Abstract: Embodiments help timely remediation of issues in a data protection system by automatically evaluating scripts configured to address the issues. The system has a bounded list of possible attributes that are deemed important by system administrators. Each attribute is assigned a System Impact Score (SIS) along a defined scale. A self-healing processing component monitors the state of each attribute over time. The scripts are evaluated through repeated execution and use of the attribute monitoring to determine which attributes are affected by a script. Weights are assigned to each attribute affected by a script to aid in the selection of scripts most likely to remediate an actionable issue. Regularly performing script evaluation and attribute weighting allows for updating of scripts with an accurate list of attributes to overcome problems associated with manual updates.

Abstract: A three-phase full quorum commit method enabling backing up of network devices that do not offer direct hooks in order to have application consistent protection. Devices are verified to be ready to perform a backup, and a condition of reaching and maintaining a full quorum of devices within a maximum time period is required before the system can be backed up. The three phase backup process reduces the chance of changes to network devices from corrupting consistency among the saved states of the different and disparate network devices. Multiple devices of different makes and models participate together as a unified backup as a network partition and all devices are verified as being in a ready state. The device configuration data is moved from device memory to local disk, and can then be tiered to secondary storage.

Abstract: Selecting user access policies for a new system, by collecting user, access policy, and resource metadata for a plurality of other users storing data dictated by one or more access restriction policies. The collected metadata is anonymized with respect to personal identifying information, and is stored in an anonymized analytics database. The system receives specific user, access policy and resource metadata for the new system from a specific user, and matches the received specific user metadata to the collected metadata to identify an optimum access policy of the one or more access policies based on the assets and access restriction requirements of the new system. The new system is then configured with the identified optimum access policy as an initial configuration.

Abstract: Embodiments automate the timely remediation of issues by matching failed operations to a script that is configured to fix the problem or problems without causing additional side effects, thus minimizing unavailability of services and loss of data. A method detects a backup operation alert message sent from a data manager or storage system that encodes one or more attributes affected by the backup operation, wherein the attributes are weighted to reflect a percentage importance of the a corresponding script to fix the problem. A script library is scanned to identify scripts that match the alert message. A component generates a score for each script, where the score factors the relevance of the script to the backup operation based on the weighted attributes, and executes the script with the highest score, as the script most likely to fix the issue causing the alert message.

Abstract: Embodiments automate the timely remediation of issues by matching failed operations to a script that is configured to fix the problem or problems without causing additional side effects, thus minimizing unavailability of services/data and loss of data. A method detects a backup operation alert message sent from a data manager or storage system that encodes one or more attributes affected by the backup operation. A script library is scanned to identify scripts that match the alert message. A component generates a score for each script, where the score factors the relevance of the script to the backup operation, and executes the script with the highest score, as the script most likely to remediate the issue causing the alert message.

Abstract: Embodiments automate the timely remediation of issues by matching failed operations to a script or set of scripts that is configured to fix the problem. A method detects a backup operation alert message sent from a data manager or storage system that encodes one or more attributes affected by the backup operation, wherein the attributes are weighted to reflect a percentage importance of the a corresponding script to fix the problem. A script library is scanned to identify scripts that match the alert message. A component generates a score for each script, where the score factors the relevance of the script to the backup operation based on the weighted attributes. Scores for each single script each possible combination of multiple scripts are computed and compared, and the script or script combination with the highest score is executed by the self-healing process.

Abstract: Embodiments are described for a system that automatically determines the ideal backup target for the backup agent to send its backup data. The system will automatically create and destroy temporary backup targets in order to handle the current backup traffic and provide the optimal throughput based on speed of the backup agent and reliability of the storage target. The backup agent is able to send incremental backups to any temporary backup target, which are later consolidated on the storage target after the agent has disconnected from the system. The final storage target may be an original primary storage target for the data asset or one of the temporary storage targets.

Abstract: Embodiments are described for a system that automatically determines the ideal backup target to send backup data. Temporary backup targets are automatically created to handle backups for mobile data assets. A backup agent sends incremental backups to any temporary backup target (TBT), which are later consolidated with primary backup target (PBT) data. To facilitate data backup to a TBT and restore operations from data located on a PBT or TBT, a Backup Location Catalog (BLC) is created for each asset to reside on the asset. A Change Record Catalog (CRC) is created for each TBT backup per asset and each CRC resides on the asset until the associated data is consolidated back to the PBT. Assets thus have access to all backups, including the data on the TBT(s) before that data is consolidated back to the PBT.

Abstract: Embodiments are described for a system that automatically determines the ideal backup target for the backup agent to send its backup data. The system will automatically create and destroy temporary backup targets in order to handle the current backup traffic and provide the optimal throughput based on speed of the backup agent and reliability of the storage target. The backup agent is able to send incremental backups to any temporary backup target, which are later consolidated on the storage target after the agent has disconnected from the system. The final storage target may be an original primary storage target for the data asset or one of the temporary storage targets.

Abstract: Embodiments are described for preventing undesired data deletion on protection storage by using delete restrictions. A delete restriction component prevents data from being staged for permanent erasure, such as by preventing files from being moved to a trash folder. A well-known process (WKP), such as a backup or migration operation that is known to request data expiration, is used to validate data deletions. The WKP identifies data that is no longer to be retained. The WKP uses a well-known process expiration list (WEL) that holds hashes of the data. A bucket confirmation count indicates candidates for data deletion. Separately, Garbage Collection maintains another list of hashes that are deletion candidates. A separate process finds common hashes between the WKP and GC hashes, and then removes the data pointed to by the common hashes.

