Technologies for variable extent storage include multiple computing devices in communication over an optical fabric. A computing device receives a key-value storage request from an application that is indicative of a key. The computing device identifies one or more non-volatile storage blocks to store a value associated with the key and issues a non-volatile memory (NVM) input/output (I/O) command indicative of the NVM storage blocks to an NVM subsystem. The key-value storage request may include a read request or a store request, and the I/O command may include a read command or a write command. The I/O command may be issued to an NVM subsystem over the optical fabric. The computing device may be embodied as a storage sled of a data center, and the application may be executed by a compute sled of the data center. Other embodiments are described and claimed.

Technologies for low-latency compression in a data center are disclosed. In the illustrative embodiment, a storage sled compresses data with a low-latency compression algorithm prior to storing the data. The latency of the compression algorithm is less than the latency of the storage device, so that the latency of the storage and retrieval times are not significantly affected by the compression and decompression. In other embodiments, a compute sled may compress data with a low-latency compression algorithm prior to sending the data to a storage sled.

Technologies for enhanced memory wear leveling is disclosed. In the illustrative embodiment, a storage controller on a storage sled performs wear leveling across several storage devices. For example, the storage controller may copy hot data from one storage device that has a high number of erasures to another storage device that has a lower number of erasures in order to make the number of erasures between the devices more even by accumulating further erasures associated with the hot data on the drive that has the lower number of erasures.

Examples may include techniques to allocate physical accelerator resources from pools of accelerator resources. In particular, virtual computing devices can be composed from physical resources and physical accelerator resources dynamically allocated to the virtual computing devices. The present disclosure provides that physical accelerator resources can be dynamically allocated, or composed, to a virtual computing device despite not being physically coupled to other components in the virtual device.

Technologies for accelerating data writes include a managed node that includes a network interface controller that includes a power loss protected buffer and non-volatile memory. The managed node is to receive, through the network interface controller, a write request from a remote device. The write request includes a data block. The managed node is also to write the data block to the power loss protected buffer of the network interface controller, and send, in response to receipt of the data block and prior to a write of the data block to the non-volatile memory, an acknowledgement to the remote device. The acknowledgement is indicative of a successful write of the data block to the non-volatile memory. The managed node is also to write, after the acknowledgement has been sent, the data block from the power loss protected buffer to the non-volatile memory. Other embodiments are also described and claimed.

Examples may include a sled for a rack of a data center including physical storage resources. The sled comprises an array of storage devices and an array of memory. The storage devices and memory are directly coupled to storage resource processing circuits which are themselves, directly coupled to dual-mode optical network interface circuitry. The circuitry can store data on the storage devices and metadata associated with the data on non-volatile memory in the memory array.

Technologies for storage block virtualization include multiple computing devices in communication over an optical fabric. A computing device receives a non-volatile memory (NVM) I/O command from an application via an optical fabric interface. The NVM I/O command is indicative of one or more virtual data storage blocks. The computing device maps the virtual data storage blocks to one or more physical data storage blocks, each of which is included in a solid-state data storage device of the computing device. The computing device performs the I/O command with the physical data storage blocks and then sends a response to the application. Mapping the virtual data storage blocks may include performing one or more data services. The computing device may be embodied as a storage sled of a data center, and the application may be executed by a compute sled of the data center. Other embodiments are described and claimed.

Technologies for managing partially synchronized writes include a managed node. The managed node is to issue a write request to write a data block, on behalf of a workload, to multiple data storage devices connected to a network, pause execution of the workload, receive an initial acknowledgment associated with one of the multiple data storage devices, wherein the initial acknowledgement is indicative of successful storage of the data block, and resume execution of the workload after receipt of the initial acknowledgement and before receipt of subsequent acknowledgements associated with any of the other data storage devices. Other embodiments are also described and claimed.

Embodiments are generally directed to apparatuses, method, techniques, and so forth including a memory coupled to processing circuitry, wherein the memory stores a firmware interface table and the firmware interface table comprises an entry to identify a non-enumerable resource. Embodiments include accessing the firmware interface table to identify the non-enumerable resource.

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.