The adoption of Artificial Intelligence (AI) based Virtual Network Functions (VNFs) has witnessed significant growth, posing a critical challenge in orchestrating AI models within next-generation 6G networks. Finding optimal AI model placement is significantly more challenging than placing traditional software-based VNFs, due to the introduction of numerous uncertain factors by AI models, such as varying computing resource consumption, dynamic storage requirements, and changing model performance. To address the AI model placement problem under uncertainties, this paper presents a novel approach employing a sequence-to-sequence (S2S) neural network which considers uncertainty estimations. The S2S model, characterized by its encoding-decoding architecture, is designed to take the service chain with a number of AI models as input and produce the corresponding placement of each AI model. To address the introduced uncertainties, our methodology incorporates the orthonormal certificate module for uncertainty estimation and utilizes fuzzy logic for uncertainty representation, thereby enhancing the capabilities of the S2S model. Experiments demonstrate that the proposed method achieves competitive results across diverse AI model profiles, network environments, and service chain requests.

翻译：基于人工智能（AI）的虚拟网络功能（VNF）的应用已显著增长，这给在下一代6G网络中编排AI模型带来了关键挑战。由于AI模型引入了众多不确定因素，如变化的计算资源消耗、动态存储需求及模型性能波动，寻找最优的AI模型放置位置比传统基于软件的VNF放置更具挑战性。为在不确定条件下解决AI模型放置问题，本文提出了一种新颖方法，采用结合不确定性估计的序列到序列（S2S）神经网络。该S2S模型具有编码-解码架构，旨在以包含多个AI模型的服务链为输入，并输出各AI模型的对应放置方案。为应对引入的不确定性，本文方法整合了正交证书模块用于不确定性估计，并利用模糊逻辑进行不确定性表征，从而增强S2S模型的能力。实验表明，所提方法在不同AI模型配置文件、网络环境和服务链请求下均取得了具有竞争力的结果。