Generative AI (GenAI), exemplified by Large Language Models (LLMs) such as OpenAI's ChatGPT, is revolutionizing various fields. Central to this transformation is Data Center Networking (DCN), which not only provides the computational power necessary for GenAI training and inference but also delivers GenAI-driven services to users. This article examines an interplay between GenAI and DCNs, highlighting their symbiotic relationship and mutual advancements. We begin by reviewing current challenges within DCNs and discuss how GenAI contributes to enhancing DCN capabilities through innovations, such as data augmentation, process automation, and domain transfer. We then focus on analyzing the distinctive characteristics of GenAI workloads on DCNs, gaining insights that catalyze the evolution of DCNs to more effectively support GenAI and LLMs. Moreover, to illustrate the seamless integration of GenAI with DCNs, we present a case study on full-lifecycle DCN digital twins. In this study, we employ LLMs equipped with Retrieval Augmented Generation (RAG) to formulate optimization problems for DCNs and adopt Diffusion-Deep Reinforcement Learning (DRL) for optimizing the RAG knowledge placement strategy. This approach not only demonstrates the application of advanced GenAI methods within DCNs but also positions the digital twin as a pivotal GenAI service operating on DCNs. We anticipate that this article can promote further research into enhancing the virtuous interaction between GenAI and DCNs.

翻译：以OpenAI的ChatGPT等大型语言模型为代表的生成式人工智能正在革新多个领域。这一变革的核心在于数据中心网络，它不仅为生成式人工智能的训练和推理提供必要的计算能力，还向用户提供生成式人工智能驱动的服务。本文探讨了生成式人工智能与数据中心网络之间的相互作用，强调了它们的共生关系和共同进步。我们首先回顾了数据中心网络当前面临的挑战，并讨论了生成式人工智能如何通过数据增强、流程自动化和领域迁移等创新来提升数据中心网络能力。随后，我们重点分析了生成式人工智能工作负载在数据中心网络中的独特特征，这些洞见推动了数据中心网络的演进，以更有效地支持生成式人工智能和大型语言模型。此外，为阐明生成式人工智能与数据中心网络的无缝集成，我们提出了一个关于全生命周期数据中心网络数字孪生的案例研究。在该研究中，我们采用配备检索增强生成的大型语言模型来构建数据中心网络的优化问题，并采用扩散-深度强化学习来优化检索增强生成的知识放置策略。该方法不仅展示了先进生成式人工智能方法在数据中心网络中的应用，还将数字孪生定位为在数据中心网络上运行的关键生成式人工智能服务。我们期望本文能推动进一步研究，以增强生成式人工智能与数据中心网络之间的良性互动。