REACT-ION: A Model-based Runtime Environment for Situation-aware Adaptations

Author:

Pfannemüller Martin1ORCID,Breitbach Martin1ORCID,Weckesser Markus2ORCID,Becker Christian1ORCID,Schmerl Bradley3ORCID,Schürr Andy2ORCID,Krupitzer Christian4ORCID

Affiliation:

1. Universität Mannheim, Schloss, Mannheim, Germany

2. Technische Universität Darmstadt, Darmstadt, Germany

3. Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

4. Universität Hohenheim, Stuttgart, Germany

Abstract

Trends such as the Internet of Things lead to a growing number of networked devices and to a variety of communication systems. Adding self-adaptive capabilities to these communication systems is one approach to reducing administrative effort and coping with changing execution contexts. Existing frameworks can help reducing development effort but are neither tailored toward the use in communication systems nor easily usable without knowledge in self-adaptive systems development. Accordingly, in previous work, we proposed REACT, a reusable, model-based runtime environment to complement communication systems with adaptive behavior. REACT addresses heterogeneity and distribution aspects of such systems and reduces development effort. In this article, we propose REACT-ION—an extension of REACT for situation awareness. REACT-ION offers a context management module that is able to acquire, store, disseminate, and reason on context data. The context management module is the basis for (i) proactive adaptation with REACT-ION and (ii) self-improvement of the underlying feedback loop. REACT-ION can be used to optimize adaptation decisions at runtime based on the current situation. Therefore, it can cope with uncertainty and situations that were not foreseeable at design time. We show and evaluate in two case studies how REACT-ION’s situation awareness enables proactive adaptation and self-improvement.

Funder

German Research Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

Software,Computer Science (miscellaneous),Control and Systems Engineering

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Rango: An Intuitive Rule Language for Learning Classifier Systems in Cyber-Physical Systems;2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS);2022-09

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