Abstract: Various examples of the present disclosure relate to an apparatus, device, method, and computer program for a network element, to a corresponding network element and to a system.

Abstract: Systems, apparatus, and methods to workload optimize hardware are disclosed herein. An example apparatus includes power control circuitry to determine an application ratio based on an instruction to be executed by one or more cores of a processor to execute a workload, and configure, before the execution of the workload, at least one of (i) the one or more cores of the processor based on the application ratio or (ii) uncore logic of the processor based on the application ratio, and execution circuitry to execute the workload with the at least one of the one or more cores or the uncore logic.

Abstract: An apparatus of a wireless device, such as a user equipment (UE), can include processing circuitry configured to perform one or more of the handover-related techniques disclosed herein. For example, when associated with moving with a plurality of mobile devices from coverage of a first cell to a second cell, the processing circuitry can detect the second cell. One or more parameters of the second cell can be measured. The one or more parameters can be communicated to one or more other mobile devices of the plurality of mobile devices.

Abstract: Various approaches for the deployment and use of communication exclusion zones, defined for use with a satellite non-terrestrial network (including within a low-earth orbit satellite constellation), are discussed. In an example, defining and implementing a non-terrestrial communication exclusion zone includes: calculating based on a future orbital position of a low-earth orbit satellite vehicle, an exclusion condition for communications from the satellite vehicle; identifying, based on the exclusion condition and the future orbital position, a timing for implementing the exclusion condition for the communications from the satellite vehicle; and generating exclusion zone data for use by the satellite vehicle, the exclusion zone data indicating the timing for implementing the exclusion condition for the communications from the satellite vehicle.

Abstract: An apparatus of a wireless device, such as a user equipment (UE), can include processing circuitry configured to perform one or more of the handover-related techniques disclosed herein. For example, when associated with moving with a plurality of mobile devices from coverage of a first cell to a second cell, the processing circuitry can detect the second cell. One or more parameters of the second cell can be measured. The one or more parameters can be communicated to one or more other mobile devices of the plurality of mobile devices.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for hardware queue scheduling for multi-core computing environments. An example apparatus includes a first core and a second core of a processor, and circuitry in a die of the processor, at least one of the first core or the second core included in the die, the at least one of the first core or the second core separate from the circuitry, the circuitry to enqueue an identifier to a queue implemented with the circuitry, the identifier associated with a data packet, assign the identifier in the queue to a first core of the processor, and in response to an execution of an operation on the data packet with the first core, provide the identifier to the second core to cause the second core to distribute the data packet.

Abstract: An apparatus of a wireless device, such as a user equipment (UE), can include processing circuitry configured to perform one or more of the handover-related techniques disclosed herein. For example, when associated with moving with a plurality of mobile devices from coverage of a first cell to a second cell, the processing circuitry can detect the second cell. One or more parameters of the second cell can be measured. The one or more parameters can be communicated to one or more other mobile devices of the plurality of mobile devices.

Abstract: It is provided a head restraint arrangement for a vehicle seat, with a head restraint which is adjustable along two spatial axes and with an actuating device, by means of which at least one locking device which, in a locked state, blocks the head restraint against adjustment in at least one spatial direction along one of the spatial axes, is manually actuatable in order to remove a block and to permit an adjustment of the head restraint. The actuating device comprises an actuating element which is provided on the head restraint and, for actuation of the locking device, is mounted displaceably along at least one of the spatial axes.

Abstract: An apparatus of a wireless device, such as a user equipment (UE), can include processing circuitry configured to perform one or more of the handover-related techniques disclosed herein. For example, when associated with moving with a plurality of mobile devices from coverage of a first cell to a second cell, the processing circuitry can detect the second cell. One or more parameters of the second cell can be measured. The one or more parameters can be communicated to one or more other mobile devices of the plurality of mobile devices.

