Abstract: Examples described herein relate to a system. In some examples, the system includes an interface and circuitry, coupled to the interface. In some examples, the circuitry, when operational, is to: based on detection of multiple management controllers, select a primary management controller and a secondary management controller from among the multiple management controllers. In some examples, the primary management controller is to perform at least one different operation than that of the secondary management controller, the primary management controller comprises a baseboard management controller (BMC), the secondary management controller comprises a BMC, and the multiple management controllers are positioned in at least one programmable network interface device and a host system.

Abstract: Various approaches for implementing platform resource management are described. In an edge computing system deployment, an edge computing device includes processing circuitry coupled to a memory. The processing circuitry is configured to obtain, from an orchestration provider, an SLO (or SLA) that defines usage of an accessible feature of the edge computing device by a container executing on a virtual machine within the edge computing system. A computation model is retrieved based on at least one key performance indicator (KPI) specified in the SLO. The defined usage of the accessible feature is mapped to a plurality of feature controls using the retrieved computation model. The plurality of feature controls is associated with platform resources of the edge computing device that are pre-allocated to the container. The usage of the platform resources allocated to the container is monitored using the plurality of feature controls.

Abstract: Examples include techniques for artificial intelligence (AI) capabilities at a network switch. These examples include receiving a request to register a neural network for loading to an inference resource located at the network switch and loading the neural network based on information included in the request to support an AI service to be provided by users requesting the AI service.

Abstract: Technologies for dynamically sharing remote resources include a computing node that sends a resource request for remote resources to a remote computing node in response to a determination that additional resources are required by the computing node. The computing node configures a mapping of a local address space of the computing node to the remote resources of the remote computing node in response to sending the resource request. In response to generating an access to the local address, the computing node identifies the remote computing node based on the local address with the mapping of the local address space to the remote resources of the remote computing node and performs a resource access operation with the remote computing node over a network fabric. The remote computing node may be identified with system address decoders of a caching agent and a host fabric interface. Other embodiments are described and claimed.

Abstract: Systems and techniques for AI model and data camouflaging techniques for cloud edge are described herein. In an example, a neural network transformation system is adapted to receive, from a client, camouflaged input data, the camouflaged input data resulting from application of a first encoding transformation to raw input data. The neural network transformation system may be further adapted to use the camouflaged input data as input to a neural network model, the neural network model created using a training data set created by applying the first encoding transformation on training data. The neural network transformation system may be further adapted to receive a result from the neural network model and transmit output data to the client, the output data based on the result.

Abstract: In an example, there is disclosed a host-fabric interface (HFI), including: an interconnect interface to communicatively couple the HFI to an interconnect; a network interface to communicatively couple the HFI to a network; network interface logic to provide communication between the interconnect and the network; a coprocessor configured to provide an offloaded function for the network; a memory; and a caching agent configured to: designate a region of the memory as a shared memory between the HFI and a core communicatively coupled to the HFI via the interconnect; receive a memory operation directed to the shared memory; and issue a memory instruction to the memory according to the memory operation.

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: Technologies for dynamic allocation of network communication resources includes a resource manager server to allocate a set of network communication resources to a compute device through an expansion bus switch that is coupled to the compute device and to the network communication resources. The resource manager server obtains telemetry data indicative of a present utilization of the allocated set of network communication resources and determines whether the present utilization satisfies a predefined utilization threshold. Additionally, the resource manager server adjusts, through the expansion bus switch and in response to a determination that the present utilization does not satisfy the predefined utilization threshold, an amount of network communication resources in the set to the compute device.

Abstract: Software and other electronic services are increasingly being executed in cloud computing environments. Edge computing environments may be used to bridge the gap between cloud computing environments and end-user software and electronic devices, and may implement Functions-as-a-Service (FaaS). FaaS may be used to create flavors of particular services, a chain of related functions that implements all or a portion of a FaaS edge workflow or workload. A FaaS Temporal Software-Defined Wide-Area Network (SD-WAN) may be used to receive a computing request and decompose the computing request into several FaaS flavors, enable dynamic creation of SD-WANs for each FaaS flavor, execute the FaaS flavors in their respective SD-WAN, return a result, and destroy the SD-WANs. The FaaS Temporal SD-WAN expands upon current edge systems by allowing low-latency creation of SD-WAN virtual networks bound to a set of function instances that are created to a execute a particular service request.

Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed. A first example apparatus disclosed herein is an autonomous mobile radio access network (RAN) node that includes communication circuitry, instructions, and programmable circuitry to cause the communication circuitry to transmit a workload to a server via a network, initiate local processing of the workload after a loss of connectivity with the server, and move the autonomous mobile RAN node from a first location to a second location. A second example apparatus disclosed herein is an autonomous mobile RAN node that includes communication circuitry, instructions, and programmable circuitry to cause communication of a workload from a client device to a server to process the workload, identify a second location relative to a first location of the compute device based on network performance, and cause the autonomous mobile RAN node to move from the first location to the second location.

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: Examples described herein relate to a network interface device that includes a network interface, one or more processors, and circuitry to: register the network interface device and based on selection as an attestation device by the management controller from among multiple candidate network interface devices, receive attestation information and perform attestation of one or more devices.

Abstract: Various aspects of methods, systems, and use cases include edge resource management, such as of a processor of an edge device. The edge device may include a processor to execute an application and a device including an interface to the processor and a network interface. The device may include circuitry to monitor a status of the processor; and based on the status and the application having an associated requirement, initiate a migration of execution of the application from the processor.

Abstract: A computing cluster can receive a request to perform a workload from a client. The request can include a service discovery agent. If the request is authenticated and permitted on the computing cluster, the service discovery agent is executed. Execution of the service discovery agent can lead to discovery of resource capabilities of the cluster and selection of the appropriate resource based on performance requirements. The selected resource can be deployed for execution of the workload.

Abstract: Examples herein relate to an interface selectively providing access to a memory region for a work request from an entity by providing selective access to a physical address of the memory region and selective access to a cryptographic key for use by a memory controller to access the memory region. In some examples, providing selective access to a physical address conversion is based on one or more of: validation of a certificate received with the work request and an identifier of the entity being associated with a process with access to the memory region. Access to the memory region can be specified to be one or more of: create, read, update, delete, write, or notify. A memory region can be a page or sub-page sized region. Different access rights can be associated with different sub-portions of the memory region, wherein the access rights comprise one or more of: create, read, update, delete, write, or notify.

Abstract: Data management for edge architected computing systems extends current storage and memory schemes of edge resources to expose interfaces to allow a device, such as an endpoint or client device, or another edge resource, to specify criteria for managing data originating from the device and stored in an edge resource, and extends the storage and memory controllers to manage data in accordance with the criteria, including removing stored data that no longer satisfies the criteria. The criteria includes a temporal hint to specify a time after which the data can be removed, a physical hint to specify a list of edge resources outside of which the data can be removed, an event-based hint to specify an event after which the data can be removed, and a quality of service condition to modify the time specified in the temporal hint based on a condition, such as memory and storage capacity of the edge resource in which the data is managed.

Abstract: An embodiment of an electronic apparatus may include one or more substrates, and logic coupled to the one or more substrates, the logic to allocate a first memory portion to a first application as a combination of a local memory and remote memory, wherein the remote memory is shared between multiple compute nodes, and manage a first memory balloon associated with the first memory portion based on two or more memory tiers associated with the local memory and the remote memory. Other embodiments are disclosed and claimed.

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: Methods, apparatus and systems for adaptive fabric allocation for local and remote emerging memories-based prediction schemes. In conjunction with performing memory transfers between a compute host and memory device connected via one or more interconnect segments, memory read and write traffic is monitored for at least one interconnect segment having reconfigurable upstream lanes and downstream lanes. Predictions of expected read and write bandwidths for the at least one interconnect segment are then made. Based on the expected read and write bandwidths, the upstream lanes and downstream lanes are dynamically reconfigured. The interconnect segments include interconnect links such as Compute Exchange Link (CXL) flex buses and memory channels for local memory implementations, and fabric links for remote memory implementations. For local memory, management messages may be used to provide telemetry information containing the expected read and write bandwidths.

Abstract: Embodiments for automatically tuning heterogenous wireless networks are disclosed herein. In one example, performance data is received for multiple wireless networks. The wireless networks are based on multiple wireless technologies, and the performance data is based on multiple layers of the protocol stacks of the wireless technologies. The performance data is used to determine one or more configuration settings to adjust for one or more of the wireless networks. The determined configuration setting(s) are then adjusted.