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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GC20-24814J" target="_blank" >GC20-24814J: FluidTrust - popora důvěry pomocí dynamicky proměnlivého řízení přistupu k datům a zdrojům v systémech Průmyslu 4.0</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science

ISBN

978-3-031-36888-2

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

16

Strana od-do

370-385

Název nakladatele

Springer Internat. Publ.

Místo vydání

Cham

Místo konání akce

Prague, Czech Republic

Datum konání akce

20. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001310761900025