The landscape of maintenance in distributed systems is rapidly evolving with the integration of Artificial Intelligence (AI). Also, as the complexity of computing continuum systems intensifies, the role of AI in predictive maintenance (Pd.M.) becomes increasingly pivotal. This paper presents a comprehensive survey of the current state of Pd.M. in the computing continuum, with a focus on the combination of scalable AI technologies. Recognizing the limitations of traditional maintenance practices in the face of increasingly complex and heterogenous computing continuum systems, the study explores how AI, especially machine learning and neural networks, is being used to enhance Pd.M. strategies. The survey encompasses a thorough review of existing literature, highlighting key advancements, methodologies, and case studies in the field. It critically examines the role of AI in improving prediction accuracy for system failures and in optimizing maintenance schedules, thereby contributing to reduced downtime and enhanced system longevity. By synthesizing findings from the latest advancements in the field, the article provides insights into the effectiveness and challenges of implementing AI-driven predictive maintenance. It underscores the evolution of maintenance practices in response to technological advancements and the growing complexity of computing continuum systems. The conclusions drawn from this survey are instrumental for practitioners and researchers in understanding the current landscape and future directions of Pd.M. in distributed systems. It emphasizes the need for continued research and development in this area, pointing towards a trend of more intelligent, efficient, and cost-effective maintenance solutions in the era of AI.

翻译：分布式系统维护领域正随着人工智能（AI）的融入而快速演进。同时，随着计算连续体系统复杂性的加剧，AI在预测性维护（Pd.M.）中的作用愈发关键。本文对计算连续体中Pd.M.的当前状态进行了全面综述，重点关注可扩展AI技术的融合。鉴于传统维护实践在应对日益复杂异构的计算连续体系统时的局限性，本研究探讨了如何利用AI（特别是机器学习与神经网络）来增强Pd.M.策略。本综述系统梳理了现有文献，重点阐述了该领域的关键进展、方法论及案例研究，并批判性审视了AI在提升系统故障预测精度、优化维护调度方面的作用，从而助力降低停机时间、延长系统寿命。通过综合该领域最新进展的研究发现，本文深入剖析了实施AI驱动预测性维护的有效性与挑战，揭示了维护实践随技术进步及计算连续体系统复杂性增长而演进的规律。本综述的结论有助于从业者与研究人员理解分布式系统中Pd.M.的当前格局与未来方向，强调了在该领域持续研发的必要性，并指向AI时代更智能、高效且经济的维护解决方案的发展趋势。