Abstract: An illustrative storage system provides distributed storage for use by containerized applications running in a container system. In some implementations, this includes converting data blocks of storage resources to distributed persistent storage such as thin-provisioned volumes for use by the containerized applications. In some implementations, the conversion includes using a device mapper to virtualize the data blocks of the storage resources to create a virtual storage pool backing datastore in which the thin-provisioned volumes are created.

Abstract: In some embodiments, storage operation routing in a container system may include receiving, by a container storage system executing on a node within a container system, a storage operation configured to be performed with respect to a storage resource; determining whether the storage resource is locally accessible at the node; and routing the storage operation based on the determining of whether the storage resource is locally accessible at the node. For example, the routing may be performed such that when the storage resource is not locally accessible at the node, the routing is by way of both a kernel space and a user space of the node, and when the storage resource is locally accessible at the node, the routing is contained within the kernel space of the node. Corresponding methods and systems are also disclosed.

Abstract: An example method for detecting and remediating anomalies in a container system by a storage system comprises detecting, by a container storage management system, a change in resources utilized on a volume of the container system by an application of the container system; determining, by the container storage management system and in response to the detecting, whether the change in resources utilized is anomalous for the application; and performing, by the container storage management system and based on the determining, an action associated with the application.

Abstract: Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem and one or more components executed by the computer subsystem. The one or more components include a deep learning model configured for denoising an image of a specimen generated by an imaging subsystem. The computer subsystem is configured for determining information for the specimen from the denoised image.

Abstract: An illustrative distributed storage system detects a request to resize an online volume that provides persistent storage for a containerized application running in a container system, wherein the online volume is backed by a virtual block device created in device namespace using a device mapper virtualization of data blocks of one or more storage devices. The distributed storage system performs, in response to the request, a resize process to resize the online volume. The resizing of the online volume includes resizing the virtual block device. In some examples, a file system implemented on the virtual block device is also resized while the online volume is online. The storage system may manage inflight IOs over the virtual block device and the online volume such that the resize process is performed without error.

Abstract: An illustrative method includes an application management system receiving a request to deploy an application within a compute environment associated with an entity; accessing deployment data associated with one or more other deployments of the application within one or more other compute environments associated with one or more other entities; and determining a set of configuration parameters for use with deployment of the application within the compute environment.

Abstract: An illustrative method includes a storage management system accessing metrics data associated with storage pools of a cluster; performing, based on the metrics data, a predictive failure analysis with respect to the storage pools, the predictive failure analysis indicating a likelihood of failure of each of the storage pools; selecting, based on the predictive failure analysis, a storage pool from the storage pools as an optimal location for a volume; and creating the volume on the storage pool.

Abstract: An EtherCAT device with a node for use in an EtherCAT network is disclosed. The EtherCAT device includes: a clock circuit; a clock input to receive an input clock signal; a clock output to send an output clock signal; and control logic. The control logic is to determine whether to operate the EtherCAT device in a clock generation mode or a clock propagation mode, wherein in the clock generation mode, the clock circuit is to drive an oscillator to generate the input clock signal; and in the clock propagation mode, the clock circuit is to receive the input clock signal from another node in the EtherCAT network. The control logic is further to control the clock circuit to output the output clock signal for a subsequent node in the EtherCAT network based upon the input clock signal.

Abstract: An EtherCAT device is disclosed. The EtherCAT device comprises a data input port to receive a signal representing data, the signal representing one of a plurality of possible logical values; and a degradation calculation circuit. The degradation calculation circuit is to read, demodulate, and convert the received signal into a digital domain representation; process the digital domain representation into slices, where the value of the received signal at a respective time is represented in a respective one of the slices; determine differences between the respective slices and reference slices; identify an intended logical value of the received signal responsive to the determined differences; determine a quantification of error at the respective time responsive to the identified logical value and the determined differences; and determine a signal quality index responsive to the determined quantification of error.

Abstract: An example method for automated elastic resource management of a container system by a storage system comprises providing, by a container storage management system, a volume for use by a containerized application of a container system, the volume deployed on a node of the container system; analyzing, by the container storage management system, incoming input/output (I/O) operations to the volume; and adjusting, by the container storage management system and based on the analyzing, a capacity of a resource associated with the node.

