A genetic algorithm based approach for scheduling decomposable data grid applications

  • Kim S
  • Weissman J
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Data grid technology promises geographically distributed scientists
to access and share physically distributed resources such as compute
resource, networks, storage, and most importantly data collections
for large-scale data intensive problems. Because of the massive size
and distributed nature of these datasets, scheduling data grid applications
must consider communication and computation simultaneously to achieve
high performance. In many data grid applications, data can be decomposed
into multiple independent sub datasets and distributed for parallel
execution and analysis. We exploit this property and propose a novel
genetic algorithm based approach that automatically decomposes data
onto communication and computation resources. The proposed GA-based
scheduler takes advantage of the parallelism of decomposable data
grid applications to achieve the desired performance level. We evaluate
the proposed approach comparing with other algorithms. Simulation
results show that the proposed GA-based approach can be a competitive
choice for scheduling large data grid applications in terms of both
scheduling overhead and the relative solution quality as compared
to other algorithms.

Author-supplied keywords

  • Internet;genetic algorithms;grid computing;process

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  • S Kim

  • J B Weissman

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