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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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