Dell Products Inc.

Rushyendra Velamuri

Praveen S Lalgoudar

Vaideeswaran Ganesan

Pandiyan Varadharajan

Raveendra Babu Madala

Abhishek Gupta

Sandeep Venkatesh Pai

Nikhil S

Winston X Fernandes

Rekha M S

Parmeshwr Prasad

Chandrasekhar Revuri

Harish Babu

Chandrasekhar R

Rekha Ms

Nidhi Kant Arora

Rahul Ranjan

Priti Parate

Gargi Priyadarshini

An information handling system includes a storage and a processor. The storage stores a firmware update for a component of the information handling system. The processor receives a firmware update notification. The firmware update notification is associated with the firmware update for the component. The processor determines whether the component is weather dependent. In response to the firmware update being weather dependent, the processor determines whether the firmware update currently may be performed based on a weather report for a location of the information handling system. In response to a determination that the firmware update may be performed, the processor performs the firmware update on the component.

External weather conditions aware firmware updates

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

Architectures and techniques are described that, in the context of a microservice platform, can provide automated discovery and configuration of message brokers on behalf of resident microservices. As a result, microservices no longer need to be tightly bound to a particular message broker. Hence, significant coding changes for microservices developers can be avoided over the lifetime of a particular microservice.

Automated message broker discovery

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 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

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

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Bengaluru, India

Rushyendra Velamuri