Self-Adaptation Based on Big Data Analytics: A Model Problem and Tool

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10370634" target="_blank" >RIV/00216208:11320/17:10370634 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/SEAMS.2017.20" target="_blank" >http://dx.doi.org/10.1109/SEAMS.2017.20</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SEAMS.2017.20" target="_blank" >10.1109/SEAMS.2017.20</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Self-Adaptation Based on Big Data Analytics: A Model Problem and Tool

Popis výsledku v původním jazyce

In this paper, we focus on self-adaptation in large-scale software-intensive distributed systems. The main problem in making such systems self-adaptive is that their adaptation needs to consider the current situation in the whole system. However, developing a complete and accurate model of such systems at design time is very challenging. To address this, we present a novel approach where the system model consists only of the essential input and output parameters. Furthermore, Big Data analytics is used to guide self-adaptation based on a continuous stream of operational data. We provide a concrete model problem and a reference implementation of it that can be used as a case study for evaluating different self-adaptation techniques pertinent to complex large-scale distributed systems. We also provide an extensible tool for endorsing an arbitrary system with self-adaptation based on analysis of operational data coming from the system. To illustrate the tool, we apply it on the model problem.

Název v anglickém jazyce

Self-Adaptation Based on Big Data Analytics: A Model Problem and Tool

Popis výsledku anglicky

In this paper, we focus on self-adaptation in large-scale software-intensive distributed systems. The main problem in making such systems self-adaptive is that their adaptation needs to consider the current situation in the whole system. However, developing a complete and accurate model of such systems at design time is very challenging. To address this, we present a novel approach where the system model consists only of the essential input and output parameters. Furthermore, Big Data analytics is used to guide self-adaptation based on a continuous stream of operational data. We provide a concrete model problem and a reference implementation of it that can be used as a case study for evaluating different self-adaptation techniques pertinent to complex large-scale distributed systems. We also provide an extensible tool for endorsing an arbitrary system with self-adaptation based on analysis of operational data coming from the system. To illustrate the tool, we apply it on the model problem.

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

2017 IEEE/ACM 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

ISBN

978-1-5386-1550-8

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

7

Strana od-do

102-108

Název nakladatele

IEEE

Místo vydání

Piscataway, NJ, USA

Místo konání akce

Buenos Aires, Argentina

Datum konání akce

22. 5. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—