Abstract: A computing cluster backup system securely backs up and restores secrets of a computing cluster. The computing cluster backup system includes a local backup server, which is in a same network as the computing cluster, and a remote backup server, which may be in a different geographic location than the local backup server. The local backup server encrypts secrets of the computing cluster, and sends the encrypted secrets to the remote backup server for offsite backup. The local backup server is separate from the computing cluster, and thus backup and restoration may be agentless.

Abstract: In some examples, a system receives workload information of a workload collection, and applies a machine learning model on the workload information, the machine learning model trained using training information including features of different types of workloads. The system produces, by the machine learning model, an identification of a first file system from among different types of file systems, the machine learning model producing an output value corresponding to the first file system that is a candidate for use in storing files of the workload collection.

Abstract: In some examples, a system computes a rate of change of data blocks of a collection of storage volumes in a data store, where changes of data blocks are detected based on monitoring of operations on the collection of storage volumes. The rate of change of data blocks of the collection of storage volumes is based on a difference between a plurality of snapshots of data of the collection of storage volumes in the data store. Based on the rate of change of data blocks of the collection of storage volumes, the system dynamically adjusts a backup policy governing backups of data of the collection of storage volumes to a backup store, wherein the dynamic adjustment of the backup policy changes a quantity of the backups of data of the collection of storage volumes.

Abstract: In some examples, a system receives workload information of a workload collection, and applies a machine learning model on the workload information, the machine learning model trained using training information including features of different types of workloads. The system produces, by the machine learning model, an identification of a first file system from among different types of file systems, the machine learning model producing an output value corresponding to the first file system that is a candidate for use in storing files of the workload collection.

Abstract: Example implementations relate to application-specific policies for failing over from an edge site to a cloud. When an application becomes operational within an edge site, a discovery phase is performed by a local disaster recovery (DR) agent. I/O associated with a workload of the application is monitored. An I/O rate for data replication that satisfies latency characteristics of the application is predicted based on the incoming I/O. Based on results of tests against multiple clouds indicative of their respective RTO/RPO values, information regarding a selected cloud to serve as a secondary system is stored in an application-specific policy. The application-specific policy is transferred to a remote DR agent running in the selected cloud. Responsive to a failover event, infrastructure within a virtualized environment of the selected cloud is enabled to support a failover workload for the application based on the application-specific policy.

Abstract: Example implementations relate to application-specific policies for failing over from an edge site to a cloud. When an application becomes operational within an edge site, a discovery phase is performed by a local disaster recovery (DR) agent. I/O associated with a workload of the application is monitored. An I/O rate for data replication that satisfies latency characteristics of the application is predicted based on the incoming I/O. Based on results of tests against multiple clouds indicative of their respective RTO/RPO values, information regarding a selected cloud to serve as a secondary system is stored in an application-specific policy. The application-specific policy is transferred to a remote DR agent running in the selected cloud. Responsive to a failover event, infrastructure within a virtualized environment of the selected cloud is enabled to support a failover workload for the application based on the application-specific policy.

Abstract: Examples described herein relate to method and system for managing write modes of a filesystem, wherein the filesystem is mapped to a thinly-provisioned virtual volume (TPVV) defined using a common-provisioning group (CPG) of a set of physical storage devices. The filesystem may be operated in a read-write mode. A used storage capacity of the CPG may be monitored when the filesystem is operational in the read-write mode. Further, a check may be performed to determine whether the used storage capacity of the CPG has reached a storage fullness threshold value. Accordingly, if it is determined that the used storage capacity of the CPG has reached the storage fullness threshold value, the filesystem may be transitioned to a partial read-only mode from the read-write mode.

Abstract: In example implementations, a computing node is provided. The computing node includes a containerized application, a local storage partition, and a controller. A portion of the local storage partition is associated with portions of other local storage partitions to form a global cache. The controller executes the containerized application, receive an indication from a second computing node that the containerized application is being migrated, transfers data associated with the containerized application to the second computing node via the global cache.