SACRE: Supporting contextual requirements' adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

TL;DR

SACRE is a novel approach that enhances self-adaptive systems' ability to handle runtime uncertainties by detecting affected contextual requirements and using machine learning for optimal adaptation, demonstrated in smart vehicle scenarios.

Contribution

SACRE extends previous work by focusing on architectural decisions and real-time evaluation in complex, uncertain environments, integrating machine learning for context operationalization.

Findings

Empirical evidence shows SACRE's effectiveness in smart vehicle simulations.

SACRE successfully detects and adapts to various uncertainty scenarios.

The approach improves system resilience and decision-making under uncertainty.

Abstract

Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today's systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today's systems are still missing. This work presents SACRE (Smart Adaptation through Contextual REquirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems' contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing self-adaptive systems' engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains.