Abstract: Embodiments for providing automated selection of optimal disk types for virtualized storage by defining a minimum number of backup samples, selecting, if the minimum number of backup samples is not met for a backup operation, a solid state drive (SSD) for a virtual machine (VM) storage for a disaster recovery operation, otherwise selecting a hard disk drive (HDD) for the VM storage. The method further defines a cold HDD threshold (CHT) value and a minimal percentage of backups (PPT) value, and obtains a cold backup count based on the CHT value. It compares a ratio of the cold backup count to an amount of backups (AB) for the disaster recovery operation to the defined PPT value, and if the ratio is greater than the PPT value, it selects the SSD rather than HDD for the VM storage.

Abstract: One example method includes chunking a respective disk of each of a plurality of virtual machines (VM) to create a respective plurality of chunks associated with each of the VMs, creating, based on the chunking process, a cluster comprising one or more of the VMs, creating a VM template whose data and disk structure match respective data and disk structures of each of the VMs in the cluster, and in response to a file operation involving a first one of the VM disks, defragmenting the first VM disk so that a disk structure of the first VM disk is the same as a disk structure of the VM template.

Abstract: A method and system for provenance-based data backups. Specifically, the method and system disclosed herein entail generating and, subsequently, using data provenance to filter which collections of data, produced through the data mining of big data, should be replicated and stored in backup storage.

Abstract: The selection of an optimal restore instance type based on a customer's speed/cost tradeoff resolution is disclosed. An automated restore activity may be performed on a baseline test VM of a predefined size using different restore instance types. The number of calibration runs or evaluations needed to identify an optimal restore instance type in terms of performance and price, with respect to bandwidth or other constraining factor, is performed on less than all of the restore instance types.

Abstract: The selection of an optimal restore instance type based on a customer's speed/cost tradeoff resolution is disclosed. Aspects of the anticipated completion time to complete the recovery and completion cost to perform the recovery may be extrapolated based on a baseline or test recovery and/or actual recovery times and costs. An automated restore activity may be performed on a baseline test VM of a predefined size using different restore instance types. An optimal restore instance type is used to form worker VMs that perform the recovery operations.

Abstract: Embodiments for providing automated selection of optimal disk types for virtualized storage by defining a minimum number of backup samples, selecting, if the minimum number of backup samples is not met for a backup operation, a solid state drive (SSD) for a virtual machine (VM) storage for a disaster recovery operation, otherwise selecting a hard disk drive (HDD) for the VM storage. The method further defines a cold HDD threshold (CHT) value and a minimal percentage of backups (PPT) value, and obtains a cold backup count based on the CHT value. It compares a ratio of the cold backup count to an amount of backups (AB) for the disaster recovery operation to the defined PPT value, and if the ratio is greater than the PPT value, it selects the SSD rather than HDD for the VM storage.

Abstract: One example method includes processing a file of a VM to create a decreased quality file that is a version of the file, storing the decreased quality file at a recovery site along with metadata indicating a file type and path name for the decreased quality file, in response to a disaster recovery request, creating a partial user VM at the recovery site, and the partial user VM includes the decreased quality file, and with the partial user VM, serving the decreased quality file to a user in response to a request from the user.

Abstract: Virtual machines can be discretely tested for disaster recovery or other data protection operations. To perform these types of data protection operations, a single virtual machine or a set of virtual machines is migrated to a target site and the migrated virtual machines are replaced with dummy virtual machines at the source site. The dummy virtual machines replicate communications. This allows the migrated virtual machines to be tested in the context of disaster recovery to a target site such as the cloud.

Abstract: One example method includes receiving input concerning a boot order sequence, where the input includes VM metadata, entering a training phase which includes generating a boot sequence rule based on the input, using the boot sequence rule to generate a proposed boot sequence, performing the proposed boot sequence, and gathering information concerning performance of the proposed boot sequence. The gathered information can be used as a basis to generate a modified boot sequence.

Abstract: One example method includes receiving input concerning a boot order sequence, where the input includes VM metadata, entering a training phase which includes generating a boot sequence rule based on the input, using the boot sequence rule to generate a proposed boot sequence, performing the proposed boot sequence, and gathering information concerning performance of the proposed boot sequence. The gathered information can be used as a basis to generate a modified boot sequence.

