AI Model Placement for 6G Networks under Epistemic Uncertainty Estimation
CoRR(2024)
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.
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