A deadline and budget constrained scheduling algorithm for eScience applications on data grids

Abstract

In this paper, we present an algorithm for scheduling of distributed data intensive Bag-of-Task applications on Data Grids that have costs associated with requesting, transferring and processing datasets. The algorithm takes into account the explosion of choices that result due to a job requiring multiple datasets from multiple data sources. The algorithm builds a resource set for a job that minimizes the cost or time depending on the user's preferences and deadline and budget constraints. We evaluate the algorithm on a Data Grid testbed and present the results. © Springer-Verlag Berlin Heidelberg 2005.