Abstract: Synchronously replicating a dataset across cloud-based storage systems, including adding a cloud-based storage system to a set of storage systems that the dataset is synchronously replicated across, where access operations are applied to the dataset equivalently through all storage systems in the set, all storage systems in the set store a separate copy of the dataset, and operations to modify the dataset performed and completed through any of the storage systems in the set are reflected in access operations to read the dataset, the cloud-based storage system including one or more cloud computing instances executing a storage controller application, a virtual drive layer that includes one or more cloud computing instances with local storage for storing at least a portion of the dataset as block data, and an object storage layer for storing at least a portion of the dataset as object data.

Abstract: Systems and methods for orchestrating coordinated snapshots across distinct storage environments are disclosed. The methods include steps for determining, for storage systems storing portions of a dataset, that a local checkpoint at a storage system of the storage systems meets dependency requirements for a coordinated checkpoint of the dataset relative to another local checkpoint at another storage system of the storage systems while storage operations are being applied to the dataset, and based on the determination, orchestrating the coordinated checkpoint.

Abstract: Establishing a guarantee for maintaining a replication relationship between object stores during a communications outage, an embodiment including identifying, by a first storage system, respective local clock values of one or more second storage systems, wherein the first storage system and the one or more second storage systems are among a plurality of storage systems replicating an object store, wherein the plurality of storage systems are configured to receive requests directed to the replicated object store; determining, by the first storage system in dependence upon the respective local clock values, respective clock differences of the one or more second storage systems relative to the first storage system; and ordering, by the first storage system, one or more updates to the replicated object store in dependence upon the respective clock differences.

Abstract: An illustrative method includes receiving, by a container storage interface (CSI) filter driver executing on a node of a cluster managed by a container orchestrator, a request to deploy a containerized application; selecting, by the CSI filter driver based on one or more attributes of the containerized application, a storage system from a plurality of storage systems attached to a plurality of clusters managed by the container orchestrator; and transmitting, by the CSI filter driver, a command to a CSI driver executing on a node of a cluster to which the storage system is attached, the cluster included in the plurality of clusters, the command configured to direct the CSI driver to provision a volume on the storage system for use with the containerized application.

Abstract: Symmetric storage using a cloud-based storage system, including: receiving, at a cloud-based storage system among storage systems synchronously replicating a dataset, an I/O operation directed to the dataset; determining, in dependence upon the I/O operation, a metadata update describing a mapping of segments of content to an address within a storage object, wherein the storage object includes the dataset; and synchronizing metadata on another storage system of the storage systems by sending the metadata update from the cloud-based storage system to the other storage system to update a metadata representation on the second storage system in accordance with the metadata update.

Abstract: An illustrative method includes a global control plane managing a plurality of local control planes that manage a respective plurality of distributed computing environments, each of the plurality of local control planes configured to manage its respective computing environment in accordance with a mode of operation that is selected from a plurality of modes of operation based on a respective connectivity status with the global control plane, wherein managing the plurality of local control planes comprises selectively deploying an update to one or more local control planes of the plurality of local control planes based on the connectivity statuses of the plurality of local control planes with the global control plane. In some embodiments, the global control plane receives log data associated with the update and deploys, based on the log data, a second update to the one or more local control planes.

Abstract: An illustrative method includes receiving, by a container storage interface (CSI) filter driver, a request provided by a source for a CSI driver associated with a storage system to perform a control plane operation with respect to the storage system, wherein the CSI filter driver and the CSI filter execute on a node of a cluster managed by a container orchestrator, and wherein the storage system is configured to store data associated with one or more containerized applications executing on the node; performing, by the CSI filter driver in response to receiving the request, an access control operation with respect the request; and allowing, by the CSI filter driver, the CSI driver to perform the control plane operation when the access control operation determines that the source is authorized to initiate the control plane operation.

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: Leveraging a large language model (‘LLM’) for managing a fleet of storage systems, including: receiving, by a fleet management service, data describing states of a plurality of storage systems in a fleet of storage systems; requesting, by the fleet management service and from a large language model (LLM), a fleet management recommendation based on the data; and receiving, by the fleet management service and from the LLM, a fleet management recommendation for the fleet of storage systems.

Abstract: Servicing I/O operations in a cloud-based storage system, including: receiving, by the cloud-based storage system, a request to write data to the cloud-based storage system; storing, in solid-state storage of the cloud-based storage system, the data; storing, in object storage of the cloud-based storage system, the data; detecting that at least some portion of the solid-state storage of the cloud-based storage system has become unavailable; identifying data that was stored in the portion of the solid-state storage of the cloud-based storage system that has become unavailable; retrieving, from object storage of the cloud-based storage system, the data that was stored in the portion of the solid-state storage of the cloud-based storage system that has become unavailable; and storing, in solid-state storage of the cloud-based storage system, the retrieved data.