Abstract: An example method for dynamic throughput capacity resource management for a container system by a storage system comprises providing, by a container storage management system, a volume for use by a containerized application of a container system, the volume deployed on a node of the container system; determining, by the container storage management system, that the containerized application is consuming below a threshold level of resources; and reducing, by the container storage management system and based on the determining, a capacity of a resource associated with an input/output (I/O) throughput rate capacity to the volume.

Abstract: An example method for detecting and remediating anomalies in a container system by a storage system comprises detecting, by a container storage management system, a change in resources utilized on a volume of the container system by an application of the container system; determining, by the container storage management system and in response to the detecting, whether the change in resources utilized is anomalous for the application; and performing, by the container storage management system and based on the determining, an action associated with the application.

Abstract: In some embodiments, storage path routing in a container system includes: determining, within the container system, a storage operation directed to a storage resource; determining one or more conditions associated with a plurality of network paths to the storage resource, where the plurality of network paths includes a first network path through a storage area network and a second network path through a container system communication network; and routing, based on the one or more conditions, the storage operation to the storage resource using the first network path.

Abstract: Aspects of the disclosure relate to prioritizing messages for server processing based on monitoring and predicting server resource utilization. A computing platform may monitor a plurality of servers (e.g., processing engines) to determine a current processing capacity and a maximum processing capacity of each processing engine. The computing platform may predict a quantity of application messages that may be received and/or processed by each processing engine at a later time. The computing platform may generate an application message prioritization framework comprising the prediction, the current processing capacity, and the maximum processing capacity of each processing engine.

Abstract: Aspects of the disclosure relate to prioritizing messages for server processing based on monitoring and predicting server resource utilization. A computing platform may monitor a plurality of servers (e.g., processing engines) to determine a current processing capacity and a maximum processing capacity of each processing engine. The computing platform may predict a quantity of application messages that may be received and/or processed by each processing engine at a later time. The computing platform may generate an application message prioritization framework comprising the prediction, the current processing capacity, and the maximum processing capacity of each processing engine.

Abstract: Two versions of a database can be held in two trees that have many of the same nodes. Both trees can be concurrently searched using recursive algorithms. A root node indicator indicates a root node for a tree search algorithm. The root node indicator can indicate a first root node of a first tree. A tree search algorithm can identify a record node in the first tree. Intermediate nodes between the record node and the first root node can be identified and retained nodes can be identified. A second root node and replacement intermediate nodes can be instantiated. A second tree that includes the second root node, the replacement intermediate node, and the retained nodes can be created. The root node indicator can be set to indicate the second root node after creating the second tree.

Abstract: An EtherCAT device includes a communications circuit and a wakeup circuit. The wakeup circuit is configured to determine a condition in which to send data to an EtherCAT master node. The wakeup circuit, based on such a condition, is configured to generate a wakeup packet. The communications circuit may be configured to receive an EtherCAT frame originating from the EtherCAT master node. The communications circuit may be configured to populate the EtherCAT frame with the data to be sent to the EtherCAT master node. The communications circuit may be configured to send the EtherCAT frame to the EtherCAT master device.

Abstract: Methods and system for directing traffic flows to a fast data path or a slow data path are disclosed. Parsers can produce packet header vectors (PHVs) for use in match-action units. The PHVs are also used to generate feature vectors for the traffic flows. A flow training engine produces a classification model. Feature vectors input to the classification model result in output predictions predicting if a traffic flow will be long lived or short lived. The classification models are used by network appliances to install traffic flows into fast data paths or the slow data paths based on the predictions.

Abstract: A vehicle includes: an engine assembly including a cylinder head, an engine output shaft, and a crankshaft; and an electric motor assembly transversely disposed above a crankcase of the engine assembly. The electric motor assembly includes an electric motor and an electromagnetic clutch mounted coaxially on the electric motor.

Abstract: A method for identifying documents that are similar in content to an input document includes receiving a request for identifying similar documents from among a plurality of candidate documents, retrieving document classification attributes for the input document and the candidate documents, where the document classification attributes are document level attributes.