Oraculum: A model for self-adaptive system optimization in smart environments

Abstract

Smart environments require adaptive resource management to handle dynamic workloads and system variability. Traditional solutions, which often rely on static configurations or heuristic adjustments, may not maintain performance as conditions change. This work presents Oraculum, an adaptive model that integrates real-time monitoring, predictive analytics, and automated decision-making. Unlike previous architectures that apply reactive or rule-based adaptations, Oraculum incorporates predictive reinforcement learning (TD3) to anticipate environmental changes and optimize reconfiguration decisions proactively. The model applies data-driven methods to adjust system configurations dynamically, improving both resource allocation and service quality. Experimental results demonstrate that Oraculum significantly reduces Mean Adaptation Time (MAT) compared to existing self-adaptive models while achieving an adaptation accuracy of 97%, overhead of 2%, and maintaining system stability at 98%. These findings highlight the advantages of predictive control in addressing the challenges of dynamic workloads and resource constraints in smart environments, offering a practical approach for maintaining consistent performance.