A distributed database system, a data disaster backup exercise method and a non-transitory computer-readable storage medium are disclosed. The distributed database system may include a local computer room (110) and an offsite computer room (120), where the local computer room (110) includes a local management node (111) and a local database cluster (112), the offsite computer room (120) includes an offsite management node (121), an offsite exercise database cluster (123) and an offsite synchronization database cluster (122); where the local database cluster (112) and the offsite synchronization database cluster (122) are both connected with the local management node (111); the offsite exercise database cluster (123) is configured for: establishing a first connection with the offsite management node (121); and receiving a test service sent by a service layer.

Disclosed are techniques for coordinating distributed backup data protection sites for alternating recording of point in time copies. For a monitored volume, or pool of monitored volumes, periodic point in time copies are recorded upon data storage capabilities of rotating backup data storage sites as each period elapses. Upon recording a point in time copy at a given backup data storage site, the given site broadcasts to other sites metadata about the point in time copies recorded by each of the backup data storage sites for the monitored volume. As subsequent periods elapse, a rotation of sites are cycled through for selection to record point in time copies for the given period such that point in time copies of the monitored volume are recorded across multiple backup data storage sites, with each backup data storage site recording point in time copies of the monitored volume snapshotted to different times.

Replication of a filesystem or a mount point or share may replicate all data that it consists of irrespective of where the data is stored. Replication protects data irrespective of location. One method is to replicate the filesystem namespace as is while skipping the data outside of the appliance/machine so that replication cost and time are reasonable. The data outside of the machine, like cloud/tape data is protected differently. One example method includes a data protection operation configured to replication a namespace associated with multiple data tiers. During replication, data from one of the tiers is skipped while all of the namespace metadata is replicated. The recovery restores the namespace metadata and the data that was replicated from the other tier. This may be performed in connection with cyber security, for example when replicating multi-tier data to a vault.

Techniques described herein relate to systems and methods of data storage, and more particularly to providing layering of file system functionality on an object interface. In certain embodiments, file system functionality may be layered on cloud object interfaces to provide cloud-based storage while allowing for functionality expected from a legacy applications. For instance, POSIX interfaces and semantics may be layered on cloud-based storage, while providing access to data in a manner consistent with file-based access with data organization in name hierarchies. Various embodiments also may provide for memory mapping of data so that memory map changes are reflected in persistent storage while ensuring consistency between memory map changes and writes. For example, by transforming a ZFS file system disk-based storage into ZFS cloud-based storage, the ZFS file system gains the elastic nature of cloud storage.

Methods, computer program products, and systems are presented. The methods include, for instance: analyzing a dataset associated with a service provided by the data protection service provider in order to determine a policy for when and how to replicate the respective components of the dataset corresponding to the service from a source site to a target site, such that the target site may perform the service with a minimum cost.

A method for enabling data set changes to be reverted to a prior point in time or state is disclosed. In one embodiment, such a method includes providing a data set comprising one or more data elements and a specified number of generations of the data elements. In certain embodiments, the data set is a partitioned data set extended (PDSE) data set, and the data elements are “members” within the PDSE data set. The method further includes tracking changes made by a job to data elements of the data set. The method further references, in a data structure (also referred to herein as a “cluster”) associated with the job, previous generations of the data elements changed by the job. In certain embodiments, the data structure is stored in the data set. A corresponding system and computer program product are also disclosed.

A system for displaying a performance dashboard comprises an input interface, a processor, and an output interface. The input interface is configured to receive log data. The log data comprises a set of process log entries. The processor is configured to determine one or more daemon response times and to determine dashboard information. The dashboard information is based at least in part on the log data and the one or more daemon response times. The output interface is configured to provide the dashboard information.

Example implementations relate to determining and implementing a feasible resource optimization plan for public cloud consumption. Telemetry data over a period of time is obtained for a current deployment of virtual infrastructure resources within a current data center of a cloud provider that supports an existing service and an application deployed on the virtual infrastructure resources. Information regarding a set of constraints to be imposed on a resource optimization plan is obtained. Indicators of resource consumption relating to the currently deployed virtual infrastructure resources during the period of time are identified by applying a deep learning algorithm to the telemetry data. A resource optimization plan is determined that is feasible within the set of constraints based on a costing model associated with resources of an alternative data center of the cloud provider, the indicators of resource consumption and costs associated with the current deployment.

A persistent memory unit for a computer system where the memory unit can detect a catastrophic event and automatically backup volatile memory into non-volatile memory. The memory unit can operate with a limited number of power inputs and detect the loss of power and then initiate a backup after the volatile memory of the memory unit has entered a stable self-refresh mode. The memory unit uses an on-board power management interface controller capable of redistributing power from an input power line and generating different power levels for different components on the memory unit.

Replication of a filesystem or a mount point or share may replicate all data that it consists of irrespective of where the data is stored. Replication protects data irrespective of location. One method is to replicate the filesystem namespace as is while skipping the data outside of the appliance/machine so that replication cost and time are reasonable. The data outside of the machine, like cloud/tape data is protected differently. One example method includes a data protection operation configured to replication a namespace associated with multiple data tiers. During replication, data from one of the tiers is skipped while all of the namespace metadata is replicated. The recovery restores the namespace metadata and the data that was replicated from the other tier. This may be performed in connection with cyber security, for example when replicating multi-tier data to a vault.