Abstract: It is provided a head restraint arrangement for a vehicle seat, with a head restraint which is adjustable along two spatial axes and with an actuating device, by means of which at least one locking device which, in a locked state, blocks the head restraint against adjustment in at least one spatial direction along one of the spatial axes, is manually actuatable in order to remove a block and to permit an adjustment of the head restraint. The actuating device comprises an actuating element which is provided on the head restraint and, for actuation of the locking device, is mounted displaceably along at least one of the spatial axes.

Abstract: Technologies for load-aware traffic steering include a compute device that includes a multi-homed network interface controller (NIC) with a plurality of NICs. The compute device determines a target virtual network function (VNF) of a plurality of VNFs to perform a processing operation on a network packet. The compute device further identifies a first steering point of a first NIC to steer the received network packet to virtual machines (VMs) associated with the target VNF and retrieves a resource utilization metric that corresponds to a usage level of a component of the compute device used by the VMs to process the network packet. Additionally, the compute device determines whether the resource utilization metric indicates a potential overload condition and provides a steering instruction to a second steering point of a second NIC that is usable to redirect the network traffic to the other VMs via the identified second steering point.

Abstract: A method to reduce memory power consumption for a computing platform includes inspecting an operating parameter associated with a resource of the computing platform that is updated by the resource of the computing platform during runtime of the computing platform. Memory power utilization is then predicted for the computing platform during the runtime of the computing platform based at least in part on the operating parameter. A current power state of at least one memory module resident on the computing platform is transitioned to one of a plurality of power states based on the predicting of the memory power utilization.

Abstract: A method according to one embodiment may include discovering, by software, at least one variable from at least one component populated on a shelf system. The method may also include performing, by the software, at least one shelf management function based on at least one variable. Of course, many alternatives, variations, and modifications are possible without departing from this embodiment.

Abstract: A method according to one embodiment may include discovering, by software, at least one variable from at least one component populated on a shelf system. The method may also include performing, by the software, at least one shelf management function based on at least one variable. Of course, many alternatives, variations, and modifications are possible without departing from this embodiment.

Abstract: A method according to one embodiment may include discovering, by software, at least one variable from at least one component populated on a shelf system. The method may also include performing, by the software, at least one shelf management function based on at least one variable. Of course, many alternatives, variations, and modifications are possible without departing from this embodiment.

Abstract: A method according to one embodiment may include discovering, by software, at least one variable from at least one component populated on a shelf system. The method may also include performing, by the software, at least one shelf management function based on at least one variable. Of course, many alternatives, variations, and modifications are possible without departing from this embodiment.

Abstract: A method according to one embodiment may include discovering, by software, at least one variable from at least one component populated on a shelf system. The method may also include performing, by the software, at least one shelf management function based on at least one variable. Of course, many alternatives, variations, and modifications are possible without departing from this embodiment.

Abstract: In some embodiments a system includes one or more processing nodes, a backplane, and one or more links to couple the one or more processing nodes to the backplane, wherein at least one of the one or more links is configurable as a standard Input/Output link and/or as a proprietary link. Other embodiments are described and claimed.

Abstract: A method is to include implementing at least one statistical prediction model to predict memory power utilization and reduce power consumption for a computing platform. The implementation includes determining a configuration parameter for the computing platform, monitoring an operating parameter for the computing platform and predicting memory power utilization for the computing platform based on the determined configuration parameter and the monitored operating parameter. The method is to also include transitioning at least one memory module resident on the computing platform to one of a plurality of power states based at least in part on memory power utilization predicted via the implementation of the at least one statistical prediction model.

Abstract: A method is to include implementing at least one statistical prediction model to predict memory power utilization and reduce power consumption for a computing platform. The implementation includes determining a configuration parameter for the computing platform, monitoring an operating parameter for the computing platform and predicting memory power utilization for the computing platform based on the determined configuration parameter and the monitored operating parameter. The method is to also include transitioning at least one memory module resident on the computing platform to one of a plurality of power states based at least in part on memory power utilization predicted via the implementation of the at least one statistical prediction model.