Abstract: Adjusting workload execution based on workload similarity. A processor may determine a similarity of a first workload to a second workload. The processor may adjust execution of the first workload based on execution parameters of the second workload and the similarity of the first workload to the second workload.

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: 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: A DTaaS architecture is described to support communication-side optimization and to reduce communication overhead while meeting necessary reliability and latency requirements. The disclosure describe techniques for utilizing DT resources for V2X environments using both virtual and physical “twin” resources to achieve improved resiliency. Moreover, to optimize redundancy costs, the ratio of virtual DT nodes to physical DT nodes may be asymmetrical. An asymmetric approach to DT redundancies involving both virtual and physical resources enables greater flexibility in managing deployment costs.

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: Embodiments of systems, apparatuses and methods provide enhanced function as a service (FaaS) to users, e.g., computer developers and cloud service providers (CSPs). A computing system configured to provide such enhanced FaaS service include one or more controls architectural subsystems, software and orchestration subsystems, network and storage subsystems, and security subsystems. The computing system executes functions in response to events triggered by the users in an execution environment provided by the architectural subsystems, which represent an abstraction of execution management and shield the users from the burden of managing the execution. The software and orchestration subsystems allocate computing resources for the function execution by intelligently spinning up and down containers for function code with decreased instantiation latency and increased execution scalability while maintaining secured execution.

Abstract: Example apparatus to perform service failover as disclosed herein are to detect a failure condition associated with execution of a service by a first compute platform, the execution of the service responsive to a first request. Disclosed example apparatus are also to send a second request to a second compute platform to execute the service. Disclosed example apparatus are further to monitor a queue of the first compute platform for a response to the first request, the response to indicate execution of the service by the first compute platform has completed, and when the response is detected in the queue, discard the response from the queue.

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: Embodiments may be generally directed to techniques to cause communication of a registration request between a first end-point and a second end-point of an end-to-end path, the registration request to establish resource load monitoring for one or more resources of the end-to-end path, receive one or more acknowledgements indicating resource loads for each of the one or more resources of the end-to-end path, at least one of the acknowledgements to indicate a resource of the one or more resources is not meeting a threshold requirement for the end-to-end path, and perform an action for communication traffic utilizing the one or more resources based on the acknowledgement.

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: A controller of a processor core may select telemetry data generated by a plurality of sensors of the processor core at a first time interval of a plurality of time intervals. The controller may transform the telemetry data based at least in part on a model. The controller may detect a change at the first time interval based on the transformed telemetry data. The controller may determine an event based on the change. The controller may initiate an action during the first time interval based on the event.

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: Technologies for function as a service (FaaS) arbitration include an edge gateway, multiple endpoint devices, and multiple service providers. The edge gateway receives a registration request from a service provider that is indicative of an FaaS function identifier and a transform function. The edge gateway verifies an attestation received from the service provider and registers the service provider. The edge gateway receives a function execution request from an endpoint device that is indicative of the FaaS function identifier. The edge gateway selects the service provider based on the FaaS function identifier, programs an accelerator with the transform function, executes the transform function with the accelerator to transform the function execution request to a provider request, and submits the provider request to the service provider. The service provider may be selected based on an expected service level included in the function execution request. Other embodiments are described and claimed.

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: 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: 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: 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: A method is described. The method includes dispatching jobs across electronic hardware components. The electronic hardware components are to process the jobs. The electronic hardware components are coupled to respective cooling systems. The respective cooling systems are each capable of cooling according to different cooling mechanisms. The different cooling mechanisms have different performance and cost operating realms. The dispatching of the jobs includes assigning the jobs to specific ones of the electronic hardware components to keep the cooling systems operating in one or more of the realms having lower performance and cost than another one of the realms.