论文标题
基于图形学习的基于CNN的CNN推理在动态边缘计算中卸载
Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing
论文作者
论文摘要
本文研究了动态多访问边缘计算（MEC）网络中CNN推断的计算卸载。为了解决通信时间和边缘服务器可用容量中的不确定性，我们使用早期外观机制终止计算以满足推理任务的截止日期。我们设计了一种奖励功能，以权衡交流，计算和推理的准确性，并提出CNN推理的卸载问题，作为最大化问题，目的是长期提高平均推理准确性和吞吐量。为了解决最大化问题，我们提出了一种基于图形学习的早期筛选机制（GRLE），在不同的动态场景下，它胜过最先进的工作，基于深厚的基于强化学习的在线卸载（DROO）及其增强的方法，具有早期EXIT机制（DROOE）。实验结果表明，GRLE的平均准确性比图形增强学习（GRL）高达3.41倍，而DROOE比DROOE达到了1.45倍，这表明了GRLE在动态MEC中卸载决策的优势。
This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and Edge servers' available capacity, we use early-exit mechanism to terminate the computation earlier to meet the deadline of inference tasks. We design a reward function to trade off the communication, computation and inference accuracy, and formulate the offloading problem of CNN inference as a maximization problem with the goal of maximizing the average inference accuracy and throughput in long term. To solve the maximization problem, we propose a graph reinforcement learning-based early-exit mechanism (GRLE), which outperforms the state-of-the-art work, deep reinforcement learning-based online offloading (DROO) and its enhanced method, DROO with early-exit mechanism (DROOE), under different dynamic scenarios. The experimental results show that GRLE achieves the average accuracy up to 3.41x over graph reinforcement learning (GRL) and 1.45x over DROOE, which shows the advantages of GRLE for offloading decision-making in dynamic MEC.
