David Vengerov

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Dr. David Vengerov

Adaptive Optimization Project
Sun Microsystems Laboratories


16 Network Circle
umpk16-160
Menlo Park, CA 94025


David Vengerov is a researcher for Sun Microsystems Laboratories, working on the High Productivity Computing Systems (HPCS) program.

His research interests include Autonomic and Utility Computing, Reinforcement Learning and Multi-Agent Systems. His current projects concern application of these techniques to Dynamic Scheduling, Load Balancing, Resource Allocation and Data Migration.

David's publications are available at his personal home page: http://research.sun.com/people/vengerov/publications.html

Education:

* Ph.D. in Management Science and Engineering, Stanford University, 2004.

* M.S. in Engineering Economic Systems and Operations Research, Stanford University, 2000.

* M.S. in Electrical Engineering and Computer Science, MIT, 1998.

* B.S. in Mathematics, MIT, 1997.

Technical Reports

  • Adaptive Optimization of the Sun Java Real-Time System Garbage Collector
    By: Qian Zhu and David Vengerov
    Report Number: TR-2009-186
    Sep 10, 2009
  • Dynamic Adaptation of User Migration Policies in Distributed Virtual Environments
    By: David Vengerov
    Report Number: TR-2009-184
    Jul 30, 2009
  • Modeling, Analysis and Throughput Optimization of a Generational Garbage Collector
    By: David Vengerov
    Report Number: TR-2009-179
    Jan 16, 2009
  • A Gradient-Based Reinforcement Learning Approach to Dynamic Pricing in Partially-Observable Environments
    By: David Vengerov
    Report Number: TR-2007-169
    Sep 24, 2007
  • Adaptive Data-Aware Utility-Based Scheduling in Resource-Constrained Systems
    By: David Vengerov, Lykomidis Mastroleon, Declan Murphy and Nick Bambos
    Report Number: TR-2007-164
    Apr 23, 2007
    See all 8 Technical Reports »

    Latest Sun Patents

  • Dynamic routing of I/O requests in a multi-tier storage environment
    (Oct 20, 2009)
  • Dynamic data migration in a multi-tier storage system
    (May 26, 2009)
  • Method for scheduling jobs using distributed utility-based preemption policies
    (Oct 28, 2008)
  • Method for scheduling jobs using distributed utility-based oversubscription policies
    (Apr 22, 2008)
  • Selecting basis functions to form a regression model for cache performance
    (Mar 18, 2008)
    See all 6 patents »

    Awards

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