Controlling Automatic Experiment-Driven Systems Using Statistics and Machine Learning

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Controlling Automatic Experiment-Driven Systems Using Statistics and Machine Learning

Original language description

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.

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

—

Continuities

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

Publication year

2023

Confidentiality

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

Article name in the collection

Software Architecture. ECSA 2022 Tracks and Workshops

ISBN

978-3-031-36889-9

ISSN

—

e-ISSN

—

Number of pages

15

Pages from-to

105-119

Publisher name

Springer

Place of publication

Switzerland

Event location

Prague, CZ

Event date

Sep 19, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001310761900009