Proceedings paper

Title:
Self-Adaptation Based on Big Data Analytics: A Model Problem and Tool
Authors:
S. Schmid, I. Gerostathopoulos, C. Prehofer, Tomáš Bureš
Publication:
IEEE/ACM 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
DOI:
Year:
2017
ISBN:
978-1-5386-1550-8
Link:

Abstract:
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.

BibTeX:
@inproceedings{schmid_selfadaptation_2017,
    title = {{Self-Adaptation Based on Big Data Analytics: A Model Problem and Tool}},
    author = {Schmid, S. and Gerostathopoulos, I. and Prehofer, C. and Bures, T.},
    year = {2017},
    booktitle = {{IEEE/ACM 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems}},
    publisher = {IEEE},
    series = {{SEAMS '17}},
    location = {Buenos Aires, Argentina},
    doi = {10.1109/SEAMS.2017.20},
    isbn = {978-1-5386-1550-8},
    pages = {102--108},
    url = {https://ieeexplore.ieee.org/document/7968137},
    shorttitle = {Self-Adaptation Based on Big Data Analytics},
}