Model-Driven Engineering of Self-Adaptive Software with EUREMA

TL;DR

EUREMA introduces an executable modeling language for runtime megamodels, enabling systematic development, dynamic adjustment, and interaction of feedback loops in self-adaptive software systems.

Contribution

It presents a domain-specific language and runtime interpreter for modeling and executing adaptive feedback loops at runtime, advancing model-driven engineering of self-adaptive systems.

Findings

Supports explicit modeling of feedback loops at a higher abstraction level

Enables dynamic adjustment and interaction of multiple feedback loops

Facilitates co-existence of self-adaptation and offline evolution

Abstract

The development of self-adaptive software requires the engineering of an adaptation engine that controls the underlying adaptable software by feedback loops. The engine often describes the adaptation by runtime models representing the adaptable software and by activities such as analysis and planning that use these models. To systematically address the interplay between runtime models and adaptation activities, runtime megamodels have been proposed. A runtime megamodel is a specific model capturing runtime models and adaptation activities. In this article, we go one step further and present an executable modeling language for ExecUtable RuntimE MegAmodels (EUREMA) that 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 feedback loops. Megamodels are kept 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 explicit at a higher level of abstraction and it enables solutions where multiple feedback loops interact or operate on top of each other and self-adaptation co-exists with offline adaptation for evolution.