Adaptive Techniques for Scheduling Distributed Data Intensive Applications: Experiments on a Production Grid

Efficient job and data management in Data Grids is complicated by various factors like unreliable resources, fluctuating load and multi-administrative challenges. We have proposed an architecture for this scenario, where agents distributed across the Grid, cooperate to schedule both jobs and data with the goal of minimizing execution times, maximizing throughput, and/or minimizing data movement. We have deployed our proposed resource management architecture along with a suite of related job and data scheduling algorithms, on a 27-site wide-area Grid laboratory, Grid3. Here, we report the results of detailed experiments in this environment, using a range of scientific application workloads. We find that intelligent data scheduling is essential for certain scenarios. However, when faced with heterogeneous workloads, adaptive scheduling strategies (that can alter between data-centric and compute-centric approaches) are crucial for achieving good performance. We also discuss insights gained (1) while implementing our architecture on Grid3 and (2) by comparing our experimental results on this Grid laboratory to results obtained via simulations.

By: Kavitha Ranganathan; Ian Foster

Published in: RC23522 in 2005


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