Dell Products Inc.

Sithiqu Shahul Hameed

Vinay Sawal

Udhaya Chandran Shanmugam

Per Henrik Fremrot

Jayanth Kumar Reddy Perneti

Anne-Marie McReynolds

Bryan D Neumann

Ravi Shankar Sabapathy

Health data relating to health of field replaceable units (FRUs) can be collected, determined, and managed. FRU can comprise a data health management component (DHMC) that can periodically or dynamically analyze sensor data relating to operational characteristics of the FRU received from sensors associated with components of or associated with the FRU, historical sensor data, or historical FRU health data, wherein the sensor data can indicate usage or accelerated usage of the components of the FRU. Based on such analysis, DHMC can determine or update an expected amount of time of remaining usable life of the FRU, which can take into account any accelerated usage of the FRU. DHMC can store, in a data store of the FRU, or present, via an interface of the FRU, information relating to the expected amount of time of remaining usable life of the FRU or other FRU health data.

Enhanced management of device health data

Presented herein are embodiments that use a language model to embed or encode configuration elements (e.g., commands, prompts, etc.) into dense, latent representations that incorporate semantic and contextual information. Using a trained language model, a configuration for a network device may be converted into a set of configuration path sentences. Given a first set of encoded configuration path sentences for a first configuration and a second set of encoded configuration path sentences for a second configuration, these two sets may be compared to gauge a degree of difference between the two sets. In one or more embodiments, an Optimal Transport method with Wasserstein distance metric may be used to obtain a comparison value that gauges difference between the two configurations. In one or more embodiments, the comparison valuation may be labeled or classified by comparing the comparison value to one or more pre-defined thresholds.

Systems and methods for gauging differences between network configurations

Methods, systems, and devices for providing computer implemented services using managed systems are disclosed. To provide the computer implemented services, the managed systems may be deployed to a location and operate in a predetermined manner conducive to, for example, execution of applications that provide the computer implemented services. When deployed to a location, the managed systems may be housed in a managed system frame. The managed system frames may include systems to guide placement of managed system in preferred frame units, remotely identify occupancy of the frame units, and/or the frame units against unexpected removals of or insertion of devices in the frame units.

System and method for guided deployment of managed systems

Techniques for service instance management in an information processing system environment are disclosed. For example, a method comprises determining associations for first components of a virtual infrastructure platform and second components of a physical infrastructure platform. The method further comprises generating a graphical representation based on the associations for the first components and the second components. The method further comprises causing instantiation of one or more processing elements configured to perform at least one management function in accordance with at least one of the virtual infrastructure platform and the physical infrastructure platform, based on the graphical representation.

Service instance management in information processing system environment

Hybrid deep learning systems and methods allow for detecting anomalies in objects, such as electrical printed circuit board (PCB) components, based on image data. In one or more embodiments, a hybrid deep learning model comprises a Graph Attention Network (GAT) that uses spatial properties of the PCB components to extract latent semantic information and generate an output set of hidden representations. The GAT treats each of the electrical components as a node and each connection between them as edges in a graph. The hybrid system further comprises a Convolutional Neural Network (CNN) that uses pixel data to obtain its own output set of hidden representations. The hybrid deep learning model concatenates both sets to detect anomalies that may be present on the PCB.

Hybrid deep learning for anomaly detection

System and method for physical security of distributed systems

A board damage classification system includes a Convolutional Neural Network (CNN) sub-engine and a Graph Convolutional Network (GCN) sub-engine that were trained based on digital images of structures that have experienced natural disasters. The CNN sub-engine receives a board digital image of a board, analyzes the board digital image to identify board features, and determines a board feature damage classification for the board features. The CGN sub-engine receives a board feature graph that was generated using the board digital image and that includes nodes that correspond to the board features in the board digital image, and defines relationships between the nodes included in the board feature graph. The board feature damage classification determined by the CNN sub-engine and the relationships defined by the GCN sub-engine are then used to generate a board damage classification that includes a damage probability for board features in the board digital image.

Growing community of inventors

Chennai, India

Sithiqu Shahul Hameed