Self-Adaptation Based on Big Data Analytics: A Model Problem and Tool
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Self-Adaptation Based on Big Data Analytics: A Model Problem and Tool
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2017 IEEE/ACM 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)
ISBN
978-1-5386-1550-8
ISSN
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e-ISSN
neuvedeno
Number of pages
7
Pages from-to
102-108
Publisher name
IEEE
Place of publication
Piscataway, NJ, USA
Event location
Buenos Aires, Argentina
Event date
May 22, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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