Multi-agent distributed adaptive resource allocation (MADARA)

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Edmondson:2010:IJCNDS,
  author =       "James Edmondson and Douglas Schmidt",
  title =        "Multi-agent distributed adaptive resource allocation
                 ({MADARA})",
  journal =      "International Journal of Communication Networks and
                 Distributed Systems",
  year =         "2010",
  volume =       "5",
  number =       "3",
  pages =        "229--245",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1754-3924",
  URL =          "http://www.inderscience.com/link.php?id=34946",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.5912",
  bibsource =    "OAI-PMH server at www.inderscience.com",
  language =     "eng",
  rights =       "Inderscience Copyright",
  abstract =     "The component placement problem involves mapping a
                 component to a particular location and maximising
                 component utility in grid and cloud systems. It is also
                 an NP hard resource allocation and deployment problem,
                 so many common grid and cloud computing libraries, such
                 as MPICH and Hadoop, do not address this problem, even
                 though large performance gains can occur by optimising
                 communications between nodes. This paper provides four
                 contributions to research on the component placement
                 problem for grid and cloud computing environments.
                 First, we present the multi-agent distributed adaptive
                 resource allocation (MADARA) toolkit, which is designed
                 to address grid and cloud allocation and deployment
                 needs. Second, we present a heuristic called the
                 comparison-based iteration by degree (CID) heuristic,
                 which we use to approximate optimal deployments in
                 MADARA. Third, we analyse the performance of applying
                 the CID heuristic to approximate common grid and cloud
                 operations, such as broadcast, gather and reduce.
                 Fourth, we evaluate the results of applying genetic
                 programming mutation to improve our CID heuristic.",
}

Genetic Programming entries for James Edmondson Douglas C Schmidt

Citations