Fossa: Learning ECA Rules for Adaptive Distributed Systems

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@InProceedings{Froemmgen:2015:ieeeICAC,
  author =       "Alexander Froemmgen and Robert Rehner and Max Lehn and 
                 Alejandro Buchmann",
  booktitle =    "2015 IEEE International Conference on Autonomic
                 Computing (ICAC)",
  title =        "Fossa: Learning ECA Rules for Adaptive Distributed
                 Systems",
  year =         "2015",
  pages =        "207--210",
  abstract =     "The development of adaptive distributed systems is
                 complex. Due to a large amount of interdependencies and
                 feedback loops between network nodes and software
                 components, distributed systems respond nonlinearly to
                 changes in the environment and system adaptations.
                 Although Event Condition Action (ECA) rules allow a
                 crisp definition of the adaptive behaviour and a loose
                 coupling with the actual system implementation,
                 defining concrete rules is nontrivial. It requires
                 specifying the events and conditions which trigger
                 adaptations, as well as the selection of appropriate
                 actions leading to suitable new configurations. In this
                 paper, we present the idea of Fossa, an ECA framework
                 for adaptive distributed systems. Following a
                 methodology that separates the adaptation logic from
                 the actual application implementation, we propose
                 learning ECA rules by automatically executing a
                 multitude of tests. Rule sets are generated by
                 algorithms such as genetic programming, and the results
                 are evaluated using a utility function provided by the
                 developer. Fossa therefore provides an automated
                 offline learner that derives suitable ECA rules for a
                 given utility function.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICAC.2015.37",
  month =        jul,
  notes =        "Also known as \cite{7266965}",
}

Genetic Programming entries for Alexander Froemmgen Robert Rehner Max Lehn Alejandro Buchmann

Citations