Dell Products Inc.

Samsung Electronics Co., Ltd.

Emc IP Holding Company LLC

Cleartrail Technologies Private Limited

Honeywell International Inc.

Other

Abhishek Gupta

Venkata Chandra Sekar Rao

Vishal Bhandari

Anand Bhargava

Sapnesh Agrawal

Winston X Fernandes

Rishi Chandra

Myeong Wuk Jang

Praveen S Lalgoudar

Chandrasekhar Revuri

Rushyendra Velamuri

Kartikeya Putturaya

Diwahar Sivaraman

Rashmi Sudhakar

Anand Changegowda

Kushal Bansal

Kevin Thomas Marks

Thomas D Judd

Raveendra Babu Madala

Vaideeswaran Ganesan

Raghu Shamasundar

Rajesh Chaubey

Ravishankar Kanakapura Nanjundaswamy

David Pepitone

Dorothee De Villele

Ravishankar Kanakapura N

Do Jun Byun

Sharanabasappa Advani

Chandrasekhar R

Sumit Gupta

Ravishankar N Kanakapura

Pandiyan Varadharajan

Adib Bouanani

Rekha M S

Nandini Arulmani

Karthik Venkataraman

Karun Thankachan

Prashanth Ramanna

Bhargavi M Shankarananda

Prasad Rao Piradi

Rohit Gujrati

Ranju Thomas Pulikot

Veeralla Sai V Yashodhar

Ramesh Abhilash Venkata

Kaushik Swaminathan

Abhinav Anshuman

Velmurugan R

Pranjal Jaju

Peter Shipman

Arvind Babu

Sham A R

Venkata ChandraSekarRao

Systems and methods take into account the criticality of workloads, the warranty needs of workloads, the warranty available time, and the lifetime of a workload to provide an optimal solution that ensures servers are used to highest extent. The warranty health of servers is computed and categorized as critical, warning, or healthy based on the number of days remaining in warranty. Workloads are tagged as short-term or long-term workloads. Workloads are also classified based on criticality. The quarantine mode for proactive high availability of servers is divided into multiple modes, including a long-time, critical-workload quarantine mode, a critical-workload quarantine mode, and a standard quarantine mode. Servers that are in quarantine mode are assigned new workloads based upon the warranty health, workload term, and workload criticality.

Distribution of workloads in cluster environment using server warranty information

A disclosed workload distribution method determines a yield index for each microservice associated with a containerized application executing on a potentially heterogeneous cluster information handling systems. Each microservice is then assigned to one of N priority categories based on its yield index, where three is an acceptable, but not exclusive, value of N. Resource configuration profiles are maintained for each of the priority categories. Each resource configuration profile assigns a resource configuration to each microservice. An information handling resource associated with a particular microservice is configured in accordance with the resource configuration assigned to the particular microservice by a particular resource configuration profile corresponding to the yield index. In this manner, workloads can be assigned and resources configured in accordance with the containerized application's priorities as exposed by the value indices.

Method and system for business yield aware workload distribution

A system and method are disclosed for performing, by a processor of an information handling system, an inventory of updateable components of the information handling system. The system and method also includes retrieving an update path catalog for the updateable component and determining published updates between a current version and a target version of the updateable component according to the update path catalog. In addition, the system and method includes determining update recommendations based on the published updates between the current version and the target version. Finally, the system and method includes consolidating the update recommendations into the consolidated update recommendation according to a criterion.

Dynamically consolidating applicable updates into an update recommendation

The present disclosure relates to platform configured to controlling one or more systems and exploring data across the one or more system connected to the platform. The platform may be configured to establish connection with a set of target systems. Further, the platform may be configured to identify a sub-set of target systems from the set of target systems based on a visual element selected, by the user, in an exploration. Furthermore, the platform may be configured to display a menu with a set of operations/composite operations associated with the sub-set of target get systems. Further, the platform may be configured to identify target system associated with the operation/composite operations selected by the user from the menu and transmit the operation/composite operations execution request to the target system for executing the operation/composite operations.

Platform to control one or more systems and explore data across one or more systems

In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may: receive performance information for a base workload; determine multiple threshold values of multiple storage media layers of each IHS of a storage cluster based at least on the performance information for the base workload and multiple inventory information respectively associated with multiple storage media layers of each IHS of the storage cluster; receive multiple condition values respectively associated with the multiple storage media layers of an IHS of the storage cluster; determine that a condition value of the multiple condition values associated with a storage media layer of the multiple storage media layers is at or below a threshold value of the multiple threshold values associated with the storage media layer of the multiple storage media layers; and reduce a storage workload of a specific IHS of the storage cluster.

System and method of wear leveling information handling systems of a storage cluster

Methods, apparatus, and processor-readable storage media for learning machine behavior related to install base information and determining event sequences based thereon are provided herein. An example computer-implemented method includes parsing data storage information based at least in part on parameters related to install base information comprising temporal parameters and event-related parameters; formatting the parsed set of data storage information into a parsed set of sequential data storage information compatible with a neural network model; training the neural network model using the parsed set of sequential data storage information and additional training parameters; predicting, by applying the trained neural network model to the parsed set of sequential data storage information, a future data unavailability event and/or a future data loss event; and outputting an alert based at least in part on the predicted future data unavailability event and/or predicted future data loss event.

