ActivFORMS: active formal models for self-adaptation

Abstract

Self-adaptation enables a software system to deal autonomously with uncertainties, such as dynamic operating conditions that are difficult to predict or changing goals. A common approach to realize self-adaptation is with a MAPE-K feedback loop that consists of four adaptation components: Monitor, Analyze, Plan, and Execute. These components share Knowledge models of the managed system, its goals and environment. To provide guarantees of the adaptation goals, state of the art approaches propose using formal models of the knowledge. However, less attention is given to the formalization of the adaptation components themselves, which is important to provide guarantees of correctness of the adaptation behavior (e.g., does the execute component execute the plan correctly?). We propose Active FORmal Models for Self-adaptation (ActivFORMS) that uses an integrated formal model of the adaptation components and knowledge models. The formal model is directly executed by a virtual machine to realize adaptation, hence active model. The contributions of ActivFORMS are: (1) the approach assures that the adaptation goals that are verified offline are guaranteed at runtime, and (2) it supports dynamic adaptation of the active model to support changing goals. We show how we have applied ActivFORMS for a small-scale robotic system.