Dell Products Inc.

Emc IP Holding Company LLC

Vaideeswaran Ganesan

Pravin Janakiram

Suren Kumar

Deepaganesh Paulraj

Ankit Kumar Singh

Parminder Singh Sethi

David Keith Chalfant

Anirban Kundu

Swamy Kadaba Chaluvaiah

Vivek Bhargava

Vinod P S

B Balaji Singh

Naman Goel

Abhishek Mishra

Balamurugan Gnanasambandam

Praveen S Lalgoudar

Chandrasekhar Revuri

Sridhar Chakravarthy

Rushyendra Velamuri

Abhijeet Bhattacharya

Pandiyan Varadharajan

Rishav Sethia

Sreeram Muthuraman

Tamilarasan Janakiram

Vinod Durairaj

Hemant Ramesh Gaikwad

Sandeep Venkatesh Pai

Santosh Kumar Sahu

Nikhil S

Prabhat Chandra Biswas

Tandava Venkata Krishna Popuri

Balaji Bondhili Singh

Sunil Lingappa

Ajaya K Senapati

Arjit Rastogi

Tushar Oza

Anitha Subramonia Iyer

Rekha M S

Rama Rao Bisa

Abhishek Gupta

Rajib Saha

Prasoon Kumar Sinha

Ankit Bansal

Harish Babu

Chandrasekhar R

Arunava Das Gupta

Shrinidhi Katte

Winston X Fernandes

Krishna Kumar Gupta

Ajit Kumar Padhi

Karan Arora

Sakshi Garg

Sakshi Arora

Lakshmi Narasimhan Mc

Shubhangi Srivastava

Tamilchelvan Bhuvaneswari

Liane Ghosh

Avinash V Patil

Rekha Ms

Viswanathan Balakrishnan

Nidhi Kant Arora

Methods, apparatus, and processor-readable storage media for converting text data to video data using artificial intelligence techniques are provided herein. An example computer-implemented method includes converting text data associated with a task into multiple action statements relating to user performance of at least a portion of the task; generating video data by processing at least a portion of the multiple action statements using one or more artificial intelligence techniques; compiling at least a portion of the generated video data into at least one video sequence corresponding to at least one action workflow for the user performance of the at least a portion of the task; and performing one or more automated actions based at least in part on the at least one video sequence.

Converting text data to video data using artificial intelligence techniques

An apparatus comprises a processing device configured to receive and parse a search request related to a given event to identify confident terms, and to determine a first subset of the confident terms which indirectly reference the given event and a second subset of the confident terms which are not in the first subset. The processing device is also configured to identify, for a given confident term in the second subset, terms having at least a threshold level of semantic similarity with the given confident term, and to construct a primary search query comprising the first and second subsets of the confident terms and additional search queries comprising the first subset of the confident terms and the identified one or more terms having at least the threshold level of semantic similarity with the given confident term. The processing device is further configured to execute the primary and additional search queries.

Constructing and executing search queries with terms semantically similar to those of a search request

An apparatus comprises a processing device configured to obtain one or more messages, the one or more messages being associated with management of a given one of one or more information technology assets in an information technology infrastructure, and to determine, based at least in part on content of a given one of the one or more messages, whether the given message is suitable for delivery in an audio format. The processing device is also configured, responsive to determining that the given message is suitable for delivery in the audio format, to convert the content of the given message into one or more audio messages. The processing device is further configured to select one or more audio output devices for delivery of the one or more audio messages, and to provide, to the selected one or more audio output devices, the one or more audio messages.

Conversion of messages into audio format

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

An apparatus comprises a processing device configured to detect a user login to access a console of a computing device implementing a multi-level console interface comprising two or more levels each associated with a set of criteria, goals to be achieved, and actions relevant for achieving the goals. The processing device is also configured to determine a current state of the computing device, and to select a given level of the multi-level console interface based at least in part on mapping the current state to a given set of criteria associated with the given level. The processing device is further configured to provide, via the console of the computing device, an interface screen corresponding to the given level, the interface screen displaying the actions relevant for achieving the goals of the given level to facilitate transitioning of the computing device from the current state to a desired state.

Multi-level console interface for computing devices

Techniques are provided for performing server device updates using update baselines that are tagged across multiple management consoles. One method comprises obtaining, by a first management console of multiple management consoles associated with a server device, a first update baseline comprising a first tag identifier, of multiple tag identifiers, that identifies a first update of a component of the server device across the multiple management consoles; and applying the first update baseline to the server device in a maintenance mode to update the component of the server device, wherein the server device is maintained in the maintenance mode when two or more of the tag identifiers are active in the server device following the application of the first update baseline. The server device can be restored from the maintenance mode when only one tag identifier is active in the server device following the application of the first update baseline.

Server device updates using update baselines tagged across multiple management consoles

Systems and methods provide customers with a need-based warranty using a deep learning neural network. After categorizing, a customer need is mapped to a warranty type based on the SLA needs. Warranties may then be suggested based on customer need. In another embodiment, systems and methods detect an optimal warranty based on part failure and performance of a server. A mean time to resolve or replace can be minimized in future failure instances by comparing the derived replacement time with available warranty offerings to identify potential deviations and thereby recommend an optimal warranty from the available offerings. In a further embodiment, systems and methods identify and offer additional service contracts for vender services. A warranty proposer looks for warranty types that are emitted by a warranty-types analyzer and by a technical-support analyzer. The overlapping warranty offers are provided to customers.

