Machine learning for emergent middleware

Bennaceur, Amel; Issarny, Valérie; Sykes, Daniel; Howar, Falk; Isberner, Malte; Steffen, Bernhard; Johansson, Richard and Moschitti, Alessandro (2013). Machine learning for emergent middleware. In: Trustworthy Eternal Systems via Evolving Software, Data and Knowledge, Springer, pp. 16–29.

DOI: https://doi.org/10.1007/978-3-642-45260-4_2

Abstract

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.

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