Learning machine behavior related to install base information and determining event sequences based thereon

The present disclosure relates to platform configured for executing and rendering templates to enable automation of control and data exploration across systems in an interactive and non-interactive mode. Initially, the platform is configured to add template to an exploration either on user request or system driven request and execute the template by extracting execution model associated with the template. Further, the target system is configured to run the execution plan in interactive mode wherein the user may provide inputs or non-interactive mode wherein the target system is configured to run the execution plan without user inputs. The target system is further configured to receive data generated by the target systems in response to the operation executed by the execution engine. On receiving the data from target systems in the data store, the target system is configured to visualize the data on the template rendered on the GUI.

Interactive and non-interactive execution and rendering of templates to automate control and exploration across systems

Methods, apparatus, and processor-readable storage media for proactively predicting large orders and providing fulfillment support related thereto are provided herein. An example computer-implemented method includes classifying, via a first set of one or more machine learning techniques, a transaction quote as a transaction quote that exceeds one or more size-related parameters or a transaction quote that does not exceed the one or more size-related parameters; determining, if the transaction quote is classified as a transaction quote that exceeds one or more size-related parameters, supportability of converting the transaction quote into a transaction order via a second set of one or more machine learning techniques; and outputting, based on the determined supportability, information pertaining to converting the transaction quote into a transaction order and fulfilling the transaction order to one or more entities associated with transaction order fulfillment.

Proactively predicting large orders and providing fulfillment support related thereto

In one embodiment, a method for managing a plurality of edge devices based on a predicted network bandwidth utilization includes: identifying a plurality of management tasks associated with the plurality of edge devices, each of the plurality of management tasks associated with a priority; determining a management task of the plurality of management tasks to be assigned to an edge device of the plurality of edge devices; accessing a time series indicating the predicted network bandwidth utilization of the plurality of edge devices; generating a threshold value for the management task based on the time series, the threshold value corresponding to a percentage of the predicted network bandwidth utilization and including one or more available time slots; determining that the predicted network bandwidth utilization is less than the threshold value; and assigning the management task to the edge device.

Managing edge devices based on predicted network bandwidth utilization

The present disclosure relates to platform configured to recommend Visual and Execution templates to automate exploration across one or more disparate systems. The platform is configured for receiving data from a target system. Further, the platform is configured to identifying a set of best fit templates, from a set of templates. In one embodiment, the set of templates may comprise one or more templates created by the user, one or more templates published by other users associated with the user, and one or more system generated templates. The platform is configured to analyze the set of templates based on the type of data received from the target system and a set of predefined rules to identify a subset of best fit templates (discovered Templates) from the set of templates. Furthermore, the platform is configured to execute the subset of best fit templates for performing one or more data processing operations.

Recommending visual and execution templates to enable automation of control and data exploration across systems

A path suggestion tool in a Software-Defined Networking (SDN) architecture to predict a router's future usage based on an analysis of the router's historical usage over a given period of time in the past and to recommend a routing path within the network in view of the predicted future usages of the routers/switches in the network. The path suggestion tool is an analytical, plug-and-play model usable as part of an SDN controller to provide more insights into different routing paths based on the future usage of each router. A Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model in the suggestion tool analyzes the historical usage data of a router to predict its future usage. A Deep Boltzmann Machine (DBM) model in the suggestion tool recommends a routing path within the SDN-based network upon analysis of the LSTM-RNN based predicted future usages of routers/switches in the network.

System and method to learn and prescribe network path for SDN

Techniques are provided for multi-model prediction and resolution of order issues. An exemplary method for predicting issues that will delay an order comprises: obtaining features of the order; processing the features using a multi-model predictor to predict the issues that will delay the order, wherein the multi-model predictor comprises a first model that determines a probability that the order will be delayed and a second model that determines a probability that the order will be delayed for each of a plurality of different predefined order issue types; and aggregating an output of the first and second models to provide a likelihood of the issues that will delay the order. The multi-model predictor also optionally comprises a prediction model that predicts a number of the delay issues for a future time period and/or a cost of the delay issues for different predefined order issue types based on historical data.

Multi-model prediction and resolution of order issues

Avionic display systems and methods are provided for generating avionic displays including instability prediction and avoidance symbology, such as dynamically-adjusted drag device deployment cues. In one embodiment, the avionic display system includes a controller and an avionic display device, which is coupled to the controller and on which an avionic display, such as a Vertical Situation Display (VSD), is generated. The controller is configured to: (i) project whether an unstable aircraft state will occur during an approach flown by an aircraft; (ii) when an unstable aircraft state is projected to occur, determine whether implementation of an optimized drag device deployment scheme can prevent the projected occurrence of the unstable aircraft state; and (iii) if determining that the implementation of the optimized drag device deployment scheme can prevent occurrence of the projected unstable aircraft state, generating symbology on the avionic display indicative of the optimized drag device deployment scheme.

Avionic display systems and methods for generating vertical situation displays including instability prediction and avoidance symbology

Systems and methods for displaying position sensitive datalink messages on avionics displays are provided. In one embodiment, a flight deck instrument display system for an aircraft comprises: a flight plan display screen that displays a graphical representation of at least a part of an aircraft's planned flight path together with symbology representing a position of the aircraft with respect to the aircraft's planned flight path; wherein the flight plan display screen further displays at least one symbol positioned along the graphical representation of at least a part of the aircraft's planned flight path that indicates a point of applicability for a received uplink datalink message.

Systems and methods for displaying position sensitive datalink messages on avionics displays

Provided is a communication method of a node, including receiving a first interest requesting content from a previous node in a content centric network (CCN), generating a first acknowledgment (ACK) message indicating reception of the first interest in response to receiving the first interest, and sending the first ACK message to the previous node. Provided also is a node that includes structure that is suitable to perform such a communication method.

Communication method of node in content centric network (CCN) and the node

Growing community of inventors

Bengaluru, India

Abhishek Gupta