System and method for warranty customization based on customer need and part failure rate

A method for managing hardware resources includes obtaining, by a local virtual hardware license manager, a license request from an application instance, in response to the license request: sending a virtual hardware license request to a license management system, wherein the virtual hardware license request comprises a hardware resource list, making a first determination that a response to the virtual hardware license request validates the virtual hardware license request, and in response to the first determination: providing, based on the hardware resource list, a signed hardware resource document to a hardware resource manager managing a hardware resource, wherein the hardware resource list specifies the hardware resource, updating a hardware resource access repository to specify enabled access of the hardware resource by the application instance, and initiating access to the hardware resource by the application instance.

System and method for managing virtual hardware licenses of hardware resources accessed via application instances

A system update appliance includes a processor and a memory device with a Content Addressable Storage (CAS) space and a location addressable storage space. The location addressable storage space partitioned into an object storage space and a device storage space. The processor stores a device entry in the device storage space. The device entry is associated with a device external to the system update appliance and includes a component entry for a component of the device. The component operates based on an update. The component entry includes a description of the component and a pointer to a record stored in the CAS space. The processor stores the record in the CAS space. The record is associated with a combination of the component and the first update. The record includes the description, a second pointer to an update repository, and a third pointer to the object storage space. When the update is not stored in the object storage space, the third pointer is a null pointer and when the update is stored in the object storage space, the third pointer includes an address of the update in the object storage space.

System and method for content addressable storage system update appliance

An information handling system includes a provisioning server and a server. The server includes a baseboard management controller (BMC) to determine a first hardware inventory profile for the server. The BMC provides the first hardware inventory profile to the provisioning server. The BMC stores first signed provisioning configuration content that is based on a first ownership certificate for a first owner of the server. The BMC determines a second hardware inventory profile for the server, and provides the second hardware inventory profile to the provisioning server. The BMC stores second signed provisioning configuration content that is based on a second ownership certificate for a second owner of the server. In response to an expiration of the second ownership certificate, the BMC removes the second signed provisioning configuration content, compares a current hardware inventory profile to the first hardware inventory profile, and generates a report to indicate any hardware changes.

Overlay ownership certificates including hardware inventory profiles

Custom-tailored warranties are provided with improved service level agreements (SLA) based upon an estimated turnaround time for service and/or parts. The turnaround time is calculated using an artificial intelligence or machine learning engine considering parameters such as the transit time from the nearest service centers and warehouses, the availability of service engineers at the service centers, and the availability of replacement parts in the warehouse. A custom-tailored warranty also may be offered for a specific customer-selected SLA if supported by the estimated turnaround time for the location. A warranty recommendation may be based on device location for data centers in multiple locations. A Location-Based Warranty Monitor (LBWM) provides fine-grained warranty suggestions and Un-bound Warranty Tokens (UWTs) can be bound to a system to assign a warranty with a desired. SLA.

System and method for warranty customization based on device location and proximity to service center

Techniques are provided for generating multi-layer configuration templates for deployment across multiple infrastructure stack layers. One method comprises evaluating a semantic equivalence of attributes in an attribute registry to identify common attributes that appear in multiple stack layers; identifying architecture-specific attributes that are distinct from the common attributes; obtaining a semantic attribute group comprising related attributes; determining a semantic attribute group configuration for the related attributes by evaluating a configuration of the related attributes identified in a deployment configuration and/or a reference template configuration comprising the related attributes; and generating a configuration template for configuring multiple server devices, wherein the configuration template comprises (i) a first portion that configures the common attributes based on a configuration in the reference template configuration, and (ii) multiple second portions, wherein each second portion comprises an architecture-specific configuration of the architecture-specific attributes for a corresponding architecture-specific infrastructure stack type.

Generating multi-layer configuration templates for deployment across multiple infrastructure stack layers

Methods and systems for managing data processing system are disclosed. A data processing system may include one or more hardware and/or software components. The operation of the data processing system may depend on the operation of these components. To manage the operation of the data processing system, future failures of the hardware components may be predicted and used as a basis for predicted services to reduce the threat of the predicted component failures. To manage performance of the predicted services, the predicted services may be scheduled for performance. To schedule performance of the predicted services, limitations on availability of service professionals that may complete the predicted services may be taken into account.

System and method for proactive management of components based on service availability

Systems and methods for dynamically moving virtual machine (VM) data based upon context are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: select a VM having a plurality of VM files; identify, among the plurality of VM files, a movable VM file; and transfer the movable VM file from a first storage tier to a second storage tier based upon a usage classification associated with the movable VM file.

Dynamically moving virtual machine (VM) data based upon context

A method includes identifying a source virtual machine to be migrated from a source domain to a target domain, extracting file-in-use metadata and shared asset metadata from virtual machine metadata of the source virtual machine, and copying one or more files identified in the file-in-use metadata to a target virtual machine in the target domain. For each of one or more shared assets identified in the shared asset metadata, the method further includes (a) determining whether or not the shared asset already exists in the target domain, (b) responsive to the shared asset already existing in the target domain, updating virtual machine metadata of the target virtual machine to specify the shared asset, and (c) responsive to the shared asset not already existing in the target domain, copying the shared asset to the target domain and updating virtual machine metadata of the target virtual machine to specify the shared asset.

Integrity-preserving cold migration of virtual machines

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Bengaluru, India

Vaideeswaran Ganesan