Abstract: Technologies for encrypted data access by field-programmable gate array (FPGA) user kernels include a computing device having an FPGA and an external memory device accessible by the FPGA. The FPGA includes a secure key store, a micro-encryption engine, and multiple slots for user kernels that are each identifiable with an index. A user kernel is programmed at an index and a symmetric encryption key is provisioned to the secure key store at the index. The micro encryption engine may read encrypted data from the external memory device, decrypt the encrypted data with the key associated with the index of the user kernel, and forward plain text data to the user kernel. The micro encryption engine may also receive plain text data from the user kernel, encrypt the plain text data with the key, and write the encrypted data to the external memory device. Other embodiments are described and claimed.

Abstract: Technologies for providing accelerated functions as a service in a disaggregated architecture include a compute device that is to receive a request for an accelerated task. The task is associated with a kernel usable by an accelerator sled communicatively coupled to the compute device to execute the task. The compute device is further to determine, in response to the request and with a database indicative of kernels and associated accelerator sleds, an accelerator sled that includes an accelerator device configured with the kernel associated with the request. Additionally, the compute device is to assign the task to the determined accelerator sled for execution.

Abstract: Technologies for dividing work across one or more accelerator devices include a compute device. The compute device is to determine a configuration of each of multiple accelerator devices of the compute device, receive a job to be accelerated from a requester device remote from the compute device, and divide the job into multiple tasks for a parallelization of the multiple tasks among the one or more accelerator devices, as a function of a job analysis of the job and the configuration of each accelerator device. The compute engine is further to schedule the tasks to the one or more accelerator devices based on the job analysis and execute the tasks on the one or more accelerator devices for the parallelization of the multiple tasks to obtain an output of the job.

Abstract: Technologies for offloading acceleration task scheduling operations to accelerator sleds include a compute device to receive a request from a compute sled to accelerate the execution of a job, which includes a set of tasks. The compute device is also to analyze the request to generate metadata indicative of the tasks within the job, a type of acceleration associated with each task, and a data dependency between the tasks. Additionally the compute device is to send an availability request, including the metadata, to one or more micro-orchestrators of one or more accelerator sleds communicatively coupled to the compute device. The compute device is further to receive availability data from the one or more micro-orchestrators, indicative of which of the tasks the micro-orchestrator has accepted for acceleration on the associated accelerator sled. Additionally, the compute device is to assign the tasks to the one or more micro-orchestrators as a function of the availability data.

Abstract: Embodiments detailed herein include an apparatus that includes a reliability assessment engine (RAE) stored in non-volatile memory and processing circuitry to execute the RAE to: receive data of at least one physical condition from a plurality of intra-die variation monitoring circuits, apply the received data least one to at least one reliability physics model, and calculate at least one of an estimated amount of lifetime consumed and an estimated amount of lifetime remaining.

Abstract: Embodiments detailed herein include an apparatus that includes a reliability assessment engine (RAE) stored in non-volatile memory and processing circuitry to execute the RAE to: receive data of at least one physical condition from a plurality of intra-die variation monitoring circuits, apply the received data least one to at least one reliability physics model, and calculate at least one of an estimated amount of lifetime consumed and an estimated amount of lifetime remaining.

Abstract: Examples may include techniques to a schedule a workload to one or more computing resources of a data center. A class is determined for the workload based on a workload type or profile for the workload. Predicted operating values for at least one of the one or more computing resources is determined based on the class and the predicted operating values are used as inputs in at least one scoring model to evaluate the workload being supported by the at least one of the one or more computing resources. The workload is then scheduled to the at least one or more computing resources based on the evaluation.

Abstract: Technologies for virtual multipath access include a computing device configured to sequester a recovery partition from a host partition while allowing the recovery partition to access one or more resources of the host partition such as host memory or data storage. A remote computing device determines whether the host partition is responsive. The recovery partition receives a request for host state data of the host partition from the remote computing device in response to a determination that the host partition is not responsive. The recovery partition retrieves the requested host state data using a host state index maintained by the host partition and transmits the requested host state data to the remote computing device. The host state index may identify the location of the requested host state data. The remote computing device may perform a recovery operation based on the received host state data. Other embodiments are described and claimed.

Abstract: Technologies for predicting the power usage of a data center are disclosed. A data center manager gathers sensor data from the compute devices of the data center. The sensor data indicates factors such as power used by the compute device and the intake air inlet temperature. The data center manager trains a machine-learning-based algorithm based on training sensor data, and then applies the machine-learning-based algorithm to sensor data as it is being gathered. The machine-learning-based algorithm can predict a change in future power usage of the data center, and control a cooling unit to compensate before the power usage even begins to change.

Abstract: Technologies for dynamically allocating resources among a set of managed nodes include an orchestrator server to receive telemetry data from the managed nodes indicative of resource utilization and workload performance by the managed nodes as the workloads are executed, generate a resource allocation map indicative of allocations of resources among the managed nodes, determine, as a function of the telemetry data and the resource allocation map, a dynamic adjustment to allocation of resources to at least one of the managed nodes to improve performance of at least one of the workloads executed on the at least one of the managed nodes, and apply the adjustment to the allocation of the resources among the managed nodes as the workloads are executed. Other embodiments are also described and claimed.

