Abstract: The described technology is generally directed towards a system and method to distribute available power to devices in a group. A group of devices, such as a rack of servers in a data center, can have a group power consumption limit, such as the limit of a power distribution unit that supplies power to the group. The group of devices can each report their individual power consumption to a manager, and the manager can combine the individual power consumptions. The manager can compare the combined power consumption of the group to the group power consumption limit to determine a power headroom. The manager can determine power consumption limits for the devices of the group based on the power headroom, the individual power consumptions, and device priority information.

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 software-defined infrastructure can identify and remediate an airflow deficiency scenario on a rack device. A rack device manager can be configured to discover rack devices and create a representation of their physical locations. The rack device manager can also be configured to periodically retrieve airflow metrics of the rack devices to calculate an estimated airflow for each rack device. The rack device manager can use the estimated airflows and the airflow metrics to generate a rack device classifier for each rack device. Using these rack device classifiers, the rack device manager can detect when rack devices are experiencing airflow deficiencies and attempt to automatically remediate such deficiencies.

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: The described technology is generally directed towards reallocating power to devices in a group, such as a rack of servers in a datacenter, when no power headroom remains available. Respective devices, which are classified into respective classes corresponding to respective priority device class levels, have power distributed thereto. Upon determining that available power headroom for power capacity distribution among the group of devices has reached an available power capacity distribution lower limit (e.g., zero), reallocation is performed by increasing power capacity allocated to a first device of a higher priority level class, and decreasing power capacity allocated to a second device of a lower priority level class. For more classes, such as high, medium and low, the power can be decreased among the lower priority level classes by a defined ratio, e.g., two-to-one between the medium and low priority classes.

Abstract: According to one embodiment, an Information Handling System (IHS) includes a host that is executed to manage deployment of a plurality of workspaces, and executable instructions to obtain an amount of energy consumed by one or more resources of the host, obtain workspace usage metrics of each of the workspaces, and determine, using the workspace usage metrics, a proportionate amount of energy used by each of the workspaces. The instructions can, using the determined proportionate amount of energy, determine an overall amount of energy used by each workspace by distributing the amount of energy used by each of the resources across each of the workspaces according to the proportionate amount of energy used by each of the workspaces.

Abstract: The described technology is generally directed towards a system and method to distribute available power to devices in a group. A group of devices, such as a rack of servers in a data center, can have a group power consumption limit, such as the limit of a power distribution unit that supplies power to the group. The group of devices can each report their individual power consumption to a manager, and the manager can combine the individual power consumptions. The manager can compare the combined power consumption of the group to the group power consumption limit to determine a power headroom. The manager can determine power consumption limits for the devices of the group based on the power headroom, the individual power consumptions, and device priority information.

Abstract: A software-defined infrastructure can identify and remediate an airflow deficiency scenario on a rack device. A rack device manager can be configured to discover rack devices and create a representation of their physical locations. The rack device manager can also be configured to periodically retrieve airflow metrics of the rack devices to calculate an estimated airflow for each rack device. The rack device manager can use the estimated airflows and the airflow metrics to generate a rack device classifier for each rack device. Using these rack device classifiers, the rack device manager can detect when rack devices are experiencing airflow deficiencies and attempt to automatically remediate such deficiencies.

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: 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: The present disclosure involves systems, software, and computer implemented methods for distributed monitoring in clusters with self-healing. One example method includes determining, by a monitoring agent of a first node of a cluster, a self-monitoring check to perform for the first node. The first node is among multiple, other nodes included in the cluster. In response to receiving a successful status for the self-monitoring check, a registry in the first node is updated with the successful status. The registry includes node statuses for each node in the cluster. In response to receiving an unsuccessful status for the self-monitoring check, the monitoring agent performs at least one corrective action on the first node and updates the registry in the first node with a result of the at least one corrective action. The registry is broadcasted to each of the other nodes in the cluster as an updated registry.

Abstract: A method and system including an Application Programming Interface (API) proxy module; an API proxy processor in communication with the API proxy module and operative to execute processor-executable process steps to cause the system to: receive API development data, wherein the API data includes at least one of back-end data and use-case data; identify one or more API clusters that is similar to the received API development data; generate an API proxy template based on the identified one or more API clusters; and display the generated API proxy template to a client on a user interface. Numerous other aspects are provided.

