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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

  • Czech description

Classification

  • 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)

Result continuities

  • Project

  • 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

  • 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