A Genetic Programming Approach to Design Resource Allocation Policies for Heterogeneous Workflows in the Cloud
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- @InProceedings{Estrada:2015:ieeeICPADS,
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author = "Trilce Estrada and Michael Wyatt and Michela Taufer",
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booktitle = "21st IEEE International Conference on Parallel and
Distributed Systems (ICPADS)",
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title = "A Genetic Programming Approach to Design Resource
Allocation Policies for Heterogeneous Workflows in the
Cloud",
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year = "2015",
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pages = "372--379",
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abstract = "When dealing with very large applications in the
cloud, higher costs do not always result in better
turnaround times, particularly for complex work-flows
with multiple task dependencies. Thus, resource
allocation policies are needed that can determine when
using expensive but faster resources is best and when
it is not. Manually developing such heuristics is time
consuming and limited by the subjective beliefs of the
developer. To overcome such impediments, we present an
automatic method that designs and evaluates a large set
of policies using a genetic programming approach. Our
method finds a robust set of policies that adapt to
changes in workload while using resources efficiently.
Our results show that our genetic programming designed
policies perform better than greedy and other human
designed policies do.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ICPADS.2015.54",
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month = dec,
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notes = "Also known as \cite{7384317}",
- }
Genetic Programming entries for
Trilce Estrada
Michael Wyatt
Michela Taufer
Citations