Embodiments of the present disclosure relate to a model training method, a failure determining method, an electronic device, and a computer program product. The model training method includes: acquiring a plurality of disk failure data sets collected in a first time period; acquiring another disk failure data set that is collected at a predetermined time point after the first time period and indicates failure information of at least one failed sector set; and training a failure determining model based on the plurality of disk failure data sets and the failure information, so that a probability of matching of predicted failure information at a predetermined time point determined by the trained failure determining model based on the plurality of disk failure data sets and the failure information is greater than a first threshold probability. By using the technical solution of the present disclosure, it is possible to predict the failure information that will occur in the sector set included in a disk based on the disk failure data set associated with a failed sector, so that a user or administrator of the disk can know the failure condition that will occur in the sector set of the disk in advance.

In general, the invention relate to providing computer implemented services using information handling systems. One or more embodiments includes after being allocated to a composed information handling system of the composed information handling systems: monitoring health of a hardware resource of the composed information handling system, making a determination, based on the monitoring of the health of the hardware resource, that the hardware resource is in a compromised state, and based on the determination, initiating a hardware replacement operation using replacement option information (ROI) for the hardware resource and replacement conditions for the hardware resource.

A computer program product, system, and computer implemented method for rapid database restoration using a database restore and recovery process that leverages one or more sparse data files and/or blocks by restoring one or more sparse data files and/or blocks and providing a mechanism to redirect requests to the one or more sparse data files and/or blocks to a backup copy of the actual data files and/or blocks and a process to populate the one or more sparse data files and/or blocks while the database is operational for servicing user requests. The approach includes the creation and population of one or more sparse data files and/or blocks, a redirection mechanism to service read operations where necessary, and a process to restore the data to one or more sparse data files and/or blocks over time, while the database maintains operability.

A system, method, and computer-readable medium are disclosed for performing a data center monitoring and management operation. The data center monitoring and management operation includes: identifying data center asset data to monitor; collecting data center asset data; and, performing an adaptive update scheduling operation, the adaptive update scheduling operation adaptively adjusting a prioritization and frequency of data center asset data collection to provide adapted data center asset data.

Methods, systems, and computer program products for multi-controller declarative fault management and coordination for microservices are provided herein. A computer-implemented method includes processing information pertaining to at least one fault impacting multiple resources within a given system, wherein respective portions of the multiple resources are managed by multiple independent controllers; determining, by each of at least a portion of the multiple independent controllers and based at least in part on the processing of the information, one or more desired resource states and one or more remediation actions; generating, based at least in part on one or more of the determined desired resource states and the determined remediation actions, a sequential ordering of the determined remediation actions to be carried out by the at least a portion of the multiple controllers; and automatically initiating execution of the determined remediation actions in accordance with the generated sequential ordering.

A simplex Flight Control Computer (FCC), usable in conjunction with a neighboring FCC, includes an input providing module for receiving sensor, system and neighboring FCC data; a processing unit for executing a command partition and a monitor partition, the processing unit receives the sensor, system data and neighboring FCC data; the monitor partition monitors the neighboring FCC data and provides a monitoring indicative signal to the neighboring FCC, and the command partition generates command signals; a hardware monitoring module provides a validity signal indicating FCC health; an output cutoff module receiving the FCC validity signal and enable signals generated by each monitor partition; the output cutoff module providing an enable signal based on a predetermined enabling strategy; and an enable switch connected with the output cutoff module and the processing unit and providing a received signal from the command partition according to the enable signal.

A data storage management layer comprises computing device(s), operatively connected to storage resources, which comprise data storage units and control units. The data storage management layer is operatively connected to the storage resources. They are operatively connected to host computers. A sub-set of the storage resources are assigned to each host, in order to provide storage services according to performance requirements predefined for the host, thereby generating Virtual Private Arrays (VPA). The computing device(s) are configured to perform a method of managing the data storage system comprising: (a) implement storage management strategies, comprising rules. The rules comprise conditions and actions. The actions are capable of improving VPA performance in a dynamic manner; (b) repetitively performing: (i) monitor VPA performance for detection of compliance of VPA with the condition(s); and (ii) responsive to detection of compliance of VPA with the condition(s), performing the action(s).

A computer system configured to identify errors in a session launch initiated by a client application is provided. The computer system includes a memory and at least one processor coupled to the memory. The at least one processor is configured to receive one or more events from one or more applications or devices involved in the session launch, wherein an event of the one or more events comprises information from an application or device call (e.g., an application programming interface (API) call) communicated during the session launch, the information comprising destination information; build a primary Directed Acyclic Graph (DAG) based on the information from the API call; determine an error identifier based on the primary DAG; retrieve a troubleshooting recommendation from a library based on the error identifier; and send the troubleshooting recommendation to the client application.

A method for managing specialized hardware resources includes obtaining, by a disaster recovery (DR) virtual resource agent, a request for a DR environment for a set of virtual resources in a primary site, in response to the request: monitoring the primary site to obtain virtual workload information corresponding to the set of virtual resources, performing a workload analysis on the set of virtual resources in the primary site using the virtual workload information to obtain a virtual resource mapping of each virtual resource in the primary site to a tiered component in the DR environment, and initiating a DR environment allocation of DR virtual resources based on the virtual resource mapping.

One example method includes performing a machine learning process that involves performing an assessment of a state of a computing system, and the assessment includes analyzing information generated by an IoT edge sensor in response to a sensed physical condition in the computing system, and identifying an entity in the computing system potentially impacted by an event associated with the physical condition. The example method further includes identifying a preemptive recovery action and associating the preemptive recovery action with an entity, and the preemptive recovery action, when performed, reduces or eliminates an impact of the event on the entity, determining a cost associated with implementation of the preemptive recovery action, evaluating the cost associated with the preemptive recovery actions and identifying the preemptive recovery action with the lowest associated cost, implementing the preemptive recovery action with the lowest associated cost, and repeating part of the machine learning process.