Abstract: One example method includes performing a data management transaction, such as a data read operation, a data write operation, or a data delete operation, generating transaction metadata relating to the data management transaction, transmitting the transaction metadata to a blockchain network, and receiving, from the blockchain network, confirmation that the transaction metadata has been stored in a distributed ledger associated with the blockchain network.

Abstract: One example method includes performing a data management transaction, such as a data read operation, a data write operation, or a data delete operation, generating transaction metadata relating to the data management transaction, transmitting the transaction metadata to a blockchain network, and receiving, from the blockchain network, confirmation that the transaction metadata has been stored in a distributed ledger associated with the blockchain network.

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: One example method includes creating a backup of data, creating metadata associated with the backup, hashing the backup to create a backup hash, obtaining a key from a blockchain, generating an aggregate hash of a combination that includes the key and the backup hash, and transmitting the aggregate hash to a blockchain network. Because the aggregate hash is not modifiable when stored in a blockchain, an immutable record exists that establishes when a particular backup was created.

Abstract: Systems and methods for replicating a device image to a storage such as the cloud. The cloud is seeded with a base image that corresponds to the device. Changes between the contents of the device and the base image are identified, uploaded to the cloud, and applied to the image. The changes are tracked continuously and the image in the cloud can thus be used to restore the device to any point in time. The cloud image can also be used in a cloud based virtual machine that provides a user of the device with access to the device's contents via the cloud based image.

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 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: 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.

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: 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: Automatic network configuration for the recovery of virtual machines. A network configuration can be automatically provisioned by discovering the network topology of a source site and then matching the virtual machines to their locations or subnets in the network topology. The network is then implemented on a target site such that the required connectivity is present in the recovered target site even when the infrastructure of the source site and the target site are different.

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 configuring security groups during data protection operations including disaster recovery operations. In preparation for recovering a source site to a target site, security information at the source site is collected and classified. The classified security information is stored as a disaster recovery plan at least for security aspects of the disaster recovery process. The disaster recovery plan can be implemented at the target site such that security risks are minimized and connectivity errors are minimized during the recovery process.

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: 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: 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.