Abstract: Embodiments described herein are generally directed to the use of sidecars to perform dynamic API contract generation and conversion. In an example, a first sidecar of a source microservice intercepts a first call to a first API exposed by a destination microservice. The first call makes use of a first API technology specified by a first contract and is originated by the source microservice. An API technology is selected from multiple API technologies. The selected API technology is determined to be different than the first API technology. Based on the first contract, a second contract is dynamically generated that specifies an intermediate API that makes use of the selected API technology. A second sidecar of the destination microservice is caused to generate the intermediate API and connect the intermediate API to the first API.

Abstract: Embodiments described herein are generally directed to the use of sidecars to perform dynamic Application Programming Interface (API) contract generation and conversion. In an example, a first call by a first microservice to a first API of a second microservice is intercepted by a first sidecar of the first microservice. The first API is of a first API type of multiple API types and is specified by a first contract. An API type of the multiple API types is selected by the first sidecar. Responsive to determining the selected API type differs from the first API type, based on the first contract, a second contract is generated by the first sidecar specifying a second API of the selected API type; and a second sidecar of the second microservice is caused to generate the second API and internally connect the second API to the first API based on the second contract.

Abstract: Examples relate to a computer system, a telemetry hub apparatus, a telemetry hub device, a telemetry hub method, a microservice apparatus, a microservice device, a microservice method and to corresponding computer programs. The telemetry apparatus is configured to obtain telemetry information from a plurality of microservices, and to provide access to the telemetry information for the plurality of microservices according to an access scheme.

Abstract: Various examples relate to an apparatus, device, method, and a non-transitory machine-readable storage medium for a node of a blockchain network. The apparatus comprises interface circuitry, machine-readable instructions and processor circuitry to execute the machine-readable instructions to compare a traffic pattern of requests associated with one or more smart contracts hosted by the node of the blockchain network with a reference traffic pattern, determine an estimated denial of service of at least one of the one or more smart contracts based on the comparison between the traffic pattern and the reference traffic pattern, determine one or more potential mitigations for the estimated denial of service, and apply at least one of the one or more potential mitigations.

Abstract: Embodiments described herein are generally directed to a transparent and adaptable mechanism for performing secure application communications through sidecars. In an example, a set of security features is discovered by a first sidecar of a first microservice of multiple microservices of an application. The set of security features are associated with a device of multiple devices of a set of one or more host systems on which the first microservice is running. Information regarding the set of discovered security features is made available to the other microservices by the first sidecar by sharing the information with a discovery service accessible to all of the microservices. A configuration of a communication channel through which a message is to be transmitted from a second microservice to the first microservice is determined by a second sidecar of the second microservice by issuing a request to the discovery service regarding the first microservice.

Abstract: Embodiments described herein are generally directed to the use of sidecars to perform dynamic API contract generation and conversion. In an example, a first call by a first microservice to a first API of a second microservice is intercepted by a first sidecar of the first microservice. The first API is of a first API type of multiple API types and is specified by a first contract. An API type of the multiple API types is selected by the first sidecar. Responsive to determining the selected API type differs from the first API type, based on the first contract, a second contract is generated by the first sidecar specifying a second API of the selected API type; and a second sidecar of the second microservice is caused to generate the second API and internally connect the second API to the first API based on the second contract.

Abstract: An apparatus to facilitate matchmaking-based enhanced debugging for microservices architectures is disclosed. The apparatus includes one or more processors to: detect, by an anomaly detector in a sidecar of a microservice hosted by a container, an anomaly in telemetry data generated by the microservice, the microservice hosted in a container executed by the processor and part of a service of an application; enable, by an enhanced debug and trace component of the sidecar, a debug mode in the microservice, the debug mode based on a type of the anomaly; collect, by the enhanced debug and trace component, a target set of data points generated by the microservice; and process, by the enhanced debug and trace component, the target set of data points with a matchmaking process to generate a timestamp and a tag for a context for each data point of the target set of data points.

Abstract: Embodiments described herein are generally directed to assigning virtual machine (VM) workloads to groupings/partitions of accelerator resources. In an example, a processing resource of a host system maintains: (i) a resource data structure containing resource utilization information for each of one or more accelerators associated with the host system; and (ii) a group data structure containing information regarding each group of multiple groups of one or more virtual functions (VFs) of the one or more accelerators that has been assigned for use by a respective VM of multiple VMs running on a virtual machine monitor (VMM) of the processing resource. A request to deploy a workload associated with a first VM is received. Responsive to the request, the workload is assigned to a VF of a group of the multiple groups determined to have resource capacity available to satisfy expected resource utilization of the workload.

Abstract: Various approaches for the integration and use of edge computing operations in satellite communication environments are discussed herein. For example, connectivity and computing approaches are discussed with reference to: identifying satellite coverage and compute operations available in low earth orbit (LEO) satellites, establishing connection streams via LEO satellite networks, identifying and implementing geofences for LEO satellites, coordinating and planning data transfers across ephemeral satellite connected devices, service orchestration via LEO satellites based on data cost, handover of compute and data operations in LEO satellite networks, and managing packet processing, among other aspects.

