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Adaptive Performance Prediction for Distributed Data-Intensive Applications

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4 Author(s)
Faerman, M. ; University of California San Diego ; Su, A. ; Wolski, R. ; Berman, F.

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, AdRM (Adaptive Regression Modeling), to determine file transfer times for network-bound distributed data-intensive applications. We demonstrate the effectiveness of the AdRM 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 use the Network Weather Service [36, 37], a resource performance measurement and forecasting facility, as a basis for the performance prediction model. Our initial findings indicate that the AdRM method can be effective in accurately predicting data transfer times in wide-area multi-user grid environments.

Published in:

Supercomputing, ACM/IEEE 1999 Conference

Date of Conference:

13-18 Nov. 1999