Abstract: Sizing a virtual storage system, including: determining a change to one or more resource demands; determining, based on the change to the one or more resource demands, one or more modifications to one or more virtual components included as part of a virtual storage system architecture of a virtual storage system within a cloud computing environment; and initiating, responsive to the change to the one or more resource demands, the one or more modifications to the one or more virtual components included as part of the virtual storage system architecture of the virtual storage system, including replacing one or more of the virtual components with a higher performance virtual component.

Abstract: An illustrative method may include passing, by a container storage interface (CSI) filter driver, requests received by way of a node agent to a CSI driver associated with a storage system, the requests comprising requests for the CSI driver to perform control plane operations with respect to the storage system, wherein the node agent, the CSI filter driver, and the CSI filter all execute on a node of a cluster managed by a container orchestrator, and wherein the storage system is configured to store data associated with one or more containerized applications executing on the node; identifying, by the CSI filter driver based on the requests, attributes associated with the control plane operations; determining, by the CSI filter driver based on the attributes, a usage profile associated with the storage system; and performing, by the CSI filter driver based on the usage profile, an optimization operation.

Abstract: Managing disaster recovery to a cloud computing environment, including: receiving recovery objectives associated with a dataset that is stored in a primary storage system; creating a cloud-based storage system; suspending the cloud-based storage system; and resuming, periodically and based on the recovery objectives, the cloud-based storage system, including refreshing a copy of the dataset that is maintained by the cloud-based storage system.

Abstract: Taking recovery actions for replicated datasets, including: determining whether a request to modify a dataset that is synchronously replicated among a plurality of storage systems has been applied on a particular storage system of the plurality of storage systems, wherein the plurality of storage systems is synchronously replicating the dataset by acknowledging the request as being complete when each storage system has modified its copy of the dataset; and applying a recovery action based on whether the request to modify the dataset has been applied on the particular storage system of the plurality of storage systems.

Abstract: Replicating multiple storage systems utilizing coordinated snapshots, including identifying a replica dataset stored across two or more target storage systems, wherein the replica dataset is a replication target for a source dataset stored across two or more source storage systems; identifying two or more local replicated checkpoints that are replicated from the two or more source storage systems to the two or more target storage systems, wherein two or more local source checkpoints for the two or more local replicated checkpoints are associated with a coordinated source checkpoint for the source dataset; and determining, based on the two or more local replicated checkpoints, a coordinated target checkpoint for the replica dataset.

Abstract: Performing disaster recovery in a cloud-based storage system, including: creating, by a storage system a snapshot of a dataset; uploading, from the storage system to a cloud computing environment, the snapshot; storing, by the cloud computing environment, the snapshot; detecting, by the cloud computing environment, that the dataset is not available on the storage system; and creating, by the cloud computing environment using the snapshot that is stored within the cloud computing environment, a cloud-based storage system that includes the dataset.

Abstract: Managing storage systems that are synchronously replicating a dataset, including: detecting a change in membership to the set of storage systems synchronously replicating the dataset; and applying one or more membership protocols to determine a new set of storage systems to synchronously replicate the dataset, wherein the one or more membership protocols include a quorum protocol, an external management protocol, or a racing protocol, and wherein one or more I/O operations directed to the dataset are applied to a new set of storage systems.

Abstract: Modifying a storage system configuration using a large language model (‘LLM’), including: receiving a request for a deployment of a resource; generating, by a large language model (LLM), based on the request and a configuration of a computing environment, data facilitating a deployment of the resource in the computing environment; and providing the data facilitating the deployment of the resource in response to the request.

Abstract: An illustrative method may include detecting a request for an application deployed within a first compute environment to have access to a storage application deployed within a second compute environment; deploying, based on the request, an endpoint proxy within the first compute environment, the endpoint proxy configured to participate in a service mesh running within the second compute environment and authorize the application to securely access the storage application by way of a network that interconnects the first and second compute environments; and routing communications between the application and the storage application through the endpoint proxy and a sidecar proxy running within the second compute environment, the sidecar proxy also participating in the service mesh and configured to allow access to the storage application.

Abstract: Leveraging distinct storage tiers in a virtual storage system, including: receiving a request to write data to the virtual storage system; selecting, from amongst a plurality of tiers in the virtual storage system, a tier for receiving the data, wherein the plurality of tiers include a write-mostly tier and at least one other tier; and storing, within the selected tier within the virtual storage system, the data.