Abstract: Described is a system that leverages an isolated disaster recovery (or testing) environment to manage a range of cyber security threats associated with a virtual machine. The system may proactively manage such security threats by creating a second instance (e.g. replica) of a production virtual machine and verifying potential configuration updates that may be applied to the virtual machine to manage such security threats. The configuration updates may be verified by performing various tests on the second instance of the virtual that executes within the secure and isolated environment. Accordingly, the configuration updates may be verified without exposing the production environment to any potential risks. Thus, the system may proactively detect cyber security threats associated with a virtual machine and automatically initiate various corrective or preventative actions.

Abstract: One example method includes receiving input concerning a boot order sequence, where the input includes VM metadata, entering a training phase which includes generating a boot sequence rule based on the input, using the boot sequence rule to generate a proposed boot sequence, performing the proposed boot sequence, and gathering information concerning performance of the proposed boot sequence. The gathered information can be used as a basis to generate a modified boot sequence.

Abstract: Described is a system that recovers multiple virtual machines (or other computing resource) by leveraging similarities between recovery (or backup) data. The system may identify the recovery data for a particular virtual machine as a primary set of data using specialized calculations and/or heuristics. The system may leverage this primary set of data by replicating a data storage for the remaining number of virtual machines to be recovered. The system may then determine a remaining (or differing) set of data for each of the remaining virtual machines to be recovered. Accordingly, the system may only be required to rehydrate the differing set of data chunks and write those data chunks to the data storage associated with each recovery virtual machine. These recovery virtual machines may then be used to restore corresponding virtual machines within a production environment.

Abstract: One example method includes chunking a respective disk of each of a plurality of virtual machines (VM) to create a respective plurality of chunks associated with each of the VMs, creating, based on the chunking process, a cluster comprising one or more of the VMs, creating a VM template whose data and disk structure match respective data and disk structures of each of the VMs in the cluster, and in response to a file operation involving a first one of the VM disks, defragmenting the first VM disk so that a disk structure of the first VM disk is the same as a disk structure of the VM template.

Abstract: Systems and methods for automatically generating a boot sequence. A multiple virtual machine computing environment is analyzed to generate a boot sequence that is used during a recovery operation. The boot sequence may be based on applications and application types running on the virtual machines, a network configuration and network traffic, and on manual boots of virtual machines. The boot sequence prioritizes the order in which the virtual machines are booted in the recovery site.

Abstract: One example method includes receiving data segments that are not already part of a full disk image of a backup, storing the data segments in storage, determining whether or not an aggregate total of data segments in the storage, that are not already part of a full disk image of a backup, equals or exceeds a threshold, when the aggregate total of data segments in the storage equals or exceeds the threshold, creating a full disk image of a backup that includes the data segments in storage, and storing the created full disk image of the backup to a recovery disk.

Abstract: One example method includes receiving data segments that are not already part of a full disk image of a backup, storing the data segments in storage, determining whether or not an aggregate total of data segments in the storage, that are not already part of a full disk image of a backup, equals or exceeds a threshold, when the aggregate total of data segments in the storage equals or exceeds the threshold, creating a full disk image of a backup that includes the data segments in storage, and storing the created full disk image of the backup to a recovery disk.

Abstract: Embodiments for systems and methods of providing a boot order for containers in a cloud native application environment by collecting container environment data from a first container site; determining dependencies and connections between the containers and applications executed within the containers based on a number of system parameters; calculating a recommended order for booting or rebooting the containers during a disaster recovery process; and communicating the recommended order to a system administrator through a graphical user interface (GUI) for acceptance or modification by the system administrator.

Abstract: One example includes performing a VM restore instance type discovery process, creating a test VM with a VM restore instance type matching a VM restore instance type identified during discovery, using the test VM to create a test restore VM at a cloud storage site, restoring the test VM at the cloud storage site using the test restore VM, generating a 4-D baseline vector based on the restoration of the test VM, the 4-D baseline vector identifying a particular VM restore instance type, generating a 5-D vector based on the 4-D baseline vector, ranking the 5-D vector relative to other 5-D vectors, the 5-D vectors identifying the same production site VM, and restoring, at the cloud storage site, the production site VM identified in the 5-D vectors, the production site VM restored at the cloud storage site has a VM restore instance type identified in the highest ranked 5-D vector.

Abstract: Methods, systems, and computer program products for cloud to cloud disaster recovery are disclosed. Exemplary embodiments may: discover one or more virtual machines associated with a first auto scaling group; and generate a plan to backup the one or more virtual machines to a second cloud.