Abstract: One or more indications that input/output (I/O) operations have been performed by a storage system are received from the storage system. An amount of energy consumed by the storage system to perform the I/O operations id determined. An energy usage effectiveness of the storage system is generated based on a number of the I/O operations performed by the storage system and the amount of power consumed by the storage system to perform the plurality of I/O operations.

Abstract: Non-disruptively moving a storage fleet control plane, including deploying, on an edge device, one or more agents that are managed by a control plane residing in a cloud computing environment; mediating one or more API requests, generated by the control plane, directed to the one or more agents on the edge device; and migrating, in response to a first condition, the control plane to the edge device.

Abstract: A method is provided. The method include obtaining, from a first system, a set of user requirements associated with one or more storage systems. The method also includes obtaining, from a second system, a set of storage system configurations based on the set of user requirements. The method further includes obtaining, from a third system, a set of costs for the set of storage system configurations. The method further includes generating multimedia content based on the set of storage system configurations, the multimedia content providing information about the set of storage system configurations. The method further includes providing the multimedia content to one or more client devices.

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: 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: Using large language models (‘LLMs’) for code hardening in a storage system, including: creating, in a storage system, a cloned software deployment based on a software deployment executed in the storage system, wherein the cloned software deployment is associated with a different tenant of the storage system than the software deployment; generating, using a large language model (LLM) and based on input data comprising data describing an error in the software deployment, a code update to fix the error; verifying the code update, including updating the cloned software deployment based on the code update; and updating the software deployment based on the verified code update.

Abstract: Managing system assets using generative artificial intelligence, including: gathering data describing characteristics of a plurality of storage media; and generating, by a generative artificial intelligence (AI) model, based on the data and one or more attributes of a particular storage medium, a characterization of the particular storage medium.

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.

Abstract: Queryable object or file metadata catalog, including: receiving a query to a metadata catalog comprising metadata describing data stored on a storage system; selecting, from the metadata catalog, a subset of the metadata responsive to the query; and providing a subset of the data stored on the storage system corresponding to the subset of the metadata.

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: 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: Data associated with a first set of managed flash storage devices 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 set of managed flash storage devices from the data. A type of change associated with a second set of managed flash storage devices is determined by the machine learning model based on a comparison of the one or more characteristics of the first set of managed flash storage devices and one or more characteristics of the second set of managed flash storage devices. The type of change associated with the second set of managed flash storage devices is provided to a cloud services provider of the cloud-based storage system.

Abstract: Dynamic interfaces created using generative artificial intelligence, including: receiving a natural language input via a natural language interface for a storage system; providing an input to a generative artificial intelligence (AI) model comprising the natural language input; and receiving, from the generative AI model and based on the input, one or more user interface elements.

Abstract: Providing data management as-a-service, including: presenting one or more available data services to a user; receiving a selection of one or more selected data services; and applying, in dependence upon the one or more selected data services, one or more data services policies to a dataset associated with the user.

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.

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: 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: 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: Secure sharing of data subsets within a cloud environment, including: receiving, from a requestor, a request to access a dataset that is stored within a cloud-based storage system; identifying one or more characteristics associated with the requestor, identifying one or more characteristics associated with the dataset; and providing the requestor with at least a portion of the dataset, wherein the portion of the dataset that is provided to the requestor is selected in dependence upon the one or more characteristics associated with the requestor and the one or more characteristics associated with the dataset.

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.