Embodiments of systems and methods for platform framework error handling are described. A platform framework may receive registration requests from framework participants that provide operation of a plurality of hardware devices of an IHS (Information Handling System). The framework registration requests by participants specify remediation policies for addressing error conditions related to respective participants. The received remediation policies are mapped to the registered participants, where remediation policies may include handles for invoking remediation procedures for a registered participant. Error conditions are detected during operation of the platform framework. The registered participant is identified as a source of the error condition and a remediation policy that is mapped to the registered participant is identified. Handles in the remediation policy are used to invoke remediation procedures for the registered participant. Remediation procedures invoked by the handles may be provided by a remediation agent that provides support for registered participants.

A system for representing attributes in a transportation system digital twin includes a digital twin datastore and one or more processors. The digital twin datastore stores a transportation-system digital twin including real-world-element digital twins embedded therein. The transportation system digital twin corresponds to a transportation system. Each real-world-element digital twin provides a digital twin of a respective real-world element that is disposed within the transportation system. The real-world-element digital twins include mobile-element digital twins. Each mobile-element digital twin provides a digital twin of a respective mobile element within the real-world elements. The one or more processors are configured to, for each mobile element, determine, in response to an occurrence of a triggering condition, a position of the mobile element, and update, in response to determining the position of the mobile element, the mobile-element digital twin corresponding to the mobile element to reflect the position of the mobile element.

A delay measurement device that is to be connected, via a communication network, to a rendering server that performs rendering processing on an image, includes: transmission means for transmitting, to the rendering server, sensor information acquired from a sensor included in the delay measurement device and trigger information acquired in a predetermined time period; receiving means for receiving a rendered image generated through the rendering processing performed in the rendering server based on the sensor information and the trigger information; and measurement means for measuring a predetermined delay based on a difference between a first time indicating a time when the trigger information was transmitted to the rendering server and a second time indicating a time when the rendered image or a predetermined image was displayed.

A log monitoring system uses log monitoring rules to monitor log data generated by applications executing on a client computing device. By monitoring log data, the system detects that one or more triggering events have occurred on the client computing device. In response, the log monitoring system can perform one or more appropriate remedial actions. Additionally, in response to the detected event(s), the log monitoring system can extract a select subset of relevant data from the client and transmit the subset of data to a separate repository for storage and/or processing.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for improving distributed monitoring using different groups of remote devices. In some implementations, a system communicates with a set of remote devices involved in a first monitoring program that involves collection of data from the remote devices over a communication network. The system identifies a pattern or similarity among monitoring data collected from a subset of the remote devices involved in the first monitoring program. The system determines that the identified pattern or similarity satisfies one or more criteria for initiating additional monitoring. In response, the system determines parameters specifying second types of data to collect in a second monitoring program. The system configures one or more devices to perform monitoring for the second monitoring program including acquiring data for the second types of data and providing the acquired data to a server over the communication network.

Apparatus, media, methods, and systems for data storage systems and methods for autonomously adapting data storage system performance, lifetime, capacity and/or operational requirements. A data storage system may comprise a controller and one or more non-volatile memory devices. The controller is configured to determine a category for a workload of one or more operations being processed by the data storage system using a machine-learned model. The controller is configured to determine an expected degradation of the one or more non-volatile memory devices. The controller is configured to adjust, based on the expected degradation and an actual usage of physical storage of the data storage system by a host system, an amount of physical storage of the data storage system available to the host system.

The disclosed technology teaches keeping up with the deployment of APIs, so that Secure Access Service Edge (SASE) protection is afforded, parsing an OpenAPI specification for an API family, for identifying overall attributes of the API family. The method includes identifying resources with respective URIs within the API family and extracting usable attributes of the API resources, useful for building a connector, extracting per-activity attributes that correspond to core activities that trigger protective actions. The disclosed technology also includes applying a connector creator that performs actions including accessing a template for creating connector rules that recognize intercepted API calls and activities requested by the API calls, and that trigger the protective actions, using the template and the extracted attributes from the API resources, producing the connector rules, and storing the connector rules for use in processing intercepted APIs. Further taught is using the connector rules for the SASE protection.

Systems, apparatuses, and methods for implementing telemetry push aggregation techniques are described. A computing system includes one or more input/output (I/O) agents interposed between functional units and a communication fabric. A given I/O agent receives a set of aggregation rules from a power management unit. The I/O agent monitors traffic from the functional units, and the I/O agent generates telemetry data from the traffic data based on the set of aggregation rules. The telemetry data is used by the power management unit to make adjustments to one or more power settings.

An example embodiment involves persistent storage defining a first configuration item representing an application deployed within a network, a second configuration item representing a software program that is deployable within the network, and a relationship between the first configuration item and the second configuration item. One or more processors may be configured to: (i) receive an indication that a change has been applied to the application or has been arranged to be applied to the application; (ii) identify the relationship between the first configuration item and the second configuration item; (iii) based on the relationship between the first configuration item and the second configuration item, determine that the change can affect operation of the software program; and (iv) in response to determining that the change can affect operation of the software program, provide a notification of the change to an agent associated with the software program.

Systems and methods are provided for implementing soft model assertions (SMA) system and techniques designed to monitor and improve Machine Learning (ML) model quality by to detecting errors within the one or more ML models. SMA techniques and systems are distinctly designed to leverage: 1) a user's ability to specify features over data; and 2) large, existing datasets of organizations, in a manner that can improve the accuracy and quality of predicting potential errors in Machine Learning (ML) models. A SMA system can include a controller device receiving predictions generated based on the ML models and output from the SMA system. The controller performs autonomous operations of the system in response to determining that the one or more detected errors within the one or more ML models yield a high certainty of errors in the predictions. The SMA system also includes a domain specific language and a severity score module.