Abstract: Illustrative methods and systems for migrating a large amount of data from a source site to one or more cloud destination sites with minimal interruptions to the source site during the migration process. Configured media agents initiate specialized backup jobs on source data to create backup copies of a specified size that minimally impacts the source site. Those backup copies are received at storage management site and stored in cache. For each received data blob, the illustrative system restores the backup copies to the destination site in the format of the original application data.

Abstract: A cloud-based data protection service is disclosed. In an embodiment, the data protection service may support backup of data sets from one or more sites associated with one or more organizations. In an embodiment, deduplication of backup data across multiple sites of an organization and/or multiple sites associated with different organizations may be supported. In an embodiment, backup data may be post-processed in the cloud to insert fingerprints corresponding to data blocks that did not change since a previous backup was performed, to scan the backup for security threats such as viruses, other malware, personally identifiable information, etc. In an embodiment, restore may be supported from the cloud, where restore blocks may be larger than backup data blocks. In another embodiment, restore may be based on blocks that have changed since the most recent backup (or a user-selected backup).

Abstract: A secure data storage management “recovery system” operates in a cloud computing environment that is apart from a source system and source data being protected. A “recovery manager” in the cloud computing environment is responsible for restoring secondary copies that were generated by the source system. The recovery manager gains knowledge (metadata) about the secondary copies by restoring, for its own use, a management database of, and backed up by, the source system. The recovery manager initiates out-of-place restores of the secondary copies to “recovery clients” in the recovery cloud. The recovery system restores, to the recovery cloud, secondary copies from any cloud platform and/or from non-cloud data centers, including secondary copies stored locally, off-cloud by the source system. The recovery manager comprises new features that enforce its isolation from the source system and they act to protect the integrity of the secondary copies generated by the source system.

Abstract: The present application discloses methods and systems that leverage an information management framework equipped with air-gapped storage and integrated with a cleanroom cloud environment. Using “Recovery Group” data (asset list, recovery target site, and various configuration settings), applications and their associated data can be securely recovered, tested, and verified in the cleanroom environment before reinstating them into operational use. These assets, which may include databases, virtual machines, and file servers, are restored according to predefined priorities, specific recovery points, and under stringent security measures. The recovery group data facilitates the formulation of a detailed cyber recovery plan, which orchestrates the recovery operation by using restore job details, asset configurations, security protocols for the cleanroom environment, and network settings.

Abstract: A data storage management system comprises features for initiating failover orchestration jobs that invoke recovery resources on demand in a cloud computing environment. Backed up data that is stored persistently in the cloud computing environment may be rapidly restored within the cloud computing environment for use in disaster recovery and/or in test and verification scenarios. This approach may be contrasted to systems where a failover system is “always on” at the failover destination, such as having failover resources always up and running in the cloud computing environment. Such resources typically include a failover virtual machine (VM), a virtual machine datastore for the restored data, and one or more computing resources for restoring an auxiliary copy to the VM's datastore. The cloud-based failover resources are deactivated or taken down once the failover event ends.

Abstract: A data storage management system is enhanced to accommodate, and moreover to optimize, the storing and retention of deduplicated secondary copies at write-once read-many (WORM) enabled storage platforms. Enhancements include without limitation: user interface (UI) options to enable WORM functionality for secondary storage, whether used for deduplicated or non-deduplicated secondary copies; enhancements to secondary copy (e.g., deduplication copy, backup) operations; and pruning changes. The storage manager is generally responsible for managing the creation, tracking, and deletion of secondary copies, with and without deduplication. Media agents that store secondary copies to and prune them from the WORM-enabled storage platforms also are enhanced for communicating and interoperating with both bucket-level and object-level WORM-enabled storage platforms to implement the features disclosed herein.

Abstract: Because Kubernetes clusters can be ephemeral, backing up in-cluster data to storage outside the cluster is important. Prior art solutions used the cluster's API server, which facilitates communications with the cluster control plane, to transfer backup data through the API server. However, the API server as a data transfer node has resiliency weaknesses and can slow down backup job performance. The present solution provides a more streamlined and scalable approach, which circumvents the API server and additionally includes more robust error checking, log capture, and realtime job monitoring to provide improved data protection resilience. The disclosed approach employs a “sponsor” data agent outside the cluster and temporarily deploys a specialized backup resource within the cluster during a backup job, such as a lightweight Kubernetes File Client and/or an enhanced File System Data Agent, both of which present substantial performance and resiliency advantages over the API server.

