Context-Aware AIGC Service Migration in Edge Intelligence Networks via Transformer DRL

With the increasing demand for artificial intelligence generated content (AIGC) services across diverse applications, AIGC service migration is essential to ensuring continuous service for mobile users in edge intelligence networks. However, AIGC service migration can lead to decreased inference accuracy due to the discarding of contextual memory. Furthermore, migrating large-scale AIGC models incurs high migration costs and latency. In this paper, we propose a context-aware AIGC service migration scheme to address the trade-off among inference accuracy, latency, and migration cost. Specifically, we focus on migrating historical AIGC context rather than large-scale AIGC models to achieve cost-efficient service provisioning. To improve service migration performance, we propose a Value of Context (VoC) metric to quantify the relevance and freshness of historical AIGC context. Based on the VoC, we formulate an optimization problem to jointly optimize inference accuracy, latency, and migration cost. To solve this problem, we develop a TransFormer-based Soft actor-critic algorithm for Context-aware AIGC service Migration (TFSCM) that leverages long-term dependencies in historical decisions for optimizing the migration process. Extensive experiments on real-world datasets demonstrate that the proposed TFSCM algorithm significantly enhances system performance compared to baseline solutions.