Microsoft Technology Licensing, LLC

Yi Zhu

Clemens Alden Szyperski

Marc Todd Friedman

Alekh Jindal

Hiren Shantilal Patel

Rathijit Sen

Shi Qiao

Anish Yatin Pimpley

Anubha Srivastava

Vishal Lalchand Rohra

Shuo Li

Solutions for optimizing job runtimes via prediction-based token allocation includes receiving training data comprising historical run data, the historical run data comprising job characteristics, runtime results, and a token count for each of a plurality of prior jobs, and the job characteristics comprising an intermediate representation and job graph data; based at least on the training data, training a token estimator, the token estimator comprising a machine learning (ML) model; receiving job characteristics for a user-submitted job; based at least on the received job characteristics, generating, with the token estimator, token prediction data for the user-submitted job; selecting a token count for the user-submitted job, based at least on the token prediction data; identifying the selected token count to an execution environment; and executing, with the execution environment, the user-submitted job in accordance with the selected token count.

Optimizing job runtimes via prediction-based token allocation

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Boston, MA, United States of America

Yi Zhu