Abstract: Some service providers (e.g., Microsoft's Office 365 services) throttle application or service requests an organization, a user, or device can request from a specific application or a suite of applications/services. This throttling can significantly impact performance for, not only the requesting user/device, but also for other tenants associated with that organization. To limit this imposed external throttling, an intelligent internal adaptive throttling or rate limiting may be implemented. In an embodiment, the system monitors throttling imposed by the service provider. Using this information, the system determines an optimal rate of calls that can made for that service to maximize throughput and performance. The system continuously monitors the throttling and in response dynamically modifies the internal target rate.

Abstract: Systems and processes disclosed herein perform an automatic remediation process. The automatic remediation process may be a log-based remediation process. Systems disclosed herein may obtain log files from an information management system and determine the occurrence of errors at the information management system based on error codes included in the logs. Further, the systems may access a knowledgebase to determine whether solutions for the errors have been previously generated. The solutions may include patches or hotfixes that can be applied to the information management system without removing user-access or stopping execution of the information management system. The systems may automatically update the information management system to address the errors. Alternatively, or in addition, the systems may alert a user, such as an administrator, of the existence of a solution to the error, and whether the solution may be applied without interrupting service or access to the information management system.

Abstract: A storage manager for an information management system determines whether one or more predetermined conditions have been met for transferring metadata of previously performed backup jobs stored in a first management database. A backup job may correspond to a backup operation of a primary storage device of a first client computing device. In response to a determination that one or more of the predetermined conditions have been met, the storage manager may transfer metadata for a second plurality of backup jobs to a second management database of a recovery manager. The recovery manager may receive a request to restore data to the primary storage device of the first client computing device based on the metadata of the second plurality of backup jobs. A media agent managed by the recovery manager may then restore the requested data to the primary storage device of the first client computing device.

Abstract: Techniques disclosed herein are well suited to restoring deduplicated backup data from cloud-based storage and from multi-node replicated files systems, and they also improve performance in more traditional data storage technologies. Pre-restore steps include analysis of deduplication indexes to identify data segments that are stored consecutively on storage media. Reading data in aggregate runs of consecutively stored data segments reduces interactions with storage media that hosts the deduplicated data and speeds up retrieval. Parallel reads from multiple storage devices in multi-node replicated file systems also speed up retrieval. An illustrative enhanced media agent pre-fetches data (stored in deduplicated form) in anticipation of read requests that are expected in the restore operation.

Abstract: An improved information management system is described herein in which the information management system can evaluate the deduplication performance of secondary copy operations and dynamically adjust the manner in which secondary copy data is created to minimize the negative effects of performing deduplication. Furthermore, the improved information management system can improve deduplication performance by applying different storage policies to different types of applications running on a client computing device. Moreover, the improved information management system can automatically detect the region of a client computing device and apply an appropriate information management policy to the client computing device to avoid inconsistencies or other errors resulting from administrator control.

Abstract: Techniques are disclosed relating to providing protection groups and rapid determination of expired objects and associated storage resources in a cloud-based backup storage context. In various embodiments, the disclosed techniques include generating, by a computing system, incremental backup data for a backup based on protection group information, including a set of cloud-based key-value data store buckets, filter information, and policy information. Disclosed techniques also include rapid determination of expired objects and associated storage resources. Disclosed techniques may advantageously provide a centralized view across buckets, accounts, and regions and allow users to target specific subsets of their data stores for backup, which may improve performance and reduce backup storage costs, relative to traditional techniques.

Abstract: Because Kubernetes clusters can be ephemeral, backing up in-cluster data to storage outside the cluster is important. Prior art solutions used the cluster's API server, which facilitates communications with the cluster control plane, to transfer backup data through the API server. However, the API server as a data transfer node has resiliency weaknesses and can slow down backup job performance. The present solution provides a more streamlined and scalable approach, which circumvents the API server and additionally includes more robust error checking, log capture, and realtime job monitoring to provide improved data protection resilience. The disclosed approach employs a “sponsor” data agent outside the cluster and temporarily deploys a specialized backup resource within the cluster during a backup job, such as a lightweight Kubernetes File Client and/or an enhanced File System Data Agent, both of which present substantial performance and resiliency advantages over the API server.

