A language for feedback loops in self-adaptive systems: Executable runtime megamodels

TL;DR

This paper introduces a domain-specific modeling language and runtime interpreter for runtime megamodels, enabling explicit, high-level modeling of feedback loops in self-adaptive systems to improve development and support complex, interacting adaptation processes.

Contribution

It presents a novel modeling language and runtime interpreter for runtime megamodels, facilitating explicit and high-level design of feedback loops in self-adaptive systems.

Findings

Supports development by modeling feedback loops explicitly

Enables building complex, interacting feedback loops

Keeps megamodels explicit and alive at runtime

Abstract

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.