Abstract: Disclosed herein are methods and systems for providing data recovery recommendations. In an exemplary aspect, a method may comprise identifying a plurality of storage devices. For each respective device of the plurality of storage devices, the method may comprise extracting a respective input parameter indicative of a technical attribute of the respective device, inputting the respective input parameter into a machine learning algorithm configured to output both a first likelihood of the respective device needing a data recovery and a second likelihood that the data recovery will fail, and determining a respective priority level of the respective device based on the first likelihood and the second likelihood. The method may comprise normalizing each respective priority level, and recommending a device of the plurality of storage devices for a test data recovery procedure based on each normalized priority level.

Abstract: Disclosed herein are systems and method for determining a boot status of a failover server. In an exemplary aspect, a method may receive a failover test request for a failover server that provides disaster recovery for a production server, wherein the failover test request queries a successful boot status of the failover server. The method may determine whether a login into the failover server can be performed to execute the failover test request. In response to determining that the login cannot be performed, the method may retrieve server metrics for a failover server from a metric store and may determine a probability of the successful boot status based on both the retrieved server metrics and historic server metrics. In response to determining that the probability is greater than a threshold probability, the method may mark a recovery point of the failover server as validated.

Abstract: Disclosed herein are methods and systems for providing data recovery recommendations. In an exemplary aspect, a method may comprise identifying a plurality of storage devices. For each respective device of the plurality of storage devices, the method may comprise extracting a respective input parameter indicative of a technical attribute of the respective device, inputting the respective input parameter into a machine learning algorithm configured to output both a first likelihood of the respective device needing a data recovery and a second likelihood that the data recovery will fail, and determining a respective priority level of the respective device based on the first likelihood and the second likelihood. The method may comprise normalizing each respective priority level, and recommending a device of the plurality of storage devices for a test data recovery procedure based on each normalized priority level.

Abstract: Disclosed herein are systems and method for determining a boot status of a failover server. In an exemplary aspect, a method may receive a failover test request for a failover server that provides disaster recovery for a production server, wherein the failover test request queries a successful boot status of the failover server. The method may determine whether a login into the failover server can be performed to execute the failover test request. In response to determining that the login cannot be performed, the method may retrieve server metrics for a failover server from a metric store and may determine a probability of the successful boot status based on both the retrieved server metrics and historic server metrics. In response to determining that the probability is greater than a threshold probability, the method may mark a recovery point of the failover server as validated.