A Genetic Programming Approach to Design Resource Allocation Policies for Heterogeneous Workflows in the Cloud

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@InProceedings{Estrada:2015:ieeeICPADS,
  author =       "Trilce Estrada and Michael Wyatt and Michela Taufer",
  booktitle =    "21st IEEE International Conference on Parallel and
                 Distributed Systems (ICPADS)",
  title =        "A Genetic Programming Approach to Design Resource
                 Allocation Policies for Heterogeneous Workflows in the
                 Cloud",
  year =         "2015",
  pages =        "372--379",
  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.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICPADS.2015.54",
  month =        dec,
  notes =        "Also known as \cite{7384317}",
}

Genetic Programming entries for Trilce Estrada Michael Wyatt Michela Taufer

Citations