Abstract: Content including information describing an issue with a storage system is received. The content is analyzed by a generative artificial intelligence (AI) model to identify the issue described by the content. A solution for the issue is determined based on at least one of information associated with the storage system or information associated with one or more other storage systems. A response including a description of the solution for the issue is generated.

Abstract: Data associated with a first storage media of a cloud-based storage system is provided as an input to a machine learning model executed by a processing device that identifies one or more characteristics of the first storage media from the data. A type of change associated with the second storage media is determined by the machine learning model based on a comparison of the one or more characteristics of the first storage media and one or more characteristics of a second storage media. The type of change associated with the second storage media is provided to a cloud services provider of the cloud-based storage system.

Abstract: Disaggregated storage systems for hyperscale deployments including retrieving workload metrics and a workload policy associated with a workload executing within a fleet of storage systems; accessing a fleet configuration of the fleet of storage systems; and generating, based on the workload metrics, the workload policy, and the fleet configuration, a fleet reconfiguration recommendation indicating a change to hardware installed on a storage system in the fleet of storage systems.

Abstract: Role enforcement for storage-as-a-service including managing, for a storage system, a plurality of roles including a storage consumer role and a storage provider role, wherein the storage consumer role is associated with data management instructions that are enabled for the storage consumer role and disabled for the storage provider role, and wherein the storage provider role is associated with storage management instructions that are enabled for the storage provider role and disabled for the storage consumer role; servicing a data management instruction from a first client associated with storage consumer role, wherein the data management instruction is an instruction to manipulate data on the storage system; and servicing a storage management instruction from a second client associated the storage provider role, wherein the storage management instruction is an instruction to manage the storage system.

Abstract: Data associated with storage media utilized by one or more storage systems is received. The data is provided as an input to a machine learning model executed by a processing device. The machine learning model identifies one or more deterministic characteristics from the data. The one or more deterministic characteristics associated with the storage media are received from the machine learning model. A data structure comprising the one or more deterministic characteristics is generated for use in a telemetry process to qualify types of storage media.

Abstract: Assessing protection for storage resources, including: identifying a set of active data protection features for one or more storage resources; generating a data protection assessment based on the set of active data protection features; and reporting the data protection assessment.

Abstract: Dynamic storage instance sizing for application deployments, including: deploying, based on a profile, an instance of a database application, wherein the profile defines one or more characteristics for the instance of the database application; monitoring one or more usage metrics for the instance of the database application; and updating, in the profile, the one or more characteristics based on the usage metrics.

Abstract: Remediating vulnerabilities for application deployments, including: selecting an image conforming to a defined security policy; and migrating one or more application instances to one or more cloud computing instances generated based on the selected image.

Abstract: Context driven user interfaces for storage systems including receiving, from a user account, a request to access a system interface for a system; identifying at least one critical system characteristic that describes a current aspect of the system; reconfiguring the system interface based on the at least one critical system characteristic; and presenting the reconfigured system interface to a user of the user account.

Abstract: A dynamic, personality-driven user experience that includes receiving, from a user account, a request to access a system interface for a system; identifying, based on personality indicators for the user account, a user account personality from a plurality of user account personalities, wherein each of the personality indicators is associated with at least one of the plurality of user account personalities; reconfiguring the system interface based on the identified user account personality; and granting, to the user account, access to the reconfigured system interface, including presenting the reconfigured system interface to a user of the user account.

Abstract: Profiling user activity to achieve social and governance objectives, including: generating, based on data describing activities within a system, a plurality of activity groupings each including one or more user accounts and corresponding to a particular activity in the system; generating, for each of the plurality of activity groupings, one or more social groupings based on user profiles of the one or more user accounts in a corresponding activity grouping, wherein each of the one or more social groupings corresponds to one or more particular user profile attributes; identifying, for a particular user account, one or more of the activity groupings having a social grouping corresponding to user profile attributes of the particular user account; and modifying one or more user experience features of the system based on the identified one or more activity groupings.

