认知不确定性估计下的6G网络AI模型部署
AI Model Placement for 6G Networks under Epistemic Uncertainty Estimation
AI总结：
针对6G网络中AI模型部署的不确定性挑战，本文提出一种结合正交证书模块和模糊逻辑的序列到序列（S2S）神经网络方法，在多变环境下实现了有竞争力的部署效果。
摘要：
基于人工智能（AI）的虚拟网络功能（VNFs）的采用已显著增长，这在下一代6G网络中编排AI模型时构成了关键挑战。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、动态存储需求和变化的模型性能，寻找最优的AI模型部署比部署传统的基于软件的VNFs更具挑战性。为了解决不确定性下的AI模型部署问题，本文提出了一种采用考虑不确定性估计的序列到序列（S2S）神经网络的新方法。S2S模型以其编码-解码架构为特征，旨在将包含多个AI模型的服务链作为输入，并生成每个AI模型的相应部署。为了应对引入的不确定性，我们的方法结合了用于不确定性估计的正交证书模块，并利用模糊逻辑进行不确定性表示，从而增强了S2S模型的能力。实验表明，所提出的方法在多样化的AI模型配置、网络环境和服务链请求中取得了有竞争力的结果。
英文摘要：
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.
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。