Abstract: Technologies for determining and storing workload characteristics include an orchestrator server to identify a workload to be executed by a managed node, obtain a profile associated with the workload, wherein the profile includes a model that relates an input parameter set indicative of one of more characteristics of the workload with an output parameter set indicative of one or more aspects of resources to be allocated for execution of the workload, determine, as a function of the input parameter set and the model, resources to allocate to the managed node to execute the workload, and allocate the determined resources to the managed node to execute the workload. Other embodiments are also described and claimed.

Abstract: Technologies for performing orchestration with online analytics of telemetry data include an orchestrator server to assign workloads to each of a set of managed nodes, receive telemetry data indicative of resource utilization from the managed nodes as the workloads are performed, generate data analytics as a function of the telemetry data as the workloads are performed, determine, as a function of the data analytics, adjustments to the workload assignments to increase resource utilization among the managed nodes as the workloads are performed, and apply the determined adjustments to the managed nodes as the workloads are performed. Other embodiments are also described and claimed.

Abstract: Technologies for dynamically managing resources in disaggregated accelerators include an accelerator. The accelerator includes acceleration circuitry with multiple logic portions, each capable of executing a different workload. Additionally, the accelerator includes communication circuitry to receive a workload to be executed by a logic portion of the accelerator and a dynamic resource allocation logic unit to identify a resource utilization threshold associated with one or more shared resources of the accelerator to be used by a logic portion in the execution of the workload, limit, as a function of the resource utilization threshold, the utilization of the one or more shared resources by the logic portion as the logic portion executes the workload, and subsequently adjust the resource utilization threshold as the workload is executed. Other embodiments are also described and claimed.

Abstract: Technologies for assigning workloads based on resource utilization phases include an orchestrator server to assign a set of workloads to the managed nodes. The orchestrator server is also to receive telemetry data from the managed nodes and identify, as a function of the telemetry data, historical resource utilization phases of the workloads. Further, the orchestrator server is to determine, as a function of the historical resource utilization phases and as the workloads are performed, predicted resource utilization phases for the workloads, and apply, as a function of the predicted resources utilization phases, adjustments to the assignments of the workloads among the managed nodes as the workloads are performed.

Abstract: Technologies for allocating resources of a set of managed nodes to workloads based on resource utilization phase residencies include an orchestrator server to receive resource allocation objective data and determine an assignment of a set of workloads among the managed nodes. The orchestrator server is further to receive telemetry data from the managed nodes, determine, as a function of the telemetry data, phase residency data, determine, as a function of at least the phase residency data and the resource allocation objective data, an adjustment to the assignment of the workloads to increase an achievement of at least one of the resource allocation objectives without decreasing the achievement of any of the other resource allocation objectives, and apply the adjustment to the assignments of the workloads among the managed nodes as the workloads are performed.

Abstract: Technologies for allocating resources of a set of managed nodes to workloads to manage heat generation include an orchestrator server to receive resource allocation objective data including a target temperature for one or more of the managed nodes. The orchestrator server is also to determine an initial assignment of a set of workloads among the managed nodes, receive telemetry data from the managed nodes indicative of resource utilization by each of the managed nodes and one or more temperatures and fan speeds of the managed nodes as the workloads are performed, predict future heat generation of the workloads as a function of the telemetry data, determine, as a function of the predicted future heat generation, an adjustment to the assignment of the workloads to achieve the target temperature, and apply the adjustments to the assignments of the workloads among the managed nodes as the workloads are performed.

Abstract: Technologies for allocating resources of a set of managed nodes to workloads with a hierarchical model include an orchestrator server to receive resource allocation objective data.

Abstract: Technologies for dynamically managing the allocation of accelerator resources include an orchestrator server. The orchestrator server is to assign a workload to a managed node for execution, determine a predicted demand for one or more accelerator resources to accelerate the execution of one or more jobs within the workload, provision, prior to the predicted demand, one or more accelerator resources to accelerate the one or more jobs, and allocate the one or more provisioned accelerator resources to the managed node to accelerate the execution of the one or more jobs. Other embodiments are also described and claimed.

Abstract: Technologies for identifying managed nodes available for workload assignments include an orchestrator server to assign workloads to the managed nodes and receive availability data from the managed nodes, indicative of a determination by each of the managed nodes as to an availability of the managed node to receive an additional workload. The orchestrator server is also to receive telemetry data from the managed nodes, indicative of resource utilization by each of the managed nodes as the workloads are performed. The orchestrator server is also to determine, as a function of the availability data, a reduced set of available managed nodes for analysis, determine, as a function of the telemetry data, adjustments to the workload assignments to increase the resource utilization among the reduced set of managed nodes, and apply the determined adjustments to the reduced set of managed nodes as the workloads are performed.

Abstract: Technologies for allocating resources of managed nodes to workloads to balance multiple resource allocation objectives include an orchestrator server to receive resource allocation objective data indicative of multiple resource allocation objectives to be satisfied. The orchestrator server is additionally to determine an initial assignment of a set of workloads among the managed nodes and receive telemetry data from the managed nodes. The orchestrator server is further to determine, as a function of the telemetry data and the resource allocation objective data, an adjustment to the assignment of the workloads to increase an achievement of at least one of the resource allocation objectives without decreasing an achievement of another of the resource allocation objectives, and apply the adjustments to the assignments of the workloads among the managed nodes as the workloads are performed. Other embodiments are also described and claimed.