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2016 | OriginalPaper | Buchkapitel

Reinforcement Learning Techniques for Decentralized Self-adaptive Service Assembly

verfasst von : M. Caporuscio, M. D’Angelo, V. Grassi, R. Mirandola

Erschienen in: Service-Oriented and Cloud Computing

Verlag: Springer International Publishing

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Abstract

This paper proposes a self-organizing fully decentralized solution for the service assembly problem, whose goal is to guarantee a good overall quality for the delivered services, ensuring at the same time fairness among the participating peers. The main features of our solution are: (i) the use of a gossip protocol to support decentralized information dissemination and decision making, and (ii) the use of a reinforcement learning approach to make each peer able to learn from its experience the service selection rule to be followed, thus overcoming the lack of global knowledge. Besides, we explicitly take into account load-dependent quality attributes, which lead to the definition of a service selection rule that drives the system away from overloading conditions that could adversely affect quality and fairness. Simulation experiments show that our solution self-adapts to occurring variations by quickly converging to viable assemblies maintaining the specified quality and fairness objectives.

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Vorheriges Kapitel Dynamic SLAs for Clouds

Nächstes Kapitel Situation-Aware Execution and Dynamic Adaptation of Traditional Workflow Models

Provided by an underlying peer sampling protocol, e.g. NEWSCAST [16].

The upper bound \(N_K\) is a system parameter.

For the sake of simplicity, we omit the details of \(\textsc {Check}_K()\), which strictly depends on the specific implementation of K.

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Titel: Reinforcement Learning Techniques for Decentralized Self-adaptive Service Assembly
verfasst von: M. Caporuscio
M. D’Angelo
V. Grassi
R. Mirandola
Verlag: Springer International Publishing
Buch: Service-Oriented and Cloud Computing
Print ISBN: 978-3-319-44481-9

Electronic ISBN: 978-3-319-44482-6

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-44482-6_4

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