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US 12,754,880 B2Granted
Dynamic configuration of backup retention using machine learning
AssigneeSap SE
InventorsSreekanth Kozhisseri Pattath, Sreeram V, Rajwin Victor Jesudason, Sanjeev Peddi Reddi Gari, Ayush Jain, Meenakshi Meenakshi, Shivkumar Chakkenchath
Life of this patent
Estimating the term…
Abstract
In an example embodiment, machine learning is leveraged to predict volume importance. A machine learning model is trained to classify volumes based on “importance”, and more specifically how important it is for a backup of a volume to be retained for a longer period. Different volumes may be given different volume importance classifications, and thus may be assigned different backup retention periods. This ensures that the backup strategy remains responsive to changes in data dynamics.
Claims17
A system comprising:
- at least one hardware processor;
- and
- a non-transitory computer-readable medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising:
- identifying a first plurality of volumes in a computer system;
- for each volume in the first plurality of volumes:
- determining file types of files stored on a corresponding volume, the files comprising a plurality of different file types;
- determining an environment in which the corresponding volume operates;
- and
- accessing a label indicating a level of importance of the corresponding volume;
- passing the determined file types, environments, and levels of importance to a machine learning algorithm to train an importance prediction model to predict importance of volumes;
- identifying a first volume not contained in the first plurality of volumes;
- determining file types of files stored on the first volume;
- determining an environment in which the first volume operates;
- generating a feature matrix, each row of the feature matrix corresponding to a different volume and environment combination and the feature matrix having a plurality of file type columns, each file type column corresponding to a different potential file type, and wherein each cell in a file type column in a body of the feature matrix contains an indication of whether a corresponding volume contained a file with a corresponding file type;
- passing the determined file types of files stored on the first volume and the environment in which the first volume operates to the importance prediction model to predict an importance level for the first volume;
- and
- based on the importance level for the first volume predicted by the importance prediction model, setting a retention period for a backup of the first volume, the backup comprising all files stored on the first volume regardless of file type, such that the backup is not deleted until after the retention period has lapsed.
14 more elements
A method comprising:
- identifying a first plurality of volumes in a computer system;
- for each volume in the first plurality of volumes:
- determining file types of files stored on a corresponding volume, the files comprising a plurality of different file types;
- determining an environment in which the corresponding volume operates;
- and
- accessing a label indicating a level of importance of the corresponding volume;
- passing the determined file types, environments, and levels of importance to a machine learning algorithm to train an importance prediction model to predict importance of volumes;
- identifying a first volume not contained in the first plurality of volumes;
- determining file types of files stored on the first volume;
- determining an environment in which the first volume operates;
- generating a feature matrix, each row of the feature matrix corresponding to a different volume and environment combination and the feature matrix having a plurality of file type columns, each file type column corresponding to a different potential file type, and wherein each cell in a file type column in a body of the feature matrix contains an indication of whether a corresponding volume contained a file with a corresponding file type;
- passing the determined file types of files stored on the first volume and the environment in which the first volume operates to the importance prediction model to predict an importance level for the first volume;
- and
- based on the importance level for the first volume predicted by the importance prediction model, setting a retention period for a backup of the first volume, the backup comprising all files stored on the first volume regardless of file type, such that the backup is not deleted until after the retention period has lapsed.
11 more elements
A non-transitory machine-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:
- identifying a first plurality of volumes in a computer system;
- for each volume in the first plurality of volumes:
- determining file types of files stored on a corresponding volume, the files comprising a plurality of different file types;
- determining an environment in which the corresponding volume operates;
- and
- accessing a label indicating a level of importance of the corresponding volume;
- passing the determined file types, environments, and levels of importance to a machine learning algorithm to train an importance prediction model to predict importance of volumes;
- identifying a first volume not contained in the first plurality of volumes;
- determining file types of files stored on the first volume;
- determining an environment in which the first volume operates;
- generating a feature matrix, each row of the feature matrix corresponding to a different volume and environment combination and the feature matrix having a plurality of file type columns, each file type column corresponding to a different potential file type, and wherein each cell in a file type column in a body of the feature matrix contains an indication of whether a corresponding volume contained a file with a corresponding file type;
- passing the determined file types of files stored on the first volume and the environment in which the first volume operates to the importance prediction model to predict an importance level for the first volume;
- and
- based on the importance level for the first volume predicted by the importance prediction model, setting a retention period for a backup of the first volume, the backup comprising all files stored on the first volume regardless of file type, such that the backup is not deleted until after the retention period has lapsed.
11 more elements
Dependencies are inferred from the claim text.