Abstract: Technologies for accelerated orchestration and attestation include multiple edge devices. An edge appliance device performs an attestation process with each of its components to generate component certificates. The edge appliance device generates an appliance certificate that is indicative of the component certificates and a current utilization of the edge appliance device and provides the appliance certificate to a relying party. The relying party may be an edge orchestrator device. The edge orchestrator device receives a workload scheduling request with a service level agreement requirement. The edge orchestrator device verifies the appliance certificate and determines whether the service level agreement requirement is satisfied based on the appliance certificate. If satisfied, the workload is scheduled to the edge appliance device. Attestation and generation of the appliance certificate by the edge appliance device may be performed by an accelerator of the edge appliance device.

Abstract: Detailed are embodiments related to bit matrix multiplication in a processor. For example, in some embodiments a processor comprising: decode circuitry to decode an instruction have fields for an opcode, an identifier of a first source bit matrix, an identifier of a second source bit matrix, an identifier of a destination bit matrix, and an immediate; and execution circuitry to execute the decoded instruction to perform a multiplication of a matrix of S-bit elements of the identified first source bit matrix with S-bit elements of the identified second source bit matrix, wherein the multiplication and accumulation operations are selected by the operation selector and store a result of the matrix multiplication into the identified destination bit matrix, wherein S indicates a plural bit size is described.

Abstract: Technologies for providing streamlined provisioning of accelerated functions in a disaggregated architecture include a compute sled. The compute sled includes a network interface controller and circuitry to determine whether to accelerate a function of a workload executed by the compute sled, and send, to a memory sled and in response to a determination to accelerate the function, a data set on which the function is to operate. The circuitry is also to receive, from the memory sled, a service identifier indicative of a memory location independent handle for data associated with the function, send, to a compute device, a request to schedule acceleration of the function on the data set, receive a notification of completion of the acceleration of the function, and obtain, in response to receipt of the notification and using the service identifier, a resultant data set from the memory sled. The resultant data set was produced by an accelerator device during acceleration of the function on the data set.

Abstract: Technologies for providing attestation for function as a service flavors include a compute device including circuitry configured to obtain function definition data indicative of a set of operations to be performed in a function and a set of hardware resources to be utilized by the function, execute a benchmark operation to produce benchmark data indicative of a measured performance of the function, and sign the function definition data and the benchmark data to produce function flavor data. The circuitry is also configured to provide the function flavor data to one or more other compute devices for validation that the function, when executed on the hardware resources, provides the measured performance and write, to a distributed ledger, the function flavor data.

Abstract: A compute device includes one or more processors, one or more resources capable of being utilized by the one or more processors, and a platform interconnect to facilitate communication of messages between the one or more processors and the one or more resources. The compute device is to obtain class of service data for one or more workloads to be executed by the compute device. The class of service data is indicative of a capacity of one or more of the resources to be utilized in the execution of each corresponding workload. The compute device is also to execute the one or more workloads and manage the amount of traffic transmitted through the platform interconnect for each corresponding workload as a function of the class of service data as the one or more workloads are executed.

Abstract: Switching architectures to manage mutex primitives used to control access to objects or data blocks that are being processed by two or more microservices in a data center are provided.

Abstract: Technologies for providing a multi-tenant local breakout switching and dynamic load balancing include a network device to receive network traffic that includes a packet associated with a tenant. Upon a determination that the packet is encrypted, a secret key associated with the tenant is retrieved. The network device decrypts a payload from the packet using the secret key. The payload is indicative of one or more characteristics associated with network traffic. The network device evaluates the characteristics and determines whether the network traffic is associated with a workload requesting compute from a service hosted by a network platform. If so, the network device forwards the network traffic to the service.

Abstract: Examples include techniques to manage training or trained models for deep learning applications. Examples include routing commands to configure a training model to be implemented by a training module or configure a trained model to be implemented by an inference module. The commands routed via out-of-band (OOB) link while training data for the training models or input data for the trained models are routed via inband links.

Abstract: A computing apparatus, including: a hardware computing platform; and logic to operate on the hardware computing platform, configured to: receive a microservice instance registration for a microservice accelerator, wherein the registration includes a microservice that the microservice accelerator is configured to provide, and a microservice connection capability indicating an ability of the microservice instance to communicate directly with other instances of the same or a different microservice; and log the registration in a microservice registration database.

Abstract: Methods and apparatus to adaptively manage data collection devices in distributed computing systems are disclosed. Example disclosed methods involve instructing a first data collection device to operate according to a first rule. The example first rule specifies a first operating mode and defining a first event of interest. Example disclosed methods also involve obtaining first data from the first data collection device while operating according to the first rule. Example disclosed methods also involve, in response to determining that the first event of interest has occurred based on the first data, providing a second rule based on the first data to the first data collection device, and providing a third rule to a second data collection device. The example second rule specifies a second operating mode and defines a second event of interest, and the examples third rule specifies a third operating mode.

