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Summary: A Performance Prediction Framework for Grid-Based Data
Mining Applications
Leonid Glimcher Gagan Agrawal
Department of Computer Science and Engineering
Ohio State University, Columbus OH 43210
{glimcher,agrawal}@cse.ohio-state.edu
ABSTRACT
For a grid middleware to perform resource allocation, prediction mod-
els are needed, which can determine how long an application will take
for completion on a particular platform or configuration. In this pa-
per, we take the approach that by focusing on the characteristics of the
class of applications a middleware is suited for, we can develop simple
performance models that can be very accurate in practice.
The particular middleware we consider is FREERIDE-G (FRame-
work for Rapid Implementation of Datamining Engines in Grid), which
supports a high-level interface for developing data mining and scien-
tific data processing applications that involve data stored in remote
repositories. The FREERIDE-G system needs detailed performance
models for performing resource selection, i.e., choosing computing
nodes and replica of the dataset. This paper presents and evaluates
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