Microsoft Technology Licensing, LLC

Bikas Saha

Albert Gordon Greenberg

Yi Lu

Srikanth Kandula

Ganesh Ananthanarayanan

Ronnie Chaiken

Edward David Harris

James David Ryseff

The described implementations relate to distributed computing. One implementation provides a system that can include an outlier detection component that is configured to identify an outlier task from a plurality of tasks based on runtimes of the plurality of tasks. The system can also include a cause evaluation component that is configured to evaluate a cause of the outlier task. For example, the cause of the outlier task can be an amount of data processed by the outlier task, contention for resources used to execute the outlier task, or a communication link with congested bandwidth that is used by the outlier task to input or output data. The system can also include one or more processing devices configured to execute one or more of the components.

System and method for proactive task scheduling of a copy of outlier task in a computing environment

Systems, methods, and computer media for scheduling vertices in a distributed data processing network and allocating computing resources on a processing node in a distributed data processing network are provided. Vertices, subparts of a data job including both data and computer code that runs on the data, are assigned by a job manager to a distributed cluster of process nodes for processing. The process nodes run the vertices and transmit computing resource usage information, including memory and processing core usage, back to the job manager. The job manager uses this information to estimate computing resource usage information for other vertices in the data job that are either still running or waiting to be run. Using the estimated computing resource usage information, each process node can run multiple vertices concurrently.

Concurrent data processing in a distributed system

Growing community of inventors

Bellevue, WA, United States of America

Bikas Saha