Abstract: In general, embodiments of the invention relate to a method for enabling enhanced logging. The method includes obtaining a historical data for a target entity, determining an error probability of the target entity using the historical data, and enabling, based on the error probability, enhanced logging on the target entity.

Abstract: One example method includes defining a configuration of SCM, receiving a stream of IOs from a client, analyzing the stream of IOs and, based on the analyzing, estimating future IO behavior of the client, and based on estimated future IO behavior of the client, reconfiguring one or more parameters of the configuration of the SCM to define an updated configuration of the SCM, and the updated configuration of the SCM enables an improvement in IO performance relative to an IO performance of the configuration of the SCM.

Abstract: In general, embodiments of the invention relate to a method for enabling enhanced logging. The method includes obtaining historical data for a target entity, determining a steady state error probability of the target entity using the historical data, and enabling, based on the steady state error probability, a first level of enhanced logging on the target entity.

Abstract: Techniques described herein relate to a method for managing jobs. Such techniques may include: generating a job completion entry that specifies that execution of a first job was completed within an estimated period of time for the first job, generating a second job completion entry that specifies that execution of a second job was not completed within a second estimated period of time for the second job, updating the job resource mapping model based on the first job completion entry and the second job completion entry to obtain a new updated job resource mapping model.

Abstract: Techniques described herein relate to methods and systems for determining data asset criticality. Such techniques may include making a first determination that a plurality of data asset attributes are part of a causal attribute set; calculating a SHapeley Additive explanation (SHAP) value for each of the plurality of data asset attributes in the causal attribute set; and performing a weighted mean calculation using the SHAP values for each of the plurality of data asset attributes and a corresponding attribute value for each of the plurality of data asset attributes of a data asset to obtain a criticality score for the data asset.

Abstract: Techniques described herein relate to methods and systems for managing backup operations. The method may include receiving a request to perform a first backup operation for a first virtual machine (VM); making a first determination, using a vProxy preference map, that a first vProxy is assigned to the first VM based on a backup capability associated with the first vProxy; making a second determination that the first vProxy is not available to perform the first backup operation; making a third determination, based on the second determination and using a vProxy information database, that a second vProxy is available to perform the first backup operation based on the second vProxy being associated with the same backup capability as the first vProxy; and performing the first backup operation using the second vProxy.

Abstract: Methods and systems support managed use of a Storage Class Memory (SCM) by one or more applications operating on an IHS (Information Handling System). The operations that are supported by an IHS processor for flushing data from the SCM are determined. Applications are identified that operate using the persistent data storage capabilities of the SCM. The SCM flushing operations invoked by each these applications are monitored. The utilization of the SCM by each of the first plurality of applications is determined based at least in part on the monitored flushing operations by each application. The utilization of the SCM may also be based on calculated metrics of SCM utilization by the respective applications. The applications are classified based their determined SCM utilizations. Based on the classifications of SCM utilization, a subset of the applications may be identified for removal from use of the SCM.

Abstract: One example method includes accessing a dataset, selecting a list of parameters of the dataset, each of the parameters being selected based on a determination that the parameter is affecting a size of the dataset and/or affecting an amount of data storage space consumed by the dataset, performing a counterfactual analysis using the parameters, and using results of the counterfactual analysis to generate a recommendation that identifies one or more data protection actions which influence utilization of the data storage space, and modifying utilization of the data storage space by implementing one of the data protection actions.

Abstract: Techniques described herein relate to methods for managing backup and restore operations. Such a method may include performing a vProxy health analysis to obtain vProxies assigned a healthy label; performing a confidence analysis to assign a health confidence score to each separate healthy label for each vProxy of the plurality of vProxies; filtering the plurality of vProxies to obtain a set of vProxies, each having a separate health confidence score over a confidence score threshold; calculating average throughput for each vProxy in the set of vProxies; assigning an optimal label to a vProxy of the set of vProxies based on the vProxy having an average throughput over a throughput threshold; and presenting a list of optimal vProxies comprising the vProxy to an entity configuring a backup job. The entity may select the vProxy for use in the backup job in response to being presented the optimal label of the vProxy.

Abstract: One example method includes selecting a best performing memory for an operation. A data protection system may maintain a global view of performance data for multiple namespaces. The performance data may reflect latency and/or bandwidth for each of the namespaces. The global view may be updated. When performing an operation, the best performing namespace can be selected from the global view based on performance.

Abstract: A method for managing a plurality of storage devices includes obtaining, by a storage device cleaning manager, a set of self-monitored statistics, performing an initial concern analysis to generate an initial concern prediction for each of the plurality of storage devices in a storage system, wherein the set of self-monitored statistics are associated with the plurality of storage devices, updating a cleaning policy based on the initial concern prediction, obtaining input/output (I/O) statistics, after updating the cleaning policy based on the initial concern prediction, performing a secondary concern analysis using the I/O statistics to generate a secondary concern prediction for each of the plurality of storage devices, wherein the I/O statistics are associated with the plurality of storage devices, further updating the cleaning policy, and performing a cleaning of at least a portion of the plurality of storage devices based on the updated cleaning policy.

