SEApr 3, 2018

mRUBiS: An Exemplar for Model-Based Architectural Self-Healing and Self-Optimization

arXiv:1804.00954v133 citationsHas Code
Originality Synthesis-oriented
AI Analysis

This addresses a gap for researchers and developers in self-adaptive software systems by offering a practical tool for comparison and experimentation, though it is incremental as it builds on existing model-based approaches.

The paper tackles the lack of an exemplar for developing and evaluating model-based architectural self-adaptation in software systems, presenting mRUBiS as an extensible tool that simulates adaptable software and provides a runtime model for self-healing and self-optimization.

Self-adaptive software systems are often structured into an adaptation engine that manages an adaptable software by operating on a runtime model that represents the architecture of the software (model-based architectural self-adaptation). Despite the popularity of such approaches, existing exemplars provide application programming interfaces but no runtime model to develop adaptation engines. Consequently, there does not exist any exemplar that supports developing, evaluating, and comparing model-based self-adaptation off the shelf. Therefore, we present mRUBiS, an extensible exemplar for model-based architectural self-healing and self-optimization. mRUBiS simulates the adaptable software and therefore provides and maintains an architectural runtime model of the software, which can be directly used by adaptation engines to realize and perform self-adaptation. Particularly, mRUBiS supports injecting issues into the model, which should be handled by self-adaptation, and validating the model to assess the self-adaptation. Finally, mRUBiS allows developers to explore variants of adaptation engines (e.g., event-driven self-adaptation) and to evaluate the effectiveness, efficiency, and scalability of the engines.

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes