemetry patterns.

Abstract: There is disclosed in one example an application-specific integrated circuit (ASIC), including: an artificial intelligence (AI) circuit; and circuitry to: identify a flow, the flow including traffic diverted from a core cloud service of a network to be serviced by an edge node closer to an edge of the network than to the core of the network; receive telemetry related to the flow, the telemetry including fine-grained and flow-level network monitoring data for the flow; operate the AI circuit to predict, from the telemetry, a future service-level demand for the edge node; and cause a service parameter of the edge node to be tuned according to the prediction.

Abstract: Technologies for providing streamlined provisioning of accelerated functions in a disaggregated architecture include a compute sled. The compute sled includes a network interface controller and circuitry to determine whether to accelerate a function of a workload executed by the compute sled, and send, to a memory sled and in response to a determination to accelerate the function, a data set on which the function is to operate. The circuitry is also to receive, from the memory sled, a service identifier indicative of a memory location independent handle for data associated with the function, send, to a compute device, a request to schedule acceleration of the function on the data set, receive a notification of completion of the acceleration of the function, and obtain, in response to receipt of the notification and using the service identifier, a resultant data set from the memory sled. The resultant data set was produced by an accelerator device during acceleration of the function on the data set.

Abstract: Telemetry information provided by a computing device includes switching key performance indicators (KPIs), platform KPIs, and topology information. The telemetry information is used to identify performance issues at the computing device, such as packets being dropped in a virtual switching stack or misconfiguration errors. A virtual switching monitor can identify which layers in the switching stack have errors and whether the errors occur along a transmit or receive path in the switching stack. A virtual switching controller can identify remedial actions that can be taken at the computing device to remedy a performance issue. A remedial action can be taken automatically, subject to user approval, or automatically after additional criteria are met.

Abstract: Systems and methods may be used to provide neutral host edge services in an edge network. An example method may include generating a virtual machine for a communication service provider at a compute device. The method may include receiving a user packet originated at a user device associated with the communication service provider and identifying dynamic route information related to the user packet using the virtual machine corresponding to the communication service provider. Data may be output corresponding to the user packet based on the dynamic route information.

Abstract: Various aspects of methods, systems, and use cases include coordinating actions at an edge device based on power production in a distributed edge computing environment. A method may include identifying a long-term service level agreement (SLA) for a component of an edge device, and determining a list of resources related to the component using the long-term SLA. The method may include scheduling a task for the component based on the long-term SLA, a current battery level at the edge device, a current energy harvest rate at the edge device, or an amount of power required to complete the task. A resource of the list of resources may be used to initiate the task, such as according to the scheduling.

Abstract: Methods and apparatus to verify trained models in edge environments are disclosed. An example apparatus to validate a trained model in an edge environment includes an attestation verifier to determine an attestation score of the model received at a first appliance, the attestation score calculated at a second appliance different from the first appliance, a comparator to compare the attestation score to a threshold, a validator to validate the model based on the comparison, and an executor to at least one of execute or deploy the model based on the validation.

Abstract: Technologies for providing efficient migration of services include a server device. The server device includes compute engine circuitry to execute a set of services on behalf of a terminal device and migration accelerator circuitry. The migration accelerator circuitry is to determine whether execution of the services is to be migrated from an edge station in which the present server device is located to a second edge station in which a second server device is located, determine a prioritization of the services executed by the server device, and send, in response to a determination that the services are to be migrated and as a function of the determined prioritization, data utilized by each service to the second server device of the second edge station to migrate the services. Other embodiments are also described and claimed.

Abstract: There is disclosed in one example a communication apparatus, including: a telemetry interface; a management interface; and an edge gateway configured to: identify diverted traffic, wherein the diverted traffic includes traffic to be serviced by an edge microcloud configured to provide a plurality of services; receive telemetry via the telemetry interface; use the telemetry to anticipate a future per-service demand within the edge microcloud; compute a scale for a resource to meet the future per-service demand; and operate the management interface to instruct the edge microcloud to perform the scale before the future per-service demand occurs.

Abstract: In one embodiment, an apparatus of an edge computing system includes memory that includes instructions and processing circuitry coupled to the memory. The processing circuitry implements the instructions to process a request to execute at least a portion of a workflow on pooled computing resources, the workflow being associated with a particular tenant, determine an amount of power to be allocated to particular resources of the pooled computing resources for execution of the portion of the workflow based on a power budget associated with the tenant and a current power cost, and control allocation of the determined amount of power to the particular resources of the pooled computing resources during execution of the portion of the workflow.

Abstract: Methods, apparatus, systems and articles of manufacture for re-use of a container in an edge computing environment are disclosed. An example method includes detecting that a container executed at an edge node of a cloud computing environment is to be cleaned, deleting user data from the container, the deletion of the user data performed without deleting the container from the memory of the edge node, restoring settings of the container to a default state; and storing information identifying the container, the information including a flavor of the container, the storing of the information to enable the container to be re-used by a subsequent requestor.

Abstract: Examples described herein relate to management of battery-use by one or more computing resources in the event of a power outage. Data used by one or more computing resources can be backed-up using battery power. Battery power is allocated to data back-up operations based at least on one or more of: criticality level of data, priority of an application that processes the data, or priority level of resource. The computing resource can back-up data to a persistent storage media. The computing resource can store a log of data that is backed-up or not backed-up. The log can be used by the computing resource to access the backed-up data for continuing to process the data and to determine what data is not available for processing.

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: Systems and techniques for intelligent data forwarding in edge networks are described herein. A request may be received from an edge user device for a service via a first endpoint. A time value may be calculated using a timestamp of the request. Motion characteristics may be determined for the edge user device using the time value. A response to the request may be transmitted to a second endpoint based on the motion characteristics.

Abstract: Methods, apparatus, systems and articles of manufacture to select a location of execution of a computation are disclosed. An example apparatus includes a cache digest interface to identify a node capable of performing a computation. A compute plan solver is to obtain a cost estimate of performing the computation from the node. Privacy weighting circuitry is to apply a privacy weighting value to the cost estimate to determine a weighted cost estimate. The compute plan solver is to select the node for performance of the computation based on the weighted cost estimate. A plan executor is to transmit a request for the selected node to perform the computation.

Abstract: Methods and apparatus to coordinate edge platforms are disclosed. A disclosed example apparatus includes to control processing of data associated with edges includes an orchestrator analyzer to determine a first performance requirement of a first microservice of an application and a second performance requirement of a second microservice of the application. The apparatus also includes an orchestrator controller to assign the first microservice and the second microservice across first and second edge nodes between a source network and a destination network by: assigning the first microservice to the first edge node based on a first capability of the first edge node satisfying the first performance requirement of the first microservice, and assigning the second microservice to the second edge node based on a second capability of the second edge node satisfying the second performance requirement of the second microservice.

Abstract: An apparatus to manage a data lake is disclosed. A disclosed example apparatus includes a location selector to select an edge device to store the data lake, a key generator to, in response to an indication that a service is authorized to access the data lake, generate an encryption key corresponding to the data lake and generate a key wrapping key corresponding to the edge device, and a key distributor to wrap the encryption key using the key wrapping key, and distribute the encryption key and the key wrapping key to the edge device, the encryption key to enable the service on the edge device to access the data lake.

Abstract: An example system to schedule service requests in a network computing system using hardware queue managers includes: a gateway-level hardware queue manager in an edge gateway to schedule the service requests received from client devices in a queue; a rack-level hardware queue manager in a physical rack in communication with the edge gateway, the rack-level hardware queue manager to send a pull request to the gateway-level hardware queue manager for a first one of the service requests; and a drawer-level hardware queue manager in a drawer of the physical rack, the drawer-level hardware queue manager to send a second pull request to the rack-level hardware queue manager for the first one of the service requests, the drawer including a resource to provide a function as a service specified in the first one of the service requests.

Abstract: Technologies for cache side channel attack detection and mitigation include an analytics server and one or more monitored computing devices. The analytics server polls each computing device for analytics counter data. The computing device generates the analytics counter data using a resource manager of a processor of the computing device. The analytics counter data may include last-level cache data or memory bandwidth data. The analytics server identifies suspicious core activity based on the analytics counter data and, if identified, deploys a detection process to the computing device. The computing device executes the detection process to identify suspicious application activity. If identified, the computing device may perform one or more corrective actions. Corrective actions include limiting resource usage by a suspicious process using the resource manager of the processor. The resource manager may limit cache occupancy or memory bandwidth used by the suspicious process.

Abstract: Methods and systems that allow a user to see the people or groups who have access to files that are maintained by a plurality of cloud content sharing services. In particular, the user may see what specific party has access to each particular file or directory, regardless of multiple cloud content sharing services involved. Moreover, a user interface and exposed application program interface allows the user to manipulate the permissions, e.g., granting access, to another person or group, to a file or directory. The user interface may also allow the user to terminate access to the file or directory for a person or group. The user's action to change a permission may be effected independently of the particular cloud content sharing service.