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 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: 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 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 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 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 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: 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: Embodiments for monitoring performance metrics of a computer network by defining key performance indicators for the computer network, collecting performance data for the key performance indicators, and providing one or more anomaly detection policies to define anomalous performance of the computer network using defined threshold values. A policy is applied to the collected performance data to detect abnormal performance and a notification is sent to a user upon each instance of the detected abnormal performance. An alert tracking component is used to reduce alert generation by compiling and displaying past alert response data to allow a user to directly modify threshold values for alert generation and define temporary parameters to increase alert thresholds.

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 executing selected remediation scripts to remedy issues in a data storage system through an API/script recorder component or process that presents a DVR (digital video recorder)-like interface and capability to capture scripts and API calls as they are used when navigating a graphical user interface (GUI). Internal components of this function perform the recording and macro creation to allow constant playback of these recorded and selected scripts. By easily generating and managing scripts macros, system administrators can easily re-execute customer workflows to apply known fixes to current issues in the data storage system.

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: Embodiments for monitoring performance metrics of a computer network by defining key performance indicators for the computer network, collecting performance data for the key performance indicators, and providing one or more anomaly detection policies to define anomalous performance of the computer network using defined threshold values. A policy is applied to the collected performance data to detect abnormal performance and a notification is sent to a user upon each instance of the detected abnormal performance. An alert tracking component is used to reduce alert generation by compiling and displaying past alert response data to allow a user to directly modify threshold values for alert generation and define temporary parameters to increase alert thresholds.

Abstract: Embodiments for a system and method of monitoring performance metrics of a computer network, by defining key performance indicators for the performance metrics of the computer network, collecting performance data for each of the key performance indicators, and providing one or more anomaly detection policies to define anomalous performance of the computer network using defined threshold values. An anomaly detection policy is applied to the collected performance data to detect abnormal performance and a notification is sent to a user upon each instance of the detected abnormal performance. The anomaly detection policy includes an algorithm applied to the assets, and one or more notification rules that dictate how the notification message is sent to the user.