Exploring the use of metaheuristic search to infer models of dynamic system behaviour

As software systems become more pervasive and increase in both size and complexity, the requirement for systems to be able to selfadapt to changing environments becomes more important. This paper describes a model-based approach for adapting to dynamic runtime environments using metaheuristic optimisation techniques. The metaheuristics exploit metamodels that capture the important components in the adaptation process. Firstly, a model of the environment's behaviour is extracted using a combination of inference and search. The model of the environment is then used in the discovery of a model of optimal system behaviour - i.e. how the system should best behave in response to the environment. The system is then updated based on this model. This paper focuses on investigating the extraction of a behaviour model of the environment and describes how our previous work can be utilised for the adaptation stage. We contextualise the approach using an example and analyse different ways of applying the metaheuristic algorithms for discovering an optimal model of the case study's environment.