Abstract: One example method includes defining a configuration of SCM, receiving a stream of IOs from a client, analyzing the stream of IOs and, based on the analyzing, estimating future IO behavior of the client, and based on estimated future IO behavior of the client, reconfiguring one or more parameters of the configuration of the SCM to define an updated configuration of the SCM, and the updated configuration of the SCM enables an improvement in IO performance relative to an IO performance of the configuration of the SCM.

Abstract: One example method includes accessing a dataset, selecting a list of attributes of the dataset, each of the attributes being selected based on a determination that the attribute is affecting growth of the dataset and affecting an amount of data storage space consumed by the dataset, assigning a SHAP score to each attribute, using the SHAP scores to assign respective weights to each attribute, deriving drift and shock information for the dataset, and based on the drift and shock information, calculating a risk score that a storage capacity of an asset where the dataset is stored will be exhausted within a particular time interval.

Abstract: One example method includes selecting a best performing memory for an operation. A data protection system may maintain a global view of performance data for multiple namespaces. The performance data may reflect latency and/or bandwidth for each of the namespaces. The global view may be updated. When performing an operation, the best performing namespace can be selected from the global view based on performance.

Abstract: The described technology is generally directed towards predicting the survival of a storage device (e.g., a hard disk drive or a solid state drive) to a specified time point, expressed as a confidence score, via a prediction uncertainty quantifier framework with a machine learning classifier. The confidence score corresponds to the likelihood of a storage device surviving until a specified time point (e.g., for n hours). In one implementation, a conformal prediction framework provides the confidence score for a storage device, based on survival rate data predicted using recent telemetry data collected for that storage device by an online semi-parametric Mondrian survival forest classifier. Updated confidence scores based on updated telemetry data can be obtained at various evaluation stages to reevaluate whether to take remedial action with respect to a storage device (e.g., replace the storage device). Multiple storage devices can be ranked by their respective associated confidence scores.

Abstract: Techniques described herein relate to methods for managing backup and restore operations. The method may include obtaining a health data items associated with vProxies; performing a first clustering analysis using the health data items to apply a first vProxy health label to a first portion of the vProxies and a second vProxy health label to a second portion of the vProxies; performing a first confidence analysis to determine a separate vProxy health confidence value for the first vProxy health label assigned to each of the first portion of vProxies; ranking the first portion of vProxies based on the first confidence analysis to obtain a ranked vProxy health list; receiving a request to perform a backup and restore management operation; and providing, in response to the request, a portion of the ranked vProxy health list to an entity requesting the backup and restore management operation.

Abstract: A method and system for risk score based asset data protection using a conformal framework. Concerning Internet-of-Things (IoT) environments, there is a growing challenge to ensure the availability and continuity of applications executing, as well as relevant application data maintained, on IoT devices, in order to mitigate potential operational failures and/or interruptions. The disclosed method and system address this growing challenge through risk-score assessments of the applications and their respective relevant application data (collectively referred to as assets), as well as the potential degradation of the underlying IoT device hardware. The aforementioned risk-score assessments employ clustering to identify, and a conformal framework to rank, high-risk (for failure) assets, which may subsequently be subjected to backup operations (overriding existing policies) to guarantee asset protection.

Abstract: A method and system for health rank based virtual machine restoration using a conformal framework. With respect to enterprise data protection, solutions need to address two primary responsibilities: at the onset of failure or disaster, restore any running applications, as well as any lost or damaged data; and minimize (if not eliminate) the future occurrence of such failures or disasters. In tackling the latter responsibility, the disclosed method and system leverage health-score assessments to ensure the restoration of virtual machines onto healthy infrastructure. The aforementioned health-score assessments employ clustering to identify, and a conformal framework to rank, healthy hosts onto which virtual machines may be restored.

Abstract: Techniques described herein relate to methods for managing backup and restore operations. The method may include obtaining a health data items associated with vProxies; performing a first clustering analysis using the health data items to apply a first vProxy health label to a first portion of the vProxies and a second vProxy health label to a second portion of the vProxies; performing a first confidence analysis to determine a separate vProxy health confidence value for the first vProxy health label assigned to each of the first portion of vProxies; ranking the first portion of vProxies based on the first confidence analysis to obtain a ranked vProxy health list; receiving a request to perform a backup and restore management operation; and providing, in response to the request, a portion of the ranked vProxy health list to an entity requesting the backup and restore management operation.

Abstract: Techniques described herein relate to methods and systems for thermal management of a thermal environment. The method may include using thermal data items from computing devices and time series analysis to predict future thermal values for the thermal data items; performing a clustering analysis using the predicted future thermal values to assign cluster labels to the computing devices; using the cluster labels and the predicted future thermal values to assign predicted thermal status labels to the computing devices; assigning a confidence value to the predicted thermal status labels and ranking the computing devices based on the confidence values; performing an analysis to determine a thermal data item contributing to the assigned thermal status; and sending the results to a thermal environment administrator.