In the face of increasing demand for zero-touch networks to automate network management and operations, two pivotal concepts have emerged: "Learn to Slice" (L2S) and "Slice to Learn" (S2L). L2S involves leveraging Artificial intelligence (AI) techniques to optimize network slicing for general services, while S2L centers on tailoring network slices to meet the specific needs of various AI services. The complexity of optimizing and automating S2L surpasses that of L2S due to intricate AI services' requirements, such as handling uncontrollable parameters, learning in adversarial conditions, and achieving long-term performance goals. This paper aims to automate and optimize S2L by integrating the two concepts of L2S and S2L by using an intelligent slicing agent to solve S2L. Indeed, we choose two candidate slicing agents, namely the Exploration and Exploitation (EXP3) and Deep Q-Network (DQN) from the Online Convex Optimization (OCO) and Deep Reinforcement Learning (DRL) frameworks, and compare them. Our evaluation involves a series of carefully designed experiments that offer valuable insights into the strengths and limitations of EXP3 and DQN in slicing for AI services, thereby contributing to the advancement of zero-touch network capabilities.

翻译：面对零接触网络自动化网络管理与运维需求的日益增长，两个关键概念应运而生："学习切片"（L2S）与"切片学习"（S2L）。L2S涉及利用人工智能（AI）技术优化面向通用服务的网络切片，而S2L则专注于定制网络切片以满足各类AI服务的特定需求。由于复杂AI服务需求（如处理不可控参数、在对抗条件下学习、实现长期性能目标等）的存在，S2L的优化与自动化复杂度远超L2S。本文旨在通过整合L2S与S2L两大概念，利用智能切片代理求解S2L问题，从而实现S2L的自动化与优化。具体而言，我们选取在线凸优化（OCO）与深度强化学习（DRL）框架下的两种候选切片代理——即探索与利用（EXP3）算法和深度Q网络（DQN）——进行对比研究。通过一系列精心设计的实验评估，本研究揭示了EXP3与DQN在AI服务切片场景中的优势与局限，为零接触网络能力的发展提供了重要见解。