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Laura Brown

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4 papers
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4

AAAI Conference 2016 Conference Paper

A Survey of Current Practice and Teaching of AI

  • Michael Wollowski
  • Robert Selkowitz
  • Laura Brown
  • Ashok Goel
  • George Luger
  • Jim Marshall
  • Andrew Neel
  • Todd Neller

The field of AI has changed significantly in the past couple of years and will likely continue to do so. Driven by a desire to expose our students to relevant and modern materials, we conducted two surveys, one of AI instructors and one of AI practitioners. The surveys were aimed at gathering information about the current state of the art of introducing AI as well as gathering input from practitioners in the field on techniques used in practice. In this paper, we present and briefly discuss the responses to those two surveys.

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AAAI Conference 2015 Conference Paper

Transfer Learning-Based Co-Run Scheduling for Heterogeneous Datacenters

  • Wei Kuang
  • Laura Brown
  • Zhenlin Wang

Today’s data centers are designed with multi-core CPUs where multiple virtual machines (VMs) can be colocated into one physical machine or distribute multiple computing tasks onto one physical machine. The result is co-tenancy, resource sharing and competition. Modeling and predicting such co-run interference becomes crucial for job scheduling and Quality of Service assurance. Co-locating interference can be characterized into two components, sensitivity and pressure, where sensitivity characterizes how an application’s own performance is affected by a co-run application, and pressure characterizes how much contentiousness an application exerts/brings onto the memory subsystem. Previous studies show that with simple models, sensitivity and pressure can be accurately characterized for a single machine. We extend the models to consider crossarchitecture sensitivity (across different machines).

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