Abstract: The present disclosure involves systems, software, and computer implemented methods for distributed monitoring in clusters with self-healing. One example method includes determining, by a monitoring agent of a first node of a cluster, a self-monitoring check to perform for the first node. The first node is among multiple, other nodes included in the cluster. In response to receiving a successful status for the self-monitoring check, a registry in the first node is updated with the successful status. The registry includes node statuses for each node in the cluster. In response to receiving an unsuccessful status for the self-monitoring check, the monitoring agent performs at least one corrective action on the first node and updates the registry in the first node with a result of the at least one corrective action. The registry is broadcasted to each of the other nodes in the cluster as an updated registry.

Abstract: The present disclosure involves systems, software, and computer implemented methods for distributed monitoring in clusters with self-healing. One example method includes determining, by a first instance of a monitoring agent of a first node of a cluster, whether an error condition in a first registry included in the first node is present, where the error condition is associated with a second node of the cluster. In response to determining that the error condition associated with the second node is present in the first registry, the first instance of the monitoring agent performs at least one corrective action on the second node. A result of that action is determined, and the first registry in the first node is updated with the result. The first registry is then broadcast to each of the other nodes in the cluster other than the first node as an updated registry.

Abstract: The present disclosure involves systems, software, and computer implemented methods for distributed monitoring in clusters with self-healing. One example method includes determining, by a monitoring agent of a first node of a cluster, a self-monitoring check to perform for the first node. The first node is among multiple, other nodes included in the cluster. In response to receiving a successful status for the self-monitoring check, a registry in the first node is updated with the successful status. The registry includes node statuses for each node in the cluster. In response to receiving an unsuccessful status for the self-monitoring check, the monitoring agent performs at least one corrective action on the first node and updates the registry in the first node with a result of the at least one corrective action. The registry is broadcasted to each of the other nodes in the cluster as an updated registry.

Abstract: The present disclosure involves systems, software, and computer implemented methods for distributed monitoring in clusters with self-healing. One example method includes determining, by a monitoring agent of a first node of a cluster, a self-monitoring check to perform for the first node. The first node is among multiple, other nodes included in the cluster. In response to receiving a successful status for the self-monitoring check, a registry in the first node is updated with the successful status. The registry includes node statuses for each node in the cluster. In response to receiving an unsuccessful status for the self-monitoring check, the monitoring agent performs at least one corrective action on the first node and updates the registry in the first node with a result of the at least one corrective action. The registry is broadcasted to each of the other nodes in the cluster as an updated registry.

Abstract: A method and system including an Application Programming Interface (API) proxy module; an API proxy processor in communication with the API proxy module and operative to execute processor-executable process steps to cause the system to: receive API development data, wherein the API data includes at least one of back-end data and use-case data; identify one or more API clusters that is similar to the received API development data; generate an API proxy template based on the identified one or more API clusters; and display the generated API proxy template to a client on a user interface. Numerous other aspects are provided.

Abstract: The present disclosure involves systems, software, and computer implemented methods for distributed monitoring in clusters with self-healing. One example method includes determining, by a monitoring agent of a first node of a cluster, a self-monitoring check to perform for the first node. The first node is among multiple, other nodes included in the cluster. In response to receiving a successful status for the self-monitoring check, a registry in the first node is updated with the successful status. The registry includes node statuses for each node in the cluster. In response to receiving an unsuccessful status for the self-monitoring check, the monitoring agent performs at least one corrective action on the first node and updates the registry in the first node with a result of the at least one corrective action. The registry is broadcasted to each of the other nodes in the cluster as an updated registry.

Abstract: System and method for enabling downloading of files. The system includes an application module (102), download configuration module (106) and a database module (108). The application module (102) is configured to be associated with a device and collect data pertaining to a user's preference for downloading a file. The application module (102) communicates the preferences of a user to a download configuration module (106). The download configuration module (106) locates the files requested for the download, and thereupon splits the file into multiple parts based on the user's preferences. The multiple parts are downloaded onto one or more devices, based on the user's preferences. Upon completion of the download onto one or more devices, all the parts of the file are imported onto a device. The application module (102) is configured to combine all the parts of the file into a larger file.