Abstract: Protecting configuration data in a clustered container system may include protecting an ETCD data store in a Kubernetes cluster. A data storage management system addresses the unique needs of protecting an ETCD data store of a target Kubernetes cluster, as well as protecting non-ETCD data payloads. The illustrative data storage management system defines ETCD as a unique kind of workload. ETCD protection is integrated within the data storage management system, which automatically creates data structures and resources within the system for, and provides special-purpose features to protect, ETCD contents and associated security certificates. One of the special-purpose features deploys a temporary data transfer agent within the target Kubernetes cluster to safeguard an ETCD snapshot and transmit its contents, along with the security certificates, to a backup infrastructure that operates outside of the target Kubernetes cluster.

Abstract: Because Kubernetes clusters can be ephemeral, backing up in-cluster data to storage outside the cluster is important. Prior art solutions used the cluster's API server, which facilitates communications with the cluster control plane, to transfer backup data through the API server. However, the API server as a data transfer node has resiliency weaknesses and can slow down backup job performance. The present solution provides a more streamlined and scalable approach, which circumvents the API server and additionally includes more robust error checking, log capture, and realtime job monitoring to provide improved data protection resilience. The disclosed approach employs a “sponsor” data agent outside the cluster and temporarily deploys a specialized backup resource within the cluster during a backup job, such as an enhanced File System Data Agent and/or a lightweight Kubernetes File Client, both of which present substantial performance and resiliency advantages over the API server.

Abstract: A cloud-based data protection service is disclosed. In an embodiment, the data protection service may support backup of data sets from one or more sites associated with one or more organizations. In an embodiment, deduplication of backup data across multiple sites of an organization and/or multiple sites associated with different organizations may be supported. In an embodiment, backup data may be post-processed in the cloud to insert fingerprints corresponding to data blocks that did not change since a previous backup was performed, to scan the backup for security threats such as viruses, other malware, personally identifiable information, etc. In an embodiment, restore may be supported from the cloud, where restore blocks may be larger than backup data blocks. In another embodiment, restore may be based on blocks that have changed since the most recent backup (or a user-selected backup).

Abstract: This application relates to ransomware detection and data pruning management. Ransomware typically involves an I/O heavy process of encrypting data files and/or deleting or renaming the original files. Thus, ransomware attacks may be detected by analyzing the I/O activity in a given file system. In some embodiments, a software module running on a client machine manages copying, archiving, migrating, and/or replicating of primary data and restoring and/or pruning secondary data (e.g., backup copies of the primary data). When a potential ransomware attack is detected, the software module is immediately stopped so that the software module does not prune any data that may need to be restored. Upon receiving user input that indicates that the client machine is not under a ransomware attack, the software module is allowed to resume its operations, including pruning of the secondary data.

Abstract: Snapshot-based disaster recovery (DR) orchestration systems and methods for virtual machine (VM) failover and failback do not require that VMs or their corresponding datastores be actively operating at the DR site before a DR orchestration job is initiated, i.e., before failover. An illustrative data storage management system deploys proprietary components at source data center(s) and at DR site(s). The proprietary components (e.g., storage manager, data agents, media agents, backup nodes, etc.) interoperate with each other and with the source and DR components to ensure that VMs will successfully failover and/or failback. DR orchestration jobs are suitable for testing VM failover scenarios (“clone testing”), for conducting planned VM failovers, and for unplanned VM failovers. DR orchestration jobs also handle failback and integration of DR-generated data into the failback site, including restoring VMs that never failed over to fully re-populate the source/failback site.

Abstract: An illustrative file indexing approach enhances what was previously possible with hypervisor-free live browsing of virtual machine (VM) block-level backup copies. Capabilities are described for indexing files discovered in VM block-level backup copies, including file content. The illustrative file indexing functionality activates a live-browse session to discover files present within VM block-level backup copies and indexes file names and directory structures as created by an original source VM, resulting in an illustrative file index. The illustrative file indexing functionality optionally indexes file contents within VM block-level backup copies, resulting in an illustrative content index. The file index and content index are retained in persistent data structure(s) stored apart from the VM block-level backup copies. The indexes are searchable without mounting or live-browsing the VM block-level backup copies. In some embodiments the file index and the content index are consolidated.