Optimizing job runtimes via prediction-based token allocation

US12189629B2 · kind B2 · utility

Assignee

• MICROSOFT TECHNOLOGY LICENSING, LLC · US

Inventors

• Rathijit Sen · Redmond, US
• Alekh Jindal · Sammamish, US
• Anish Pimpley · Los Altos, US
• Shuo Li · Beijing, CN
• Anubha Srivastava · Siesta Shores, US
• Vishal Lalchand Rohra · Boston, US
• Yi Zhu · Darmstadt, DE
• Hiren Shantilal Patel · Bothell, US
• Shi Qiao · Bellevue, US
• Marc T. Friedman · Seattle, US
• Clemens A. Szyperski · Redmond, US

Key dates

• Filing date: Sep 30, 2020
• Grant date: Jan 7, 2025
• Expiry date: Jan 30, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/045
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

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.