Google Inc.

Wesley Kendall

Kenneth Jerome Goldman

Jelena Pjesivac-Grbovic

Matthew Faulkner

Aspects of the disclosure relate to learning the most useful backup tasks when processing a job in a distributed computing application. For example, training data may be generated by running a job and running backup tasks for some of the tasks for that job. List of features may be generated for each backup task at different times. A representative list of features may be labeled according to the backup tasks usefulness to the job. The lists and usefulness labels may be used to generate a model of usefulness. The model may then be used to evaluate the usefulness of the tasks for the next job. This information may then be used to schedule backup tasks for execution. In addition, lists of features for these backup tasks may be generated and then used to update the model in a continuous learning loop.

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Powell, TN, United States of America

Wesley Kendall