A Performance Prediction Framework for Grid-Based Data Mining Applications

For a grid middleware to perform resource allocation, prediction models are needed, which can determine how long an application will take for completion on a particular platform or configuration. In this paper, 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 (FRamework for Rapid Implementation of Datamining Engines in Grid), which supports a high-level interface for developing data mining and scientific 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 such a performance model. By exploiting the fact that the processing structure of data mining and scientific data analysis ap...

• Type: Conference
• Year: 2007
• Where: IPPS
• Authors: Leonid Glimcher, Gagan Agrawal
• Keywords: Data Mining, Distributed And Parallel Computing, IPPS 2007, Performance Model, Scientific Data