Abstract: An apparatus comprising a memory to store an observed trajectory of a pedestrian, the observed trajectory comprising a plurality of observed locations of the pedestrian over a first plurality of timesteps; and a processor to generate a predicted trajectory of the pedestrian, the predicted trajectory comprising a plurality of predicted locations of the pedestrian over the first plurality of timesteps and over a second plurality of timesteps occurring after the first plurality of timesteps; determine a likelihood of the predicted trajectory based on a comparison of the plurality of predicted locations of the pedestrian over the first plurality of timesteps and the plurality of observed locations of the pedestrian over the first plurality of timesteps; and responsive to the determined likelihood of the predicted trajectory, provide information associated with the predicted trajectory to a vehicle to warn the vehicle of a potential collision with the pedestrian.

Abstract: A computing apparatus, including: a hardware computing platform; and logic to operate on the hardware computing platform, configured to: receive a microservice instance registration for a microservice accelerator, wherein the registration includes a microservice that the microservice accelerator is configured to provide, and a microservice connection capability indicating an ability of the microservice instance to communicate directly with other instances of the same or a different microservice; and log the registration in a microservice registration database.

Abstract: Examples include techniques to manage training or trained models for deep learning applications. Examples include routing commands to configure a training model to be implemented by a training module or configure a trained model to be implemented by an inference module. The commands routed via out-of-band (OOB) link while training data for the training models or input data for the trained models are routed via inband links.

Abstract: Technologies for batching requests in an edge infrastructure include a compute device including circuitry configured to obtain a request for an operation to be performed at an edge location. The circuitry is also configured to determine, as a function of a parameter of the obtained request, a batch that the obtained request is to be assigned to. The batch includes a one or more requests for operations to be performed at an edge location. The circuitry is also configured to assign the batch to a cloudlet at an edge location. The cloudlet includes a set of resources usable to execute the operations requested in the batch.

Abstract: Technologies for network packet processing between cloud and telecommunications networks includes a network computing device which includes two application layer packet translators (ALPTs). The first ALPT is configured to receive a network packet from a computing device in a telecommunications network, identify a virtual network function (VNF) instance, and perform an application layer encapsulation of at least a portion of data of the received network packet as a parameter of a remote procedure call (RPC) associated with the identified VNF instance. The first ALPT is additionally configured to invoke the identified VNF instance using an API call corresponding to the RPC that includes the RPC parameter and the VNF instance is configured to transmit an RPC call response to the second ALPT. The second ALPT is configured to generate a new network packet as a function of the RPC call response and transmit the new network packet to another computing device in a cloud network.

Abstract: Technologies for accelerated orchestration and attestation include multiple edge devices. An edge appliance device performs an attestation process with each of its components to generate component certificates. The edge appliance device generates an appliance certificate that is indicative of the component certificates and a current utilization of the edge appliance device and provides the appliance certificate to a relying party. The relying party may be an edge orchestrator device. The edge orchestrator device receives a workload scheduling request with a service level agreement requirement. The edge orchestrator device verifies the appliance certificate and determines whether the service level agreement requirement is satisfied based on the appliance certificate. If satisfied, the workload is scheduled to the edge appliance device. Attestation and generation of the appliance certificate by the edge appliance device may be performed by an accelerator of the edge appliance device.

Abstract: Technologies for providing attestation for function as a service flavors include a compute device including circuitry configured to obtain function definition data indicative of a set of operations to be performed in a function and a set of hardware resources to be utilized by the function, execute a benchmark operation to produce benchmark data indicative of a measured performance of the function, and sign the function definition data and the benchmark data to produce function flavor data. The circuitry is also configured to provide the function flavor data to one or more other compute devices for validation that the function, when executed on the hardware resources, provides the measured performance and write, to a distributed ledger, the function flavor data.

Abstract: An apparatus for training artificial intelligence (AI) models is presented. In embodiments, the apparatus may include an input interface to receive in real time model training data from one or more sources to train one or more artificial neural networks (ANNs) associated with the one or more sources, each of the one or more sources associated with at least one of the ANNs; a load distributor coupled to the input interface to distribute in real time the model training data for the one or more ANNs to one or more AI appliances; and a resource manager coupled to the load distributor to dynamically assign one or more computing resources on ones of the AI appliances to each of the ANNs in view of amounts of the training data received in real time from the one or more sources for their associated ANNs.

Abstract: Technologies for providing adaptive utilization of different interconnects for workloads include a compute device. The compute device includes a connection abstraction logic unit to determine a quality of service target to be satisfied in the execution of a workload that is to communicate with at least one other workload through one or more interconnects of a set of interconnects associated with the compute device, determine a quality of service property of each interconnect of the set of interconnects, and allocate, as a function of the determined quality of service property of each interconnect, one or more of the set of interconnects to the workload to satisfy the quality of service target. The compute device also includes circuitry to execute the workload and communicate with the at least one other workload through the allocated one or more interconnects. Other embodiments are also described and claimed.

Abstract: Examples described herein relate to dynamically composing an application as a monolithic implementation or two or more microservices based on telemetry data. In some examples, based on composition of an application as two or more microservices, at least one connection between microservices based on telemetry data is adjusted. In some examples, a switch can be configured to perform forwarding of communications between microservices based on the adjusted at least one connection between microservices.