There is provided a method for managing a solid state storage system with hybrid storage technologies. The method includes monitoring one or more storage request streams to identify operating mode characteristics therein from among a set of possible operating mode characteristics. The set of possible operating mode characteristics correspond to a set of available operating modes of the hybrid storage technologies. The method further includes identifying a current operating mode from among the set of available operating modes responsive to the identified operating mode characteristics. The method also includes predicting a likely future operating mode responsive to variations in workload requirements to generate at least one future operating mode prediction. The method additionally includes controlling at least one of data placement, wear leveling, and garbage collection, responsive to the at least one future operating mode prediction.

Methods and systems are provided for rapid failure recovery for a distributed storage system for failures by one or more nodes.

Event records from multiple computing devices are received at a managing unit. Individual event records include an event identifier field including an event identifier identifying a first event associated with a particular computing device, a parent event identifier field identifying a parent event that initialized the first event, and an entity identifier field including an entity identifier identifying the particular computing device. The managing unit generates log records associated with event identifiers included in the event records. The log records include state fields indicating a state of a particular event associated with a particular event identifier. Based on a correlation of the event and log records, the managing unit determines at least two computing devices associated with events resulting in an error state, and identifies parent events that initialized the events with errors. The managing unit generates a report linking the parent events to the events having an error state.

Managing input sensitivity of an information handling system, including performing a calibration and configuration of an input sensitivity management model, including: identifying contextual data associated with the information handling system, the contextual data including i) system characteristics of the information handling system and ii) o user characteristics of a user of the information handling system; identifying historic states of the information handling system, the historic states including for each state in time of the information handling system, inputs to the information handling system and outputs of the information handling system based on the inputs to the information handling system; training, based on the contextual data and the historic states, the input sensitivity management model for determining an input sensitivity of the inputs to the information handling system; performing, after performing the calibration and configuration of the input sensitivity management model, a steady-state monitoring of the information handling system.

Multiple data paths may be available to a data management system for transferring data between a primary storage device and a secondary storage device. The data management system may be able to gain operational advantages by performing load balancing across the multiple data paths. The system may use application layer characteristics of the data for transferring from a primary storage to a backup storage during data backup operation, and correspondingly from a secondary or backup storage system to a primary storage system during restoration.

The management computer stores a configuration information of a storage, a configuration information of a host computer and a VM, an information on a service level of the VM, and a performance information of a storage subsystem and a network. If an access path that the host computer uses to access a volume is changed in response to a change of storage configuration, an I/O performance of the VM operating in the host computer may be changed. If the change of state of the storage is detected, the management computer calculates a change of state of whether a service level defined for the VM is satisfied, and selects an appropriate host computer in which the VM should be operated.

Multiple data paths may be available to a data management system for transferring data between a primary storage device and a secondary storage device. The data management system may be able to gain operational advantages by performing load balancing across the multiple data paths. The system may use application layer characteristics of the data for transferring from a primary storage to a backup storage during data backup operation, and correspondingly from a secondary or backup storage system to a primary storage system during restoration.

A storage device is disclosed. The storage device may include storage to store data and a controller to manage reading data from and writing data to the storage. The controller may also include a receiver to receive a plurality of requests, information determination logic to determine information about the plurality of requests, storage for the information about a plurality of requests, and sharing logic to share the information with a management controller.

A data storage system includes a plurality of storage devices organized as a redundant array of inexpensive disks (RAID) storage array and a RAID controller. The RAID controller monitors the plurality of storage devices in the RAID storage array. The RAID controller also detects that a host read request of a host has a latency exceeding a latency threshold. Based on the monitoring, the RAID controller determines whether a proactive rebuild of a data requested by the host read request in absence of a data error would likely be beneficial to performance. Based on determining that a proactive rebuild of the data requested by the host read request would likely be beneficial to performance, the RAID controller initiates the proactive rebuild of the data and sends the requested data to the host.

Example implementations relate to determining and implementing a feasible resource optimization plan for public cloud consumption. Telemetry data over a period of time is obtained for a current deployment of virtual infrastructure resources within a current data center of a cloud provider that supports an existing service and an application deployed on the virtual infrastructure resources. Information regarding a set of constraints to be imposed on a resource optimization plan is obtained. Indicators of resource consumption relating to the currently deployed virtual infrastructure resources during the period of time are identified by applying a deep learning algorithm to the telemetry data. A resource optimization plan is determined that is feasible within the set of constraints based on a costing model associated with resources of an alternative data center of the cloud provider, the indicators of resource consumption and costs associated with the current deployment.