License: 
Creative Commons Attribution 3.0 Germany license (CC BY 3.0 DE)
When quoting this document, please refer to the following
DOI: 10.4230/DARTS.3.1.5
URN: urn:nbn:de:0030-drops-71435
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2017/7143/
Schmid, Sanny ;
Gerostathopoulos, Ilias ;
Prehofer, Christian ;
Bures, Tomas
Model Problem (CrowdNav) and Framework (RTX) for Self-Adaptation Based on Big Data Analytics (Artifact)
Abstract
This artifact supports our research in 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. We are instead investigating
a novel approach where the system model consists only
of the essential input and output parameters and Big Data
analytics is used to guide self-adaptation based on a continuous
stream of operational data. In this artifact, we provide a concrete model
problem 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-based framework for endorsing an arbitrary
system with self-adaptation based on analysis of operational
data coming from the system. The model problem (CrowdNav) and the framework (RTX) have been packaged together in this artifact, but can also work independently.
BibTeX - Entry
@Article{schmid_et_al:DARTS:2017:7143,
author = {Sanny Schmid and Ilias Gerostathopoulos and Christian Prehofer and Tomas Bures},
title = {{Model Problem (CrowdNav) and Framework (RTX) for Self-Adaptation Based on Big Data Analytics (Artifact)}},
pages = {5:1--5:3},
journal = {Dagstuhl Artifacts Series},
ISSN = {2509-8195},
year = {2017},
volume = {3},
number = {1},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2017/7143},
URN = {urn:nbn:de:0030-drops-71435},
doi = {10.4230/DARTS.3.1.5},
annote = {Keywords: self-adaptation; Big Data analytics; model problem, tool, framework}
}
