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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Machine Learning for Emergent Middleware
verfasst von : Amel Bennaceur, Valérie Issarny, Daniel Sykes, Falk Howar, Malte Isberner, Bernhard Steffen, Richard Johansson, Alessandro Moschitti
Erschienen in: Trustworthy Eternal Systems via Evolving Software, Data and Knowledge
Verlag: Springer Berlin Heidelberg
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Highly dynamic and heterogeneous distributed systems are challenging today’s middleware technologies. Existing middleware paradigms are unable to deliver on their most central promise, which is offering interoperability. In this paper, we argue for the need to dynamically synthesise distributed system infrastructures according to the current operating environment, thereby generating “Emergent Middleware” to mediate interactions among heterogeneous networked systems that interact in an
ad hoc
way. The paper outlines the overall architecture of Enablers underlying Emergent Middleware, and in particular focuses on the key role of learning in supporting such a process, spanning statistical learning to infer the semantics of networked system functions and automata learning to extract the related behaviours of networked systems.