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: 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 set of restore jobs are created to restore a backup. The backup may be maintained across a set of storage devices connected to a backup server. The set of storage devices have different levels of performance and each restore job is responsible for restoring from a respective storage device. Information describing arrangements of processors and memory units is reviewed to create a set of heterogeneous domains, each domain including a processor and memory unit. Performance characteristics, including latency and bandwidth measurements, of the heterogeneous domains are examined. The heterogeneous domains are ranked based on their performance characteristics. The restore jobs are assigned to the heterogeneous domains by successively matching a restore job associated with a storage device having a lowest level of performance relative to other unmatched storage devices with a heterogeneous domain having a highest level of performance relative to other unmatched heterogeneous domains.

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: Drives of a RAID group are classified as either healthy or failing using a trained learning process. The failure probability is then determined for each failing drive using a Venn-ABERS framework which provides a boundary range on the failure prediction probability. A variable sparing mechanism is used to enable one or more drives of the RAID group to be used as dual-purpose drives. In a first state, the dual-purpose drives are user-addressable drives and are available to be used to process IO workload on the RAID group. Spreading the IO workload on the RAID group across a larger number of drives results in increased performance in the form of reduced latency. In a second state, the dual-purpose drives are not user-addressable and are spare drives in the RAID group, which improves the level of protection provided to data stored in the RAID group.

Abstract: Embodiments described herein relate to techniques for placing backup data based on health scores. The techniques may include: obtaining data items associated with a first data domain restorer; obtaining data items associated with a second data domain restorer; making a prediction that the first data domain restorer is operating normally; making a prediction that the second data domain restorer is operating normally; assigning a confidence value to the first prediction; making a classification of the first data domain restorer in a first group based on the confidence value; assigning a confidence value to the second prediction; making a classification of the second data domain restorer in a second group based on the confidence value; and performing a data backup to the first data domain restorer from a first computing device based on the classification and a first service level required for the first computing device.

Abstract: Non-volatile Random Access Memory (NVR) on a storage system may be dynamically converted between use as temporary memory in a memory context and use as persistent memory in a storage context. NVR (e.g., embodied as DIMM) may be utilized in a hybrid capacity, where some of the NVR is used as memory and some of the NVR is used as storage, and where NVR memory is converted to memory as needed, dynamically as I/O is being processed using the NVR. A host system may be directly connected to an internal switching fabric of the data storage system without an intervening component of the storage system (e.g., a director) controlling access of the host system to the internal fabric or to the memory. The host system may provision and use the NVR as storage by directly communicating with the NVR over the internal fabric, for example, using RDMA.

Abstract: Systems and methods for transferring firmware updates using an in-band interface are described. In an embodiment, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive a firmware update package having a payload; create a namespace in a persistent memory; copy the payload to a segment of the persistent memory associated with the namespace; and write, to an Extensible Firmware Interface (EFI) variable, an Emulator Device Path (EDP) pointing to the payload.

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: A kexec-based system update process wherein user-specific data is transferred on reboot of the second kernel. Upon initializing kexec load, buffer memory is assigned to the second kernel and the system loads control pages of fixed size for the second kernel boot, and also loads user-specific data onto extended control pages of variable size. Upon boot of the second kernel, the user-specific data is extracted from the extended control pages and transferred to the corresponding applications.

Abstract: Techniques described herein relate to a method for forecasting backup failures. Such techniques may include: obtaining data items associated with backup jobs; writing entries in a time series database, the entries comprising successful backup jobs and failed backup jobs; performing a first analysis to predict future failed backup jobs based on the entries in the time series database to obtain a future backup job failure predictions; performing a second analysis to determine a confidence prediction for each of the future backup job failure predictions; ranking the future backup job failure predictions based on the second analysis; performing a third analysis to determine at least one variable leading to each of the future backup job failure predictions; and sending results of the second analysis and the third analysis to an administrator of a data domain.

