Adaptive Performance Prediction for Distributed Data-Intensive Applications

Marcio Faerman, Alan Su, Richard Wolski and Francine Berman
May 18, 1999

The computational grid is becoming the platform of choice for large-scale distributed data-intensive applications. Accurately predicting the transfer times of remote data files, a fundamental component of such applications, is critical to achieving application performance. In this paper, we introduce a performance prediction method, ARM (Adaptive Regression Modeling), to determine data transfer times for network-bound distributed data-intensive applications. We demonstrate the effectiveness of the ARM method on two distributed data applications, SARA (Synthetic Aperture Radar Atlas) and SRB (Storage Resource Broker), and discuss how it can be used for application scheduling. Our experiments demonstrate that applying the ARM method to these applications predicted data transfer times in wide-area multi-user grid environments with accuracy of 88% or better.

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