Provided are a computer program product, system, and method for using at least one machine learning module to select a priority queue from which to process an Input/Output (I/O) request. Input I/O statistics are provided on processing of I/O requests at the queues to at least one machine learning module. Output is received from the at least one machine learning module for each of the queues. The output for each queue indicates a likelihood that selection of an I/O request from the queue will maintain desired response time ratios between the queues. The received output for each of the queues is used to select a queue of the queues. An I/O request from the selected queue is processed.

An arbiter may include a plurality of cells, mapping logic, a fixed priority arbiter, and unmapping logic. Each cell may be associated with a corresponding client and configured to store a priority for the corresponding client. The mapping logic may be connected to the plurality of cells to order requests received from the clients according to the priorities stored in the cells. The fixed priority arbiter may receive the ordered requests and generate a grant for a winning request of the requests. The unmapping logic may use the stored priorities to yield the grant back to the winning client that sent the winning request.

A command is received from a first computer. The command is to transfer a data from the first computer to a second computer. One or more ports of the second computer are determined that are available for the data transfer. A ranking is determined for the one or more ports. The first computer is notified of one or more data transfer ports of the one or more ports. The one or more data transfer ports are above a threshold in the determined ranking.

During operation, the system receives, by a master node, a first I/O request with associated data, wherein the master node is in communication with a first plurality of storage drives via a switch based on a network protocol, wherein the master node and the first plurality of storage drives are allowed to reside in different cabinets, and wherein a respective collection of storage drives are coupled to a converter module, which is configured to convert data between the network protocol and an I/O protocol used to access the storage drives. The system identifies, by the master node, a first collection of storage drives from the first plurality on which to execute the first I/O request. The system executes, based on a communication via the switch and a converter module associated with the first collection of storage drives, the first I/O request on the first collection of storage drives.

Provided are a computer program product, system, and method for using at least one machine learning module to select a priority queue from which to process an Input/Output (I/O) request. Input I/O statistics are provided on processing of I/O requests at the queues to at least one machine learning module. Output is received from the at least one machine learning module for each of the queues. The output for each queue indicates a likelihood that selection of an I/O request from the queue will maintain desired response time ratios between the queues. The received output for each of the queues is used to select a queue of the queues. An I/O request from the selected queue is processed.

An arbiter may include a plurality of cells, mapping logic, a fixed priority arbiter, and unmapping logic. Each cell may be associated with a corresponding client and configured to store a priority for the corresponding client. The mapping logic may be connected to the plurality of cells to order requests received from the clients according to the priorities stored in the cells. The fixed priority arbiter may receive the ordered requests and generate a grant for a winning request of the requests. The unmapping logic may use the stored priorities to yield the grant back to the winning client that sent the winning request.

In one embodiment, an apparatus comprises: an endpoint circuit to perform an endpoint operation on behalf of a host processor; and an input/output circuit coupled to the endpoint circuit to receive telemetry information from the endpoint circuit, encode the telemetry information into a virtual bus encoding, place the virtual bus encoding into a payload field of a control message, and communicate the control message having the payload field including the virtual bus encoding to an upstream device. Other embodiments are described and claimed.

Provided are a computer program product, system, and method for using at least one machine learning module to select a priority queue from which to process an Input/Output (I/O) request. Input I/O statistics are provided on processing of I/O requests at the queues to at least one machine learning module. Output is received from the at least one machine learning module for each of the queues. The output for each queue indicates a likelihood that selection of an I/O request from the queue will maintain desired response time ratios between the queues. The received output for each of the queues is used to select a queue of the queues. An I/O request from the selected queue is processed.

An information processing device includes a memory, and a plurality of processor cores that access the memory. The plurality of processor cores respectively executes processes to be executed by the plurality of processor cores in accordance with execution priority levels of the processes. When a polling process for repeatedly determining whether reception data for input/output processing is received is underway in one of the plurality of processor cores, the plurality of processor cores respectively executes the input/output processing in response to a determination, made by the polling process, that the reception data have been received, and when the polling process is not underway in any of the plurality of processing cores, the plurality of processor cores respectively executes the input/output processing in response to a processor interrupt issued upon reception of the reception data.

A data processing system includes a memory, a group of input/output (I/O) devices, an input/output memory management unit (IOMMU). The IOMMU is connected to the memory and adapted to allocate a hardware resource from among a group of hardware resources to receive an address translation request for a memory access from an I/O device. The IOMMU detects address translation requests from the plurality of I/O devices. The IOMMU reorders the address translation requests such that an order of dispatching an address translation request is based on a policy associated with the I/O device that is requesting the memory access. The IOMMU selectively allocates a hardware resource to the input/output device, based on the policy that is associated with the I/O device in response to the reordering.