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arXiv 2312.02471cs.NIcs.LGeess.SP

使用图神经网络的无线多跳网络中拥塞感知分布式任务卸载

Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks

Rice University(莱斯大学);US Army’s DEVCOM Army Research Laboratory(美国陆军DEVCOM陆军研究实验室)

Zhongyuan Zhao, Jake Perazzone, Gunjan Verma, Santiago Segarra

AI总结:

针对无线多跳网络中多设备任务引发的拥塞问题,本文用图机器学习增强分布式贪心框架,提出低开销的拥塞感知卸载方案,并在20–110节点仿真中验证其降拥塞和降延迟效果。

摘要:

计算卸载已成为移动和智能设备边缘智能的使能组件。现有卸载方案主要关注移动设备和服务器,而忽视了来自多个移动设备的任务可能造成的网络拥塞,尤其是在无线多跳网络中。为填补这一空白,我们通过用基于图的机器学习增强分布式贪心框架,提出了一种低开销、拥塞感知的分布式任务卸载方案。在包含20–110个节点、并采用基于最短路径路由和基于竞争的链路调度的资源分配方案的仿真无线多跳网络中,我们的方法被证明可有效减少与上下文无关基线下的拥塞或不稳定队列,同时相较本地计算改善执行延迟。

英文摘要:

Computational offloading has become an enabling component for edge intelligence in mobile and smart devices. Existing offloading schemes mainly focus on mobile devices and servers, while ignoring the potential network congestion caused by tasks from multiple mobile devices, especially in wireless multi-hop networks. To fill this gap, we propose a low-overhead, congestion-aware distributed task offloading scheme by augmenting a distributed greedy framework with graph-based machine learning. In simulated wireless multi-hop networks with 20-110 nodes and a resource allocation scheme based on shortest path routing and contention-based link scheduling, our approach is demonstrated to be effective in reducing congestion or unstable queues under the context-agnostic baseline, while improving the execution latency over local computing.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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