A language for feedback loops in self-adaptive systems: Executable runtime megamodels
This work addresses the problem of engineering feedback loops in self-adaptive systems for software developers, representing an incremental advancement by building on existing runtime megamodel concepts.
The paper tackles the challenge of developing adaptation logic in self-adaptive software by introducing a modeling language for runtime megamodels, which eases development through a domain-specific approach and runtime interpreter, enabling explicit modeling of feedback loops at a higher abstraction level and supporting complex interactions.
The development of self-adaptive software requires the engineering of proper feedback loops where an adaptation logic controls the underlying software. The adaptation logic often describes the adaptation by using runtime models representing the underlying software and steps such as analysis and planning that operate on these runtime models. To systematically address this interplay, runtime megamodels, which are specific runtime models that have themselves runtime models as their elements and that also capture the relationships between multiple runtime models, have been proposed. In this paper, we go one step further and present a modeling language for runtime megamodels that considerably eases the development of the adaptation logic by providing a domain-specific modeling approach and a runtime interpreter for this part of a self-adaptive system. This supports development by modeling the feedback loops explicitly and at a higher level of abstraction. Moreover, it permits to build complex solutions where multiple feedback loops interact or operate on top of each other, which is leveraged by keeping the megamodels explicit and alive at runtime and by interpreting them.