Fossa: Learning ECA Rules for Adaptive Distributed Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@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