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Decoupling Computation and Data Scheduling in Distributed Data-Intensive Applications
 

Summary: Decoupling Computation and Data Scheduling
in Distributed Data-Intensive Applications
Kavitha Ranganathan*
Ian Foster*#
*
Department of Computer Science, University of Chicago, Chicago, IL 60637, USA
#
Math and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA
{krangana,foster}@cs.uchicago.edu
Abstract
In high energy physics, bioinformatics, and other
disciplines, we encounter applications involving
numerous, loosely coupled jobs that both access and
generate large data sets. So-called Data Grids seek to
harness geographically distributed resources for such
large-scale data-intensive problems. Yet effective
scheduling in such environments is challenging, due to
a need to address a variety of metrics and constraints
(e.g., resource utilization, response time, global and
local allocation policies) while dealing with multiple,

  

Source: Agrawal, Gagan - Department of Computer Science and Engineering, Ohio State University

 

Collections: Computer Technologies and Information Sciences