Abstract: A system that automatically determines ideal temporary backup targets (TBTs) to store and restore backup data. For multiple TBTs, backup data is saved with location information to restore incremental backup data from these multiple targets. Hashes of each file are stored in a Merkle tree to enable data restore workflows and minimizing an amount of storage required to hold backup data. The lowest tree level comprises hashes of the backup data chunks, and higher levels contain hashes of the next lower levels. A backup target location value is added to non-data chunk hash records in the higher levels to identify where any piece of backup data is located so that restore operations can occur when the backup data is spread across multiple backup targets.

Abstract: Embodiments for a data protection method of grouping assets for protection policy assignment based on asset metadata by defining a set of metrics characterizing each asset in the system and comparing each metric of an asset with corresponding metrics of other asset groups each containing one or more other assets. A unique protection policy is assigned to each group for application to each asset within a respective group. An overall affinity percentage of the metrics of asset with the corresponding metrics of each group is determined, and the asset is automatically grouped into the group based with the highest overall affinity percentage. The user is prompted to confirm the automatic grouping or to select a different group for assigning to the asset.

Abstract: Embodiments for a system and method of selecting data protection policies for a new system, by collecting user, policy, and asset metadata for a plurality of other users storing data dictated by one or more protection policies. The collected metadata is anonymized with respect to personal identifying information, and is stored in an anonymized analytics database. The system receives specific user, policy and asset metadata for the new system from a specific user, and matches the received specific user metadata to the collected metadata to identify an optimum protection policy of the one or more protection policies based on the assets and protection requirements of the new system. The new system is then configured with the identified optimum protection policy as an initial configuration.

Abstract: Embodiments for migrating hash values for backup data blocks in a network of data protection targets (DPTs) and a common data protection target implementing a Gold image library management system in which backups of Gold images used as templates for physical machines and virtual machines are stored on the CDPT. The CDPT and each DPT stores backup data split into chunks that are uniquely identified by a respective hash of its contents, and maintains data structures comprising the hash, chunk size, chunk data, and a list of DPT and CDPT identifiers. The hashes are partitioned into a set of buckets in the CDPT. A Bloom filter is generated for each bucket of hashes, and stored in each DPT so that each DPT stores Bloom filters for all CDPTs in the network. Each DPT checks its list of hashes against the Bloom filters in each of the DPTs to determine whether to keep or free chunks of data.

Abstract: Embodiments for a system and method of monitoring performance metrics of a computer network, by defining key performance indicators for the user behavior in the computer network, collecting behavior statistics for each user for each of the key performance indicators, and providing one or more anomaly detection policies to define anomalous behavior of the users with respect to data assets of the computer network. An anomaly detection policy is applied to the collected behavior statistics to detect anomalous user behavior and an alert notification is sent to administrative or security personnel upon each detected instance of abnormal user behavior.

Abstract: Automatically updating operating system and application programs in a large-scale network using Gold image data. An asset update process receives validation by a user for use of an updated program comprising new Gold image data. The process automatically updates the previous version of the updated version with the updated program to generate new user content data, without requiring further user intervention by using a defined tag linking the new Gold image data with previous Gold image data for the older version of the program. The Gold image data is stored in a central data protection target storage separate from data protection target storage for the user content data.

Abstract: Optimizing multiple backup operations for multiple sources by discovering shared data routes between sources and targets for backup operations performed in a certain time period. Backups using shared routes use a Best Time Algorithm that determines a size of a dataset to be saved an available bandwidth in each transfer window of the time period, and then determines an order of the backup operations based on first ordering the backups based on decreasing dataset size and second ordering the transfer windows in order of decreasing bandwidth, and matching the backups to the transfer windows in accordance with the first ordering and second ordering. The optimum time represents a time to initiate each backup of the multiple backups that results in a shortest data transfer time over the entire time period.

Abstract: Optimizing backups for a data protection system by determining a size of a dataset to be saved in each backup operation and an available bandwidth in each transfer window of transfer windows for data transfers between a source and destination. An order of the backup operations is defined based on first ordering the backups based on decreasing dataset size and second ordering the transfer windows in order of decreasing bandwidth, and matching the backups to the transfer windows in accordance with these orderings. A dynamic window sizing process determines an initial change between the minimum and maximum bandwidth utilization over a period of time and then iteratively split and consolidate the time blocks until optimal utilization over the time period is reached.

Abstract: Embodiments for generating user customized alert notifications for application operations and activities based on monitored performance metrics. Key performance indicators for the application and user behavior are defined, and a monitor process collects behavior statistics of the application for each user with respect to data assets for each of the key performance indicators. Anomaly detection policies are provided to define anomalous behavior of the users with respect to data assets of the computer network. An anomaly detection process detects anomalous user behavior and an alert notification is sent to administrative or security personnel upon each detected instance of abnormal user behavior. The alert notification rules are defined by the user based on operation severity, asset type, operation, and defined metrics to tailor and minimize the number of alerts sent to the user.

Abstract: A lightweight, workload-agnostic application management agent (AMA) automatically upgrading application agents on a virtual machine (VM). The AMA is installed in the VM as part of a Gold image installation. The AMA detects a workload-type of the VM and sends the workload information to an application manager. The AMA continuously monitors the VM for any change in workload type, and any change causes the AMA to install a respective workload-specific agent (WSA) for the new workload in the VM. The AMA accesses the new WSA from the application manager though a push or pull operation. Applications are thus automatically upgraded through VM resident agents. The user needs only to update policies and load updated software to the application manager without manually upgrading the WSA agents themselves.