Abstract: Various approaches for deploying and using virtual pools of compute resources with the use of infrastructure processing units (IPUs) and similar networked processing units are disclosed. A host computing system may be configured to operate a virtual pool of resources, with operations including: identifying, at the host computing system, availability of a resource at the host computing system; transmitting, to a network infrastructure device, a notification that the resource at the host computing system is available for use in a virtual resource pool in the edge computing network; receiving a request for the resource in the virtual resource pool that is provided on behalf of a client computing system, based on the request being coordinated via the network infrastructure device and includes at least one quality of service (QoS) requirement; and servicing the request for the resource, based on the at least one QoS requirement.

Abstract: Various approaches for deploying and controlling distributed compute operations with the use of infrastructure processing units (IPUs) and similar networked processing units are disclosed. For example, a request to verify integrity of a device is received at a networking infrastructure device. A representation of device components of the device may be obtained. The representation of the device components may be compared with a reference value held by the networking infrastructure device. A response to the request may be transmitted based on matching the representation of the device components and the reference value. Here, the response indicates that the integrity of the device is intact.

Abstract: Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes processor circuitry to execute computer readable instructions to: execute a machine learning model to generate a first code recommendation for programming code, the first code recommendation being associated with security of the programming code; cause output of the first code recommendation via a user interface; update the machine learning model based on feedback obtained via the user interface; determine a performance of the programming code; generate a second code recommendation, the second code recommendation being associated with the performance of the programming code; and cause output of the second code recommendation via the user interface.

Abstract: Systems, apparatuses and methods may provide for origination camera technology that generates a cell representation of a local space associated with an origination camera in a multicast domain, predicts that an object in the local space will exit the local space and enter one or more adjacent spaces associated with additional cameras in the multicast domain, and sends the cell representation and a trajectory of the object to the additional cameras before the object exits the local space. Additionally, transition camera technology may generate a leader election message based on a multicasted trajectory of an object and a predicted trajectory of the object, send the leader election message from a transition camera to one or more additional cameras in a multicast domain, and track the object in the local space in response to a leader notification message.

Abstract: Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes a feature extractor to convert compiled code into a first feature vector; a first machine leaning model to identify a cluster of stored feature vectors corresponding to the first feature vector; and a second machine learning model to recommend a second algorithm corresponding to a second feature vector of the cluster based on a comparison of a parameter of a first algorithm corresponding to the first feature vector and the parameter of the second algorithm.

Abstract: System and techniques for an environmental control loop are described herein. A device for an environmental control loop can include a memory including instructions and processing circuitry that when in operation, can be configured by the instructions to receive environmental sensor data from a first component in a set of heterogeneous components installed in an environment with a controller. The environmental sensor data can indicate a service level value sensed by the first component. The controller can also measure a violation of a service level objective based on comparing the environmental sensor data to a threshold. The controller can also transmit an adjustment to an operating parameter of a second component of the set of heterogeneous components. The adjustment can be operative to attenuate the violation of the service level objective when implemented by the second component.

Abstract: Systems, apparatuses and methods may provide for technology that detects an unidentified individual at a first location along a trajectory in a scene based on a video feed of the scene, wherein the video feed is to be associated with a stationary camera, and selects a non-stationary camera from a plurality of non-stationary cameras based on the trajectory and one or more settings of the selected non-stationary camera. The technology may also automatically instruct the selected non-stationary camera to adjust at least one of the one or more settings, capture a face of the individual at a second location along the trajectory, and identify the unidentified individual based on the captured face of the unidentified individual.

Abstract: An apparatus and system are described to provide indoor positioning and movement information using a private next generation (NG) network. A heatmap of pathloss vs distance from a remote radio unit (RRU) is provided from the UE and federated with other heatmaps from different UEs under similar conditions. The federated heatmap is provided to the UE. A private location server containing an AI module is trained using data from the UEs. The location and movement of the UE is determined to a particular pixel based on the heatmap. WiFi reference points (RP) are used if multiple pixels satisfy data of the heatmap.

Abstract: The application relates to an autonomous recommendation system, including: one or more BESs each configured to generate anonymous data representing customer-related information based on content streams from product-related cameras installed in a branch corresponding to the BES, and provide recommendation content to the branch based on the anonymous data; one or more ECSs each configured to aggregate the anonymous data from one or more BESs corresponding to respective branches in a region corresponding to the ECS to generate aggregated anonymous data shareable among regions corresponding to respective ECSs; and a DO configured to coordinate operations of the BESs and the ECSs based on predefined policies.

Abstract: Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes a feature extractor to convert compiled code into a first feature vector; a first machine leaning model to identify a cluster of stored feature vectors corresponding to the first feature vector; and a second machine learning model to recommend a second algorithm corresponding to a second feature vector of the cluster based on a comparison of a parameter of a first algorithm corresponding to the first feature vector and the parameter of the second algorithm.

Abstract: Systems, apparatuses and methods may provide for technology that identifies a sequence of events associated with a computer architecture, categorizes, with a natural language processing system, the sequence of events into a sequence of words, identifying an anomaly based on the sequence of words and triggering an automatic remediation process in response to an identification of the anomaly.