Automated Repair Architecture Using Reward-Driven Artificial Intelligence for Independent Distributed System Restoration and Robustness
Keywords:
Autonomous repair systems, reinforcement learning, distributed systems, cloud resilienceAbstract
Modern distributed systems, particularly cloud and edge-based infrastructures, operate under highly dynamic, heterogeneous, and failure-prone conditions. As system scale increases, traditional reactive fault management mechanisms become insufficient for ensuring reliability, resilience, and continuous service availability. This research proposes an automated repair architecture driven by reward-based artificial intelligence, specifically leveraging reinforcement learning and deep policy optimization techniques, to enable autonomous restoration and robustness in distributed systems.
The proposed framework formulates system failure recovery as a sequential decision-making problem modeled using reinforcement learning principles originally established in Q-learning and extended deep reinforcement learning paradigms (Watkins & Dayan, 1992; Mnih et al., 2015). The architecture integrates distributed monitoring, intelligent fault detection, and reward-driven repair strategies that dynamically adapt to system states in real time. Inspired by large-scale distributed learning systems such as TensorFlow (Abadi et al., 2015) and massively parallel reinforcement learning frameworks (Nair et al., 2015), the system is designed for scalability and robustness across cloud-native environments.
The model further incorporates transfer learning principles (Taylor & Stone, 2009; Weiss et al., 2016) to generalize repair policies across heterogeneous environments, reducing retraining overhead. Additionally, insights from autonomous driving and simulation-based learning systems such as AirSim (Shah et al., 2017) and DeepDriving (Chen et al., 2015) inform the design of simulated failure environments for training and evaluation.
Experimental reasoning suggests that reward-driven autonomous repair systems can significantly reduce mean recovery time, improve system uptime, and enhance fault tolerance compared to traditional rule-based approaches. However, challenges such as reward design complexity, state explosion in distributed systems, and safety constraints in autonomous recovery actions remain critical limitations.
This study contributes a unified conceptual and technical framework for autonomous system restoration, bridging reinforcement learning theory with distributed system engineering to enable next-generation self-healing infrastructures.
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Copyright (c) 2025 Dr. Lucas van der Meer, Dr. Emma J. de Vries
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