Introducing Estimators-Abstraction for Easy ML Employment in Self-adaptive Architectures

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10467810" target="_blank" >RIV/00216208:11320/23:10467810 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-36889-9_25" target="_blank" >https://doi.org/10.1007/978-3-031-36889-9_25</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-36889-9_25" target="_blank" >10.1007/978-3-031-36889-9_25</a>

Result language
angličtina
Original language name
Introducing Estimators-Abstraction for Easy ML Employment in Self-adaptive Architectures
Original language description
Machine learning (ML) has shown its potential in extending the ability of self-adaptive systems to deal with unknowns. To date, there have been several approaches to applying ML in different stages of the adaptation loop. However, the systematic inclusion of ML in the architecture of self-adaptive applications is still an objective that has not been very elaborated yet. In this paper, we show one approach to address this by introducing the concept of estimators in an architecture of a self-adaptive system. The estimator serves to provide predictions on future and currently unobservable values via ML. As a proof of concept, we show how estimators are employed in ML-DEECo-a dedicated ML-enabled component model for adaptive component architectures. It is based on our DEECo component model, which features autonomic components and dynamic component coalitions (ensembles). It makes it possible to specify ML-based adaptation already at the level of the component-based application architecture (i.e., at the model level) without having to explicitly deal with the intricacies of the adaptation loop. As part of the evaluation, we provide an open-source implementation of ML-DEECo run-time framework in Python.
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GC20-24814J" target="_blank" >GC20-24814J: FluidTrust – Enabling trust by fluid access control to data and physical resources in Industry 4.0 systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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