Abstract: Techniques described herein relate to a method for forecasting backup failures. Such techniques may include: obtaining data items associated with backup jobs; writing entries in a time series database, the entries comprising successful backup jobs and failed backup jobs; performing a first analysis to predict future failed backup jobs based on the entries in the time series database to obtain a future backup job failure predictions; performing a second analysis to determine a confidence prediction for each of the future backup job failure predictions; ranking the future backup job failure predictions based on the second analysis; performing a third analysis to determine at least one variable leading to each of the future backup job failure predictions; and sending results of the second analysis and the third analysis to an administrator of a data domain.

Abstract: Embodiments described herein relate to techniques for placing backup data based on health scores. The techniques may include: obtaining data items associated with a first data domain restorer; obtaining data items associated with a second data domain restorer; making a prediction that the first data domain restorer is operating normally; making a prediction that the second data domain restorer is operating normally; assigning a confidence value to the first prediction; making a classification of the first data domain restorer in a first group based on the confidence value; assigning a confidence value to the second prediction; making a classification of the second data domain restorer in a second group based on the confidence value; and performing a data backup to the first data domain restorer from a first computing device based on the classification and a first service level required for the first computing device.

Abstract: Drives of a RAID group are classified as either healthy or failing using a trained learning process. The failure probability is then determined for each failing drive using a Venn-ABERS framework which provides a boundary range on the failure prediction probability. A variable sparing mechanism is used to enable one or more drives of the RAID group to be used as dual-purpose drives. In a first state, the dual-purpose drives are user-addressable drives and are available to be used to process IO workload on the RAID group. Spreading the IO workload on the RAID group across a larger number of drives results in increased performance in the form of reduced latency. In a second state, the dual-purpose drives are not user-addressable and are spare drives in the RAID group, which improves the level of protection provided to data stored in the RAID group.

Abstract: Systems and methods are provided for identifying a safe window for creating a firmware update package. In some embodiments, an Information Handling System (IHS), may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: generate tokens based upon a log of historical firmware update package creation events; identify a plurality of workflows based upon the tokens; predict, based upon the plurality of workflows, a future workload of a firmware update package creation service; and indicate, based upon the future workload, a time window for creating a firmware update package using the firmware update package creation service that increases a probability of successful creation.

Abstract: Systems and methods are provided for context-aware maintenance window identification. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: estimate a completion time of a maintenance operation; predict future usage of the IHS; identify a time window for the maintenance operation based upon the estimation and the prediction.

Abstract: Systems and methods for transferring firmware updates using an in-band interface are described. In an embodiment, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive a firmware update package having a payload; create a namespace in a persistent memory; copy the payload to a segment of the persistent memory associated with the namespace; and write, to an Extensible Firmware Interface (EFI) variable, an Emulator Device Path (EDP) pointing to the payload.

Abstract: Methods, apparatus, and processor-readable storage media for automatically purging data using a deep neural network are provided herein. An example computer-implemented method includes training a neural network model using multiple types of attribute data and job-related data associated with historical workflow data maintained within multiple data structures of an enterprise system; dynamically analyzing workflows derived from the enterprise system, wherein dynamically analyzing the workflows comprises determining the multiple types of attribute data for the workflows and processing job-related data associated with the workflows into multiple data structures within one or more databases; applying the neural network model to the determined attribute data; and removing at least a portion of the multiple data structures from the one or more databases based at least in part on the application of the neural network model to the determined attribute data.

Abstract: Systems and methods are provided for identifying a safe window for creating a firmware update package. In some embodiments, an Information Handling System (IHS), may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: generate tokens based upon a log of historical firmware update package creation events; identify a plurality of workflows based upon the tokens; predict, based upon the plurality of workflows, a future workload of a firmware update package creation service; and indicate, based upon the future workload, a time window for creating a firmware update package using the firmware update package creation service that increases a probability of successful creation.

Abstract: Methods, apparatus, and processor-readable storage media for machine learning techniques for automated processing of workflow approval requests are provided herein. An example computer-implemented method includes generating a machine learning model for workflow approval requests, wherein generating the machine learning model comprises automatically learning temporal-based patterns from historical data related to previous workflow approval requests; generating, in response to an additional workflow approval request, an output value by applying the machine learning model to input data related to the additional workflow approval request; comparing the output value to at least one predefined threshold value; and automatically rejecting the additional workflow approval request responsive to a result of the comparing of the output value to the at least one predefined threshold value.