DiffLoader: Environment-Adaptive Computation Offloading via Generative Prompt-Conditional Planning
Abstract
Facing the rapid development of both user equipment (UE) and mobile applications, mobile edge computing (MEC) emerged as a new paradigm to furnish UE with satisfactory computational capability and task latency. Studies on MEC have put a great deal of focus on computation offloading, mainly involving multi-step task offloading and resource allocation (OffAll) which is mostly non-trivial. Concerning the heavy burden of online OffAll optimization on scalability and efficiency, offline learning-based OffAll approaches are of high popularity. However, most existing learning-based OffAll is still short of generalizability and adaptability across different MECs, thus difficult for general-purpose application. Inspired by the remarkable success of LLMs for their impressive generalization ability across different tasks by conditioning on instructions or prompts, this paper proposes a diffusion-based OffAll approach (termed DiffLoader) by utilizing the generative conditional-planning ability of diffusion models learned from multi-MEC OffAll experiences. Specifically, we leverage a few-shot trajectory of MEC states transitions as MEC-specific prompts, adopt diffusion to learn multi-MEC conditional OffAll distribution, and generate OffAll decisions via distribution-sampling during execution. We conduct extensive experiments to verify the advantages of DiffLoader over state-of-the-art learning-based multi-MEC OffAll approaches, and also show the compositive generating ability of DiffLoader even for unseen MEC.
