Self-Healing Distributed Networks For AI Systems: A Paradigm Shift In Resilient Architecture

Abstract

The article introduces a conceptual system design of AI systems as self-healing distributed networks that can ensure the integrity of operation in cloud, edge, and device environments. The increasingly sophisticated AI deployments are not kept alive by traditional fault tolerance mechanisms, which are not sufficient in maintaining their continuity of learning and quality of inferences through failures. The architecture suggested integrates intelligence into every layer of the infrastructure and algorithm, which allows the infrastructure to keep sensing, diagnosing, and adapting via a multi-layered feedback loop that was based on the biological homeostasis. By arranging self-healing skills into micro, meso, and macro-level control systems, the system can react suitably to varying forms of failures and be coherent globally. The framework incorporates specialized elements of health monitoring, diagnosis, recovery planning, execution, and adaptation that all make up a closed-loop learning system. The case studies show how these principles are implemented in the edge-cloud collaborative systems, large-scale model training, and real-time AI services. Although the results are promising, there are still major challenges in complexity management, observability, resource overhead, and validation methodologies, which indicate research opportunities in formal methods and causal learning, meta-learning, and human-AI collaboration.