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: An apparatus to facilitate provenance audit trails for microservices architectures is disclosed. The apparatus includes one or more processors to obtain provenance metadata for a microservice from a local blockchain of provenance metadata maintained for the hardware resource executing a task performed by the microservice, the provenance metadata comprising identification of the microservice, operating state of at least one of a hardware resource or a software resource used to execute the microservice and the task, and an operating state of a sidecar of the microservice during the task; access one or more policies established for the microservice; analyze the provenance metadata with respect to the one or more policies to identify if there is a violation of the one or more policies; and generate one or more evaluation metrics based on whether the violation of the one or more policies is identified.

Abstract: It is proposed an apparatus, the apparatus comprising interface circuitry, machine-readable instructions, and a semiconductor die comprising processing circuitry to execute the machine-readable instructions to obtain data associated with the processing circuitry, and store the data in non-volatile memory integrated into the semiconductor die.

Abstract: In some examples, an apparatus uses a blockchain to agree on a time in an information exchange network. A first node includes a processor communicatively coupled to a storage device including instructions. When executed by the processor, the instructions cause the processor to verify a time estimate from each of one or more other node, to determine a time match of a time estimate of the first node with the time estimates from the one or more other node, and if the time match is determined, to commit to the blockchain a transaction that includes a time stamp.

Abstract: A mechanism is described for facilitating misuse index for explainable artificial intelligence in computing environments, according to one embodiment. A method of embodiments, as described herein, includes mapping training data with inference uses in a machine learning environment, where the training data is used for training a machine learning model. The method may further include detecting, based on one or more policy/parameter thresholds, one or more discrepancies between the training data and the inference uses, classifying the one or more discrepancies as one or more misuses, and creating a misuse index listing the one or more misuses.

Abstract: An apparatus is proposed, the apparatus comprising interface circuitry, machine-readable instructions, and processing circuitry to execute the machine-readable instructions to receive a request to execute a task on a computing system, receive a service-level agreement, SLA, indicating at least one of a desired computing performance and a desired computing power for an execution of the task by the computing system, determine an interdependency between at least two resources of the computing system required for the execution of the task based on the SLA and schedule the execution of the task based on the interdependency between the at least two resources.

Abstract: A system for trust brokering as a service includes an edge computing node and a trust brokering service edge computing device. The trust brokering service edge computing device receives a computing workload request from an application configured to process secure data and identifies a set of security requirements associated with the request. The device also identifies a security feature present in the set of security requirements but not provided by the edge computing node. To address this, the device generates an application execution environment that includes a secure plugin providing the security feature and a virtual device representing the edge computing node. The computing workload request is then executed at the application execution environment, providing a secure and efficient solution for trust brokering as a service.

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: Methods and apparatus for secured information transfer are disclosed. An example apparatus includes programmable circuitry to execute instructions to determine characteristics of an asset associated with a first entity that utilizes a first type of decentralized security, assign the asset to a carrier for transport to a second entity that utilizes a second type of decentralized security, obtain attested information for the asset from the carrier, and transmit the attested information to the second entity via a first gateway, the first gateway to transmit the attested information for the asset to a second gateway of the second entity.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: An apparatus to facilitate provenance audit trails for microservices architectures is disclosed. The apparatus includes one or more processors to: obtain, by a microservice of a service hosted in a datacenter, provisioned credentials for the microservice based on an attestation protocol; generate, for a task performed by the microservice, provenance metadata for the task, the provenance metadata including identification of the microservice, operating state of at least one of a hardware resource or a software resource used to execute the microservice and the task, and operating state of a sidecar of the microservice during the task; encrypt the provenance metadata with the provisioned credentials for the microservice; and record the encrypted provenance metadata in a local blockchain of provenance metadata maintained for the hardware resource executing the task and the microservice.

Abstract: An apparatus to facilitate telemetry targeted query injection for enhanced debugging in microservices architectures is disclosed. The apparatus includes one or more processors to: identify contextual trace of a previous query recorded in collected data of a service, where microservices of the service responded to the previous query; access an interdependency flow graph representing an architecture and interaction of microservices deployed for a service; retrieve, based on the interdependency flow graph, telemetry data of the microservices corresponding to the contextual trace; identify, based on the telemetry data, an activation profile corresponding to the previous query, the activation profile detailing a response of the microservices to the previous query; compare the activation profile to a correlation profile for the previous query to detect whether an anomaly occurred in the service in response to the previous query; and recommend a modified query based on detection of the anomaly.

Abstract: Systems, apparatuses, methods, and computer-readable media, are provided for providing connectivity-based and/or connectivity-considered routing with supplemental wireless connections in driving assistance-related activities. Embodiments may be relevant to multi-access edge computing (MEC) and Automotive Edge Computing Consortium (AECC) technologies. Other embodiments may be described and/or claimed.

Abstract: A service coordinating entity device includes communications circuitry to communicate with a first access network, processing circuitry, and a memory device. The processing circuitry is to perform operations to, in response to a request for establishing a connection with a user equipment (UE) in a second access network, retrieve a first Trusted Level Agreement (TLA) including trust attributes associated with the first access network. One or more exchanges of the trust attributes of the first TLA and trust attributes of a second TLA associated with the second access network are performed using a computing service executing on the service coordinating entity. A common TLA with trust attributes associated with communications between the first and second access networks is generated based on the exchanges. Data traffic is routed from the first access network to the UE in the second access network based on the trust attributes of the common TLA.

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, apparatus, systems and articles of manufacture (e.g., physical storage media) to perform data quality assessment and learning for automated agents are disclosed. An example apparatus disclosed herein includes processor circuitry to calculate a data quality score for data generated by sensors of an autonomous agent. The processor circuitry also generates a reputation score based on the data quality score and the data generated by the sensors. The reputation score indicates a level of confidence in an accuracy of the data quality score. Usage of the data by an action circuitry of the autonomous agent is controlled based on the data quality score and the reputation score. The data quality score and the reputation score are a first value and a second value, respectively.

Abstract: A mechanism is described for facilitating misuse index for explainable artificial intelligence in computing environments, according to one embodiment. A method of embodiments, as described herein, includes mapping training data with inference uses in a machine learning environment, where the training data is used for training a machine learning model. The method may further include detecting, based on one or more policy/parameter thresholds, one or more discrepancies between the training data and the inference uses, classifying the one or more discrepancies as one or more misuses, and creating a misuse index listing the one or more misuses.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Abstract: Logic may determine a specific performance of a neural network based on an event and may present the specific performance to provide a user with an explanation of the inference by a machine learning model such as a neural network. Logic may determine a first activation profile associated with the event, the first activation profile based on activation of nodes in one or more layers of the neural network during inference to generate an output. Logic may correlate the first activation profile against a second activation profile associated with a first training sample of training data. Logic may determine that the first training sample is associated with the event based on the correlation. Logic may output an indicator to identify the first training sample as being associated with the event.

Abstract: Embodiments described herein are generally directed to intelligent management of microservices failover. In an example, responsive to an uncorrectable hardware error associated with a processing resource of a platform on which a task of a service is being performed by a primary microservice, a failover trigger is received by a failover service. A secondary microservice is identified by the failover service that is operating in lockstep mode with the primary microservice. The secondary microservice is caused by the failover service to takeover performance of the task in non-lockstep mode based on failover metadata persisted by the primary microservice. The primary microservice is caused by the failover service to be taken offline.