An architecture for affective management of systems of adaptive systems

  • Authors:

  • Kevin Feeney;John Keeney;Rob Brennan;Declan O'Sullivan

  • Affiliations:

  • FAME & Knowledge and Data Engineering Group, School of Computer Science & Statistics, Trinity College Dublin, Ireland;FAME & Knowledge and Data Engineering Group, School of Computer Science & Statistics, Trinity College Dublin, Ireland;FAME & Knowledge and Data Engineering Group, School of Computer Science & Statistics, Trinity College Dublin, Ireland;FAME & Knowledge and Data Engineering Group, School of Computer Science & Statistics, Trinity College Dublin, Ireland

  • Venue:
  • MACE'10 Proceedings of the 5th IEEE international conference on Modelling autonomic communication environments
  • Year:
  • 2010

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Abstract

Modern information and communications systems are increasingly composed of highly dynamic aggregations of adaptive or autonomic sub-systems. Such composed systems of adaptive systems frequently exhibit complex interaction patterns that are difficult or impossible to predict with behavioral models. This creates significant challenges for management and governance across such systems as component behavior must adapt in ways that only become apparent after the system is deployed. As a result, the complexity of modern ICT systems, such as telecommunications networks, often exceeds the technological capacity to apply coherent, integrated governance to these systems of adaptive systems. Where components are managed, they are often managed in isolation (or silos) and where intelligent adaptive components are deployed, they adapt in an isolated response to pre-defined variables in an attempt to satisfy local goals. This results in partitioned, incoherent, inflexible, inefficient and expensive management, even for locally adaptive or autonomic systems. This paper presents an approach to apply emotional (affective) modeling, and processing and reasoning techniques to the management of such a system of adaptive systems. We focus on how an emotional management substrate can ease the modeling and mapping of high-level semantic governance directives down to enforceable constraints over the adaptive elements that make up the complex managed system.