Artificial Intelligence (AI) -现代分布式计算系统中以资源为中心的管理
Artificial Intelligence (AI)-Centric Management of Resources in Modern Distributed Computing Systems
摘要：
诸如云数据中心的当代分布式计算系统（DCS）是大规模的、复杂的、异构的，并且分布在多个网络和地理边界上。另一方面，物联网（IoT）驱动的应用程序正在产生大量数据，需要实时处理和快速响应。有效地管理这些资源以向最终用户或应用程序提供可靠的服务是一项具有挑战性的任务。现有的资源管理系统（RMS）依赖于静态或启发式的解决方案不适合这样的复合和动态系统。由于数据可用性和处理能力，人工智能（AI）的出现表现为在RMS任务中探索数据驱动的解决方案的可能性，这些解决方案是自适应的，准确的，高效的。在这方面，本文的目的是得出在资源管理中的数据驱动的解决方案的动机和必要性。它确定了与之相关的挑战，并概述了未来潜在的研究方向，详细说明了在不同的RMS任务中如何应用数据驱动技术。最后，它为DCS提供了一个概念性的数据驱动RMS模型，并展示了两个实时用例（GPU频率扩展和来自Google Cloud和Microsoft Azure的数据中心资源管理），展示了以AI为中心的方法的可行性。
英文摘要：
Contemporary Distributed Computing Systems (DCS) such as Cloud Data Centres are large scale, complex, heterogeneous, and distributed across multiple networks and geographical boundaries. On the other hand, the Internet of Things (IoT)-driven applications are producing a huge amount of data that requires real-time processing and fast response. Managing these resources efficiently to provide reliable services to end-users or applications is a challenging task. The existing Resource Management Systems (RMS) rely on either static or heuristic solutions inadequate for such composite and dynamic systems. The advent of Artificial Intelligence (AI) due to data availability and processing capabilities manifested into possibilities of exploring data-driven solutions in RMS tasks that are adaptive, accurate, and efficient. In this regard, this paper aims to draw the motivations and necessities for data-driven solutions in resource management. It identifies the challenges associated with it and outlines the potential future research directions detailing where and how to apply the data-driven techniques in the different RMS tasks. Finally, it provides a conceptual data-driven RMS model for DCS and presents the two real-time use cases (GPU frequency scaling and data centre resource management from Google Cloud and Microsoft Azure) demonstrating AI-centric approaches' feasibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
