Orchestrating Generative AI Services in Ultra-Dense 6G Networks: A Mean Field Game Framework

Abstract

Future 6G networks will enable pervasive Generative Artificial Intelligence (GenAI) services. Migrating Large Language Model (LLM) inference to the network edge is essential to meet the low-latency and privacy demands of these services. However, LLM inference offloading faces a mismatch between resource-limited edge infrastructure and volatile computational demands. Conventional offloading schemes for static bit-level tasks cannot sufficiently handle the prefill/decoding phase difference and the uncertain output length of LLM requests. To bridge this gap, we derive a stochastic workload model based on decoder-only Transformer mechanics and profiled input/output token statistics, characterizing expected workload and task-dependent fluctuations. We embed these statistics into edge-cloud queue dynamics and formulate the massive-scale collaborative inference problem as a Mean Field Game (MFG). We approximate the high-concurrency queue dynamics using Stochastic Differential Equations (SDEs) and develop a Mean Field State Deep Q-Network (MFSDQN) algorithm. MFSDQN enables edge agents to learn decentralized offloading-ratio decisions under Quality of Service (QoS) constraints using local observations and neighbor-assisted mean-field states, avoiding global edge-state exchange. Extensive simulations with up to 500 edge agents demonstrate stable convergence and scalability. MFSDQN reduces the average system cost and unfinished workload by 8.0% and 6.3%, respectively, relative to the best-performing baseline.