Thomas Vogel

@tomvog

Bridging the Gap between Control and Self- Adaptive System Properties: Identification, Characterization, and Mapping

Reference

Javier Camara, David Garlan, Shihong Huang, Masako Kishida, Alberto Leva, Alessandro Vittorio Papadopoulos, Yasuyuki Tahara, Kenji Tei, Thomas Vogel, and Danny Weyns. Bridging the Gap between Control and Self- Adaptive System Properties: Identification, Characterization, and Mapping. Report of the 2nd National Institute of Informatics (NII) Shonan Meeting on Controlled Adaptation of Self-Adaptive Systems (CASaS). Tokyo, Japan, 2017.

Abstract

The development of self-adaptive software requires the engineering of an adaptation engine that controls and adapts the underlying adaptable software by means of feedback loops. The adaptation engine often describes the adaptation by using runtime models representing relevant aspects of the adaptable software and particular activities such as analysis and planning that operate on these runtime models. To systematically address the interplay between runtime models and adaptation activities in adaptation engines, runtime megamodels have been proposed for self-adaptive software. A runtime megamodel is a specific runtime model whose elements are runtime models and adaptation activities. Thus, a megamodel captures the interplay between multiple models and between models and activities as well as the activation of the activities. In this article, we go one step further and present a modeling language for ExecUtable RuntimE MegAmodels (EUREMA) that considerably eases the development of adaptation engines by following a model-driven engineering approach. We provide a domain-specific modeling language and a runtime interpreter for adaptation engines, in particular for feedback loops. Megamodels are kept explicit and alive at runtime and by interpreting them, they are directly executed to run feedback loops. Additionally, they can be dynamically adjusted to adapt feedback loops. Thus, EUREMA supports development by making feedback loops, their runtime models, and adaptation activities explicit at a higher level of abstraction. Moreover, it enables complex solutions where multiple feedback loops interact or even operate on top of each other. Finally, it leverages the co-existence of self-adaptation and off-line adaptation for evolution.

BibTeX

@misc{2017-CASaS-report,
	author = {Camara, Javier and Garlan, David and Huang, Shihong and Kishida, Masako and Leva, Alberto and Papadopoulos, Alessandro Vittorio and Tahara, Yasuyuki and Tei, Kenji and Vogel, Thomas and Weyns, Danny},
	title = {Bridging the Gap between Control and Self- Adaptive System Properties: Identification, Characterization, and Mapping},
	year = {2017},
	howpublished = {Report of the 2nd National Institute of Informatics (NII) Shonan Meeting on Controlled Adaptation of Self-Adaptive Systems (CASaS)},
	address = {Tokyo, Japan},
	url = {https://shonan.nii.ac.jp/archives/seminar/110/},
}
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