A Model-Based Fuzzy Control Approach to Achieving Adaptation with Contextual Uncertainties

TL;DR

This paper presents a model-based fuzzy control method for self-adaptive systems to dynamically manage uncertainties in requirements and context, enhancing adaptability in volatile environments.

Contribution

It introduces a fuzzy control approach underpinned by feedforward-feedback mechanisms to handle uncertainties in NFRs and context for self-adaptive systems.

Findings

Effective adaptation of NFRs and tasks demonstrated in mobile computing application.

Fuzzy control approach successfully manages contextual uncertainties.

Simulation experiments validate the approach's effectiveness.

Abstract

Self-adaptive system (SAS) is capable of adjusting its behavior in response to meaningful changes in the operational context and itself. Due to the inherent volatility of the open and changeable environment in which SAS is embedded, the ability of adaptation is highly demanded by many software-intensive systems. Two concerns, i.e., the requirements uncertainty and the context uncertainty are most important among others. An essential issue to be addressed is how to dynamically adapt non-functional requirements (NFRs) and task configurations of SASs with context uncertainty. In this paper, we propose a model-based fuzzy control approach that is underpinned by the feedforward-feedback control mechanism. This approach identifies and represents NFR uncertainties, task uncertainties and context uncertainties with linguistic variables, and then designs an inference structure and rules for the fuzzy controller based on the relations between the requirements model and the context model. The adaptation of NFRs and task configurations is achieved through fuzzification, inference, defuzzification and readaptation. Our approach is demonstrated with a mobile computing application and is evaluated through a series of simulation experiments.