Abstract: An apparatus is provided. The apparatus comprises interface circuitry, machine-readable instructions, and processing circuitry to execute the machine-readable instructions to receive a request from a virtual machine to execute a task, receive a service-level agreement, SLA, from the virtual machine indicating a desired feature of scanning a computing resource to execute the task for errors, and scan the computing resource for errors based on the SLA.

Abstract: Various examples relate to an apparatus, device, method, and computer program for extending instructions sup-ported by a processor. The apparatus is configured to identify at least a part of a computer program targeting an instruction unsupported by a pre-defined set of instructions of an Instruction Set Architecture (ISA) of the processor. The apparatus is configured to extend the instructions supported by the processor, based on the targeted unsupported instruction. The apparatus is configured to execute the computer program.

Abstract: It is provided an apparatus comprising interface circuitry, machine-readable instructions, and processing circuitry to execute the machine-readable instructions. The machine-readable instructions comprise instructions to determine one or more configurable firmware variables of a computing system based on performance analysis data of the computing system executing a workload. The machine-readable instructions further comprise instructions to set the determined one or more configurable firmware variables of the computing system based on reference data. The machine-readable instructions further comprise instructions to control the computing system to apply the set firmware variables during run-time.

Abstract: It is provided an apparatus comprising interface circuitry, machine-readable instructions, and processing circuitry to execute the machine-readable instructions. The machine-readable instructions comprise instructions to receive a request of a virtual machine to access a resource of a network node, determine whether a number of available virtual functions associated to the resource falls below a predefined threshold and, if it is determined that the number of available virtual functions falls below the predefined threshold, emulate and/or para virtualize a physical function associated to the resource. The machine-readable instructions further comprise instructions to provide access to the resource via the emulated and/or para virtualized physical function for the virtual machine.

Abstract: Apparatus, articles of manufacture, and methods for managing processing units are disclosed. An example apparatus includes first processor circuitry to implement a central processing unit and second processor circuitry to perform at least one of first operations, second operations or third operations to obtain a resource request associated with a first workload; determine if a processing resource of a programmable network device is available to perform processing for the workload; determine if a second workload can be migrated from execution on the programmable network device; based on the determination that the second workload can be migrated, cause the second workload to be migrated; and cause the first workload to execute on the processing resource of the programmable network device.

Abstract: Examples relate to an apparatus, a device, a method, a computer program (or computer-readable medium) and computer system for determining presence of a noisy neighbor virtual machine. Some aspects of the present disclosure relate to an apparatus for a computer system, the apparatus comprising interface circuitry, machine-readable instructions, and processor circuitry to execute the machine-readable instructions to obtain performance information of one or more hardware performance measurement components of the computer system, determine, based on the performance information, a deviation of a utilization of the computer system from an expected utilization of the computer system, and determine presence of a first virtual machine having a workload that impacts a performance of one or more second virtual machines based on the deviation.

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: Various systems and methods for content adaptation based on seat position or occupant position in a vehicle are described herein. An example implementation for content adaptation based on seat position in a vehicle includes: obtaining sensor data, the sensor data including a seat position of a seat in the vehicle; identifying audiovisual content for output to a human occupant in the vehicle; identifying an occupant position of the human occupant, based on the seat position, for a user experience of the output of the audiovisual content; and cause one or more adjustments to the output of the audiovisual content in the vehicle, via an output device, based on the identified position of the human occupant.

Abstract: Systems, apparatuses and methods may provide for technology that enables, during a boot sequence, a first set of ranks in a memory module based on a battery status and a user interface and disables, during the boot sequence, a second set of ranks in the memory module based on the battery status and the user interface. The technology may also generate a map between a system address space and a first set of banks in the first set of ranks and exclude a second set of banks in the first set of ranks from the map.

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: Technologies for privacy-safe security policy evaluation are disclosed herein. An example apparatus includes at least one memory, and at least one processor to execute instructions to at least identify one or more non-sensitive parameters of a plurality of policy parameters and one or more sensitive parameters of the plurality of the policy parameters, the plurality of the policy parameters obtained from a computing device in response to a request from a cloud analytics server for the plurality of the policy parameters, encrypt the one or more sensitive parameters to generate encrypted parameter data in response to the identification of the one or more sensitive parameters, and transmit the encrypted parameter data to the cloud analytics server, the cloud analytics server to curry a security policy function based on one or more of the plurality of the policy parameters.

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.

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: Various examples relate to apparatuses, devices, methods and computer programs for a group leader and a group member of a group of nodes of a blockchain network. The apparatus for the group leader comprises interface circuitry, machine-readable instructions, and processor circuitry to execute the machine-readable instructions to manage a membership of nodes of the blockchain network in the group of nodes, perform or delegate blockchain-related computational activity on behalf of the group of nodes according to an energy criterion.

Abstract: An apparatus to facilitate at-scale telemetry using interactive matrix for deterministic microservices performance is disclosed. The apparatus includes one or more processors to: receive user input comprising an objective or task corresponding to scheduling a microservice for a service, wherein the objective or task may include QoS, SLO, ML feedback; identify interaction matrix components in an interaction matrix that match the objective or tasks for the microservice; identify knowledgebase components in knowledgebase that match the objective or tasks for the microservice; and determine a scheduling operation for the microservice, the scheduling operation to deploy the microservice in a configuration that is in accordance with the objective or task, wherein the configuration comprises a set of hardware devices and microservice interaction points determined based on the interaction matrix components and the knowledgebase components.

Abstract: Examples relate to a concept for software application container hardware resource allocation, and in particular to sidecar apparatuses, sidecar devices, methods for a software application container sidecars, a resource management controller apparatus, a resource management controller device, and corresponding computer programs and computer systems. A sidecar apparatus comprises interface circuitry, machine-readable instructions and processing circuitry to execute the machine-readable instructions to obtain information on hardware resources desired by a software application container from the software application container, and to provide a request for changing the hardware resources allocated to the software application container to another entity capable of influencing an allocation of hardware resources to the software application container.

Abstract: Managing scan detection of a component in a computing system includes detecting a scan interrupt, reading a scan register of the component, the scan register including a hashed identifier (ID) of the component; getting material vintage information of the component based at least in part on the hashed ID; and initiating a scan of the component based at least in part on the material vintage information to detect any defects in the component.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to optimize resources in edge networks. An example apparatus includes agent managing circuitry to invoke an exploration agent to identify platform resource devices, select a first one of the identified platform resource devices, and generate first optimization metrics for the workload corresponding to the first one of the identified platform resource devices, the first optimization metrics corresponding to a first path. The example agent is to further select a second one of the identified platform resource devices, generate second optimization metrics for the workload corresponding to the second one of the identified platform resource devices, the second optimization metrics corresponding to a second path.

Abstract: Various embodiments for dynamically generating an advertisement in a video stream are disclosed. In one embodiment, video stream content associated with a video stream for a user device is received. Video analytics data is obtained for the video stream content, which indicates a scene recognized in the video stream content. An advertisement to be generated and inserted into the video stream content is then selected based on the scene recognized in the video stream content, and an advertisement template for generating the selected advertisement is obtained. Video advertisement content corresponding to the advertisement is then generated based on the advertisement template and the video analytics data. The video advertisement content is then inserted into the video stream content, and the modified video stream content is transmitted to the user device.

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.