Abstract: Adaptive storage processing for storage-as-a-service, including detecting, by a cloud-based monitoring system, a storage system state for a storage system by monitoring the storage system in real-time remotely via a network; selecting, by the cloud-based monitoring system based on the storage system state, an entry in a tunables repository, wherein the entry in the tunables repository comprises a tunable parameter for the storage system state; accessing, by the cloud-based monitoring system via the network, a gateway for the storage system; and modifying, by the cloud-based monitoring system via the gateway, the tunable on the storage system based on the tunable parameter for the storage system state.

Abstract: Data associated with storage media utilized by one or more storage systems is received. The data is provided as an input to a machine learning model executed by a processing device. The machine learning model identifies one or more deterministic characteristics from the data. The one or more deterministic characteristics associated with the storage media are received from the machine learning model. A data structure comprising the one or more deterministic characteristics is generated for use in a telemetry process to qualify types of storage media.

Abstract: Using replication to create storage service tiers, including: receiving a request for data stored in a first storage array and not stored in a second storage array associated with a lower latency tier than the first storage array; providing the data from the first storage array in response to the request; and asynchronously replicating the data from the first storage array to the second storage array.

Abstract: Providing storage tailored for a storage consuming application, including: identifying, for an application that utilizes storage resources within a cloud-based storage system, one or more storage performance characteristics associated with the application; comparing the storage performance characteristics of the application that were identified with storage performance characteristics of storage resources of one or more cloud-based storage systems; and selecting, based on the comparing, one or more storage resources within the one or more cloud-based storage systems to provide storage services to the application.

Abstract: Software lifecycle management for a storage system, including: generating, based on user input, a user-defined software lifecycle management policy for software on one or more storage systems; identifying, in dependence upon on a software configuration of the one or more storage systems, one or more candidate updates for the software; and generating, in dependence upon the user-defined software lifecycle management policy, lifecycle management data that identifies one or more conforming updates from among the one or more candidate updates.

Abstract: Adaptive storage processing for storage-as-a-service, including detecting, by a cloud-based monitoring system, a storage system state for a storage system by monitoring the storage system in real-time remotely via a network; selecting, by the cloud-based monitoring system based on the storage system state, an entry in a tunables repository, wherein the entry in the tunables repository comprises a tunable parameter for the storage system state; accessing, by the cloud-based monitoring system via the network, a gateway for the storage system; and modifying, by the cloud-based monitoring system via the gateway, the tunable on the storage system based on the tunable parameter for the storage system state.

Abstract: Fingerprint-based database container deployment, including: identifying, in dependence upon data collected from one or more storage systems in a storage environment, characteristics of a plurality of database workloads on the one or more storage systems; identifying, in dependence upon the characteristics, fingerprint information for a plurality of database workload types; and deploying, in dependence upon the fingerprint information for a particular database workload type, a database container for the particular database workload type in the storage environment.

Abstract: Monitoring gateways to a storage environment including accessing, by a cloud-based monitoring system, a management gateway for a fleet of storage systems in a storage environment; determining, by the cloud-based monitoring system via the management gateway, a location for each storage system in the fleet of storage systems; monitoring, by the cloud-based monitoring system via the management gateway, the fleet of storage systems using the location of each storage system by crawling each storage system to retrieve storage system characteristics; and generating, by the cloud-based monitoring system, a fleet alteration proposal based on the storage system characteristics from each storage system in the fleet of storage systems.

Abstract: Rebalancing in a fleet of storage systems using data science including generating, by the cloud-based rebalancing system, a plurality of workload migration scenarios to address a detected workload imbalance among a plurality of workloads in a fleet of storage systems; determining, by the cloud-based rebalancing system, a plurality of movement vectors for each workload migration scenario, wherein each of the plurality of movement vectors describes a consideration factor for migrating a workload of the plurality of workloads within the fleet of storage systems; and generating, by the cloud-based rebalancing system, at least one rebalancing proposal based on the plurality of movement vectors for each workload migration scenario.