Title
Antifragility = elasticity + resilience + machine learning : models and algorithms for open system fidelityAntifragility = elasticity + resilience + machine learning : models and algorithms for open system fidelity
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Research group
Modeling Of Systems and Internet Communication (MOSAIC)
Publication type
conferenceObject
Publication
Amsterdam :Elsevier science bv,
Subject
Physics
Engineering sciences. Technology
Computer. Automation
Source (journal)
TECHNOLOGIES (ANT-2014), THE 4TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY (SEIT-2014)
Procedia computer science
Source (book)
5th International Conference on Ambient Systems, Networks and, Technologies (ANT) / 4th International Conference on Sustainable Energy, Information Technology (SEIT), JUN 02-05, 2014, Hasselt, BELGIUM
Volume/pages
32(2014), p. 834-841
ISSN
1877-0509
ISI
000361562600106
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
We introduce a model of the fidelity of open systems-fidelity being interpreted here as the compliance between corresponding figures of interest in two separate but communicating domains. A special case of fidelity is given by real-timeliness and synchrony, in which the figure of interest is the physical and the system's notion of time. Our model covers two orthogonal aspects of fidelity, the first one focusing on a system's steady state and the second one capturing that system's dynamic and behavioural characteristics. We discuss how the two aspects correspond respectively to elasticity and resilience and we highlight each aspect's qualities and limitations. Finally we sketch the elements of a new model coupling both of the first model's aspects and complementing them with machine learning. Finally, a conjecture is put forward that the new model may represent a first step towards compositional criteria for antifragile systems. (C) 2014 The Authors. Published by Elsevier B.V.
E-info
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Full text (open access)
https://repository.uantwerpen.be/docman/irua/b39743/128755.pdf
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