Summary of Dnn Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: a Lyapunov-guided Diffusion-based Reinforcement Learning Approach, by Zhang Liu and Hongyang Du and Junzhe Lin and Zhibin Gao and Lianfen Huang and Seyyedali Hosseinalipour and Dusit Niyato
DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach
by Zhang Liu, Hongyang Du, Junzhe Lin, Zhibin Gao, Lianfen Huang, Seyyedali Hosseinalipour, Dusit Niyato
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the challenge of processing Deep Neural Network (DNN)-based tasks in vehicular networks, where a single vehicle lacks sufficient computation resources. Vehicular Edge Computing (VEC) offers a solution by pooling resources through Vehicle-to-Vehicle/Infrastructure (V2V/V2I) communications. The authors formulate the problem as a dynamic long-term optimization to minimize task completion time while ensuring system stability. They use Lyapunov optimization and propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating diffusion models for DNN partitioning and task offloading decisions. Additionally, they integrate convex optimization techniques to allocate computation resources. The proposed algorithm outperforms existing benchmarks in simulations using real-world vehicle movement traces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how to make it possible for vehicles to use their own computing power for complex tasks like artificial intelligence. Right now, a single car doesn’t have enough power to do these tasks on its own. Vehicular Edge Computing helps by sharing resources between cars and infrastructure. The problem is how to decide when to share the work and where to do it. The authors come up with a new algorithm that uses special techniques to make this decision efficiently. They test their algorithm using real car movement data and show that it performs better than existing solutions. |
Keywords
» Artificial intelligence » Diffusion » Neural network » Optimization » Reinforcement learning
