Abstract

The increasing complexity of modern software systems, coupled with dynamic environmental conditions and unpredictable workloads, has driven the evolution from traditional static systems to self-adaptive systems capable of autonomous behavior. At the heart of self-adaptation lies the integration of autonomic computing principles and feedback control loops that enable systems to monitor their own behavior, reason about performance objectives, and reconfigure themselves in response to changes. This research explores the design and realization of self-adaptive systems grounded in autonomic computing frameworks, emphasizing the role of feedback control mechanisms such as Monitor-Analyze-Plan-Execute (MAPE) and proportional-integral-derivative (PID) controllers. Through comprehensive analysis, we examine architectural patterns, adaptation policies, control strategies, and decision models that facilitate dynamic adaptation while ensuring system stability and robustness. The study synthesizes current literature, compares methodologies, and identifies key challenges including latency, scalability, and conflicting adaptation goals. Results demonstrate that incorporating formal feedback models enhances predictability and reduces adaptation overhead when designed with appropriate objectives and performance metrics. The paper concludes with insights into future research directions, such as machine learning-based control, hybrid control architectures, and integration with DevOps pipelines to support continuous adaptation in evolving environments.