Abstract: Systems and methods disclosed herein improve on current technology for block-level data replication to cloud computing environments. A new system architecture deploys one or more replication tail proxies in a cloud computing environment, locally (at the cloud) tracks replicated data and determines which replicated data meet criteria for reconstructing a desired point-in-time in the cloud, and persists data blocks received at the replication tail proxy until they are processed as recovery points. The disclosed approach presents resiliency and performance advantages. First, the resiliency of block-level data replication to cloud is improved by deploying the replication tail proxy in the destination cloud. Second, a Recovery Time Objective (RTO) is reduced by enabling faster cloud deployment of virtual machines for disaster recovery, failover, and/or test purposes based on the replicated data.

Abstract: Systems and methods disclosed herein improve on current technology for block-level data replication to cloud computing environments. A new system architecture deploys one or more replication tail proxies in a cloud computing environment, locally (at the cloud) tracks replicated data and determines which replicated data meet criteria for reconstructing a desired point-in-time in the cloud, and persists data blocks received at the replication tail proxy until they are processed as recovery points. The disclosed approach presents resiliency and performance advantages. First, the resiliency of block-level data replication to cloud is improved by deploying the replication tail proxy in the destination cloud. Second, a Recovery Time Objective (RTO) is reduced by enabling faster cloud deployment of virtual machines for disaster recovery, failover, and/or test purposes based on the replicated data.

Abstract: Systems and methods disclosed herein improve on current technology for block-level data replication to cloud computing environments. A new system architecture deploys one or more replication tail proxies in a cloud computing environment, locally (at the cloud) tracks replicated data and determines which replicated data meet criteria for reconstructing a desired point-in-time in the cloud, and persists data blocks received at the replication tail proxy until they are processed as recovery points. The disclosed approach presents resiliency and performance advantages. First, the resiliency of block-level data replication to cloud is improved by deploying the replication tail proxy in the destination cloud. Second, a Recovery Time Objective (RTO) is reduced by enabling faster cloud deployment of virtual machines for disaster recovery, failover, and/or test purposes based on the replicated data.

Abstract: To overcome problems encountered with instability and/or risk of data corruption arising from using software snapshots, the present approach relies instead on data replication and sparse files to provide personal or private versions of a data storage volume (“private writable snapshots”). The disclosed technological solution avoids the use of software snapshot technologies altogether. Instead, a family of inter-related and cascading sparse files are maintained as data sources of replicated data volumes. In the event that the server which controls these sparse files crashes, the sparse files themselves are persistent and remain uncorrupted in a data storage platform, such as a storage array. An illustrative Internet Small Computer Systems Interface server (“ISCSI server”) provides user computing devices with private writable snapshots of a desired volume of data using the illustrative sparse files.