DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach
arxiv(2024)
摘要
The rapid advancement of Artificial Intelligence (AI) has introduced Deep
Neural Network (DNN)-based tasks to the ecosystem of vehicular networks. These
tasks are often computation-intensive, requiring substantial computation
resources, which are beyond the capability of a single vehicle. To address this
challenge, Vehicular Edge Computing (VEC) has emerged as a solution, offering
computing services for DNN-based tasks through resource pooling via
Vehicle-to-Vehicle/Infrastructure (V2V/V2I) communications. In this paper, we
formulate the problem of joint DNN partitioning, task offloading, and resource
allocation in VEC as a dynamic long-term optimization. Our objective is to
minimize the DNN-based task completion time while guaranteeing the system
stability over time. To this end, we first leverage a Lyapunov optimization
technique to decouple the original long-term optimization with stability
constraints into a per-slot deterministic problem. Afterwards, we propose a
Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm,
incorporating the innovative use of diffusion models to determine the optimal
DNN partitioning and task offloading decisions. Furthermore, we integrate
convex optimization techniques into MAD2RL as a subroutine to allocate
computation resources, enhancing the learning efficiency. Through simulations
under real-world movement traces of vehicles, we demonstrate the superior
performance of our proposed algorithm compared to existing benchmark solutions.
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