Adapting a system with noisy outputs with statistical guarantees

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10389005" target="_blank" >RIV/00216208:11320/18:10389005 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1145/3194133.3194152" target="_blank" >https://doi.org/10.1145/3194133.3194152</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3194133.3194152" target="_blank" >10.1145/3194133.3194152</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Adapting a system with noisy outputs with statistical guarantees

Popis výsledku v původním jazyce

Many complex systems are intrinsically stochastic in their behavior which complicates their control and optimization. Current self-adaptation and self-optimization approaches are not tailored to systems that have (i) complex internal behavior that is unrealistic to model explicitly, (ii) noisy outputs, (iii) high cost of bad adaptation decisions, i.e. systems that are both hard and risky to adapt at runtime. In response, we propose to model the system to be adapted as black box and apply state-of-the-art optimization techniques combined with statistical guarantees. Our main contribution is a framework that combines runtime optimization with guarantees obtained from statistical testing and with a method for handling cost of bad adaptation decisions. We evaluate the feasibility of our approach by applying it on an existing traffic navigation self-adaptation exemplar.

Název v anglickém jazyce

Adapting a system with noisy outputs with statistical guarantees

Popis výsledku anglicky

Many complex systems are intrinsically stochastic in their behavior which complicates their control and optimization. Current self-adaptation and self-optimization approaches are not tailored to systems that have (i) complex internal behavior that is unrealistic to model explicitly, (ii) noisy outputs, (iii) high cost of bad adaptation decisions, i.e. systems that are both hard and risky to adapt at runtime. In response, we propose to model the system to be adapted as black box and apply state-of-the-art optimization techniques combined with statistical guarantees. Our main contribution is a framework that combines runtime optimization with guarantees obtained from statistical testing and with a method for handling cost of bad adaptation decisions. We evaluate the feasibility of our approach by applying it on an existing traffic navigation self-adaptation exemplar.

Druh

D - Stať ve sborníku

CEP obor

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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/LTE117003" target="_blank" >LTE117003: ESTABLISH - Senzory životního prostředí pro lepší kvalitu života: Smart Health</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

ACM/IEEE 13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

ISBN

978-1-4503-5715-9

ISSN

0270-5257

e-ISSN

neuvedeno

Počet stran výsledku

11

Strana od-do

58-68

Název nakladatele

ACM

Místo vydání

USA

Místo konání akce

Gothenburg, Sweden

Datum konání akce

28. 5. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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