Controlling Automatic Experiment-Driven Systems Using Statistics and Machine Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10475283" target="_blank" >RIV/00216208:11320/23:10475283 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-36889-9_9" target="_blank" >https://doi.org/10.1007/978-3-031-36889-9_9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-36889-9_9" target="_blank" >10.1007/978-3-031-36889-9_9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Controlling Automatic Experiment-Driven Systems Using Statistics and Machine Learning

Popis výsledku v původním jazyce

Experiments are used in many modern systems to optimize their operation. Such experiment-driven systems are used in various fields, such as web-based systems, smart-* systems, and various self-adaptive systems. There is a class of these systems that derive their data from running simulations or another type of computation, such as in digital twins, online planning using probabilistic model-checking, or performance benchmarking. To obtain statistically significant results, these systems must repeat the experiments multiple times. As a result, they consume extensive computation resources. The GraalVM benchmarking project detects performance changes in the GraalVM compiler. However, the benchmarking project has an extensive usage of computational resources and time. The doctoral research project proposed in this paper focuses on controlling the experiments with the goal of reducing computation costs. The plan is to use statistical and machine learning approaches to predict the outcomes of experiments and select the experiments yielding more useful information. As an evaluation, we are applying these methods to the GraalVM benchmarking project; the initial results confirm that these methods have the potential to significantly reduce computation costs.

Název v anglickém jazyce

Controlling Automatic Experiment-Driven Systems Using Statistics and Machine Learning

Popis výsledku anglicky

Experiments are used in many modern systems to optimize their operation. Such experiment-driven systems are used in various fields, such as web-based systems, smart-* systems, and various self-adaptive systems. There is a class of these systems that derive their data from running simulations or another type of computation, such as in digital twins, online planning using probabilistic model-checking, or performance benchmarking. To obtain statistically significant results, these systems must repeat the experiments multiple times. As a result, they consume extensive computation resources. The GraalVM benchmarking project detects performance changes in the GraalVM compiler. However, the benchmarking project has an extensive usage of computational resources and time. The doctoral research project proposed in this paper focuses on controlling the experiments with the goal of reducing computation costs. The plan is to use statistical and machine learning approaches to predict the outcomes of experiments and select the experiments yielding more useful information. As an evaluation, we are applying these methods to the GraalVM benchmarking project; the initial results confirm that these methods have the potential to significantly reduce computation costs.

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

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Software Architecture. ECSA 2022 Tracks and Workshops

ISBN

978-3-031-36889-9

ISSN

—

e-ISSN

—

Počet stran výsledku

15

Strana od-do

105-119

Název nakladatele

Springer

Místo vydání

Switzerland

Místo konání akce

Prague, CZ

Datum konání akce

19. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001310761900009