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,计算范式的格局逐渐但显著地从单一计算转向分布式和分散式的范式,如物(IoT)、Edge、Fog、Cloud和无服务器的互联网。这些计算技术的前沿由于从人工编码算法转向人工智能驱动的优化和可靠管理分布式计算资源自主系统而得到加强。先前的工作重点是在一系列广泛的领域,如高效提供资源、应用部署、任务安排和服务管理,改进使用AI的现有系统。本调查审查了数据驱动的AI强化技术的演变及其对计算机系统的影响。我们解开新技术的神秘化,在Edge、Fog和云资源管理方面与AI方法相关使用的关键见解,并研究了AI如何在资源连续存在的情况下创新传统应用提高服务的质量。我们介绍了最新的趋势和影响领域,如优化在系统上部署的AI模型或计算机化模型。我们为未来研究方向绘制了路线图,如资源驱动型AI-S研究的可靠性,我们最后讨论了这一系统。