Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods are disclosed for warranty recommendations for users based upon warranty selections of peer users. The users are clustered into peer groups based upon industry or market segment based upon user data including primary variables, such as workload type data and market segment data, and secondary variables, such as virtual machine size or number, cluster size, cost, and downtime. New users are matched to a top similar user within their peer group based upon a vector distance, wherein the vector comprises the primary and secondary variables. A current warranty of the top similar user is recommended to the new user. Warranty changes by members of a peer group cause trigger an updated ranking of the peer group warranties. Expert user comments and rankings are used to generate expert user recommendations. A cost-based impact assessment may also be used for warranty recommendations by highlighting favorable and unfavorable warranty properties.

Abstract: 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.

Abstract: Embodiments of systems and methods for verification of software packages prior to deployment on an Information Handling System (IHS) are described. In an illustrative, non-limiting embodiment, an IHS may include computer-executable instructions for identifying a version of a first software package that a second software package depends upon to operate on the client IHS, and updating a data structure to indicate the identified software package dependency to the first software package version. Later on, when the client IHS requests to be upgraded with the second software package, the instructions access the data structure to determine whether or not the first software package version meets the identified software package dependency, and either allow or inhibit upgrading of the second software package depending upon whether or not the first software package version meets the identified software package dependency.