In recent years, the landscape of computing paradigms has witnessed a gradual yet remarkable shift from monolithic computing to distributed and decentralized paradigms such as Internet of Things (IoT), Edge, Fog, Cloud, and Serverless. The frontiers of these computing technologies have been boosted by shift from manually encoded algorithms to Artificial Intelligence (AI)-driven autonomous systems for optimum and reliable management of distributed computing resources. Prior work focuses on improving existing systems using AI across a wide range of domains, such as efficient resource provisioning, application deployment, task placement, and service management. This survey reviews the evolution of data-driven AI-augmented technologies and their impact on computing systems. We demystify new techniques and draw key insights in Edge, Fog and Cloud resource management-related uses of AI methods and also look at how AI can innovate traditional applications for enhanced Quality of Service (QoS) in the presence of a continuum of resources. We present the latest trends and impact areas such as optimizing AI models that are deployed on or for computing systems. We layout a roadmap for future research directions in areas such as resource management for QoS optimization and service reliability. Finally, we discuss blue-sky ideas and envision this work as an anchor point for future research on AI-driven computing systems.

翻译：近年来，计算范式的格局经历了从单体计算向分布式与去中心化计算（如物联网、边缘计算、雾计算、云计算和无服务器计算）的渐进式显著转变。这些计算技术的前沿已从人工编码算法转向人工智能驱动的自主系统，以实现分布式计算资源的最优可靠管理。现有研究主要聚焦于利用人工智能在资源高效配置、应用部署、任务调度及服务管理等多个领域改进现有系统。本综述回顾了数据驱动的人工智能增强技术的演进历程及其对计算系统的影响。我们揭示新兴技术本质，提炼边缘、雾和云计算资源管理中人工智能方法的关键应用见解，并剖析在资源连续体环境下人工智能如何革新传统应用以增强服务质量。我们呈现了诸如面向计算系统部署的AI模型优化等最新趋势与影响领域，并规划了面向服务质量优化与服务可靠性的资源管理研究路线图。最后，我们探讨具有前瞻性的构想，将本研究定位为未来人工智能驱动计算系统研究的关键锚点。