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arXiv 2009.02128cs.NI
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基于学习的网络协议自动设计框架

Towards A Learning-Based Framework for Self-Driving Design of Networking Protocols

Hannaneh Barahouei Pasandi, Tamer Nadeem

摘要:

网络协议是经过人类长期艰苦努力而设计的。已经开发了基于机器学习(ML)的解决方案用于通信协议设计,以避免手动调整各个协议参数。而其他提出的基于ML的方法主要集中在调整各个协议参数(例如,调整竞争窗口),我们的主要贡献是提出了一种新的基于深度强化学习(DRL)的框架,以系统地设计和评估网络协议。我们将协议解耦为一组参数模块,每个模块代表一个主要的协议功能,用作DRL输入,以更好地理解生成的协议设计优化,并以系统的方式分析它们。作为一个案例研究,我们介绍和评估DeepMAC一个框架,在这个框架中,MAC协议被解耦成一组跨流行风格的802.11 WLAN的块(例如,802.11 b/a/g/n/ac)。我们有兴趣看看DeepMAC在不同的网络场景中选择了哪些块,以及DeepMAC是否能够适应网络动态。

英文摘要:

Networking protocols are designed through long-time and hard-work human efforts. Machine Learning (ML)-based solutions have been developed for communication protocol design to avoid manual efforts to tune individual protocol parameters. While other proposed ML-based methods mainly focus on tuning individual protocol parameters (e.g., adjusting contention window), our main contribution is to propose a novel Deep Reinforcement Learning (DRL)-based framework to systematically design and evaluate networking protocols. We decouple a protocol into a set of parametric modules, each representing a main protocol functionality that is used as DRL input to better understand the generated protocols design optimization and analyze them in a systematic fashion. As a case study, we introduce and evaluate DeepMAC a framework in which a MAC protocol is decoupled into a set of blocks across popular flavors of 802.11 WLANs (e.g., 802.11 b/a/g/n/ac). We are interested to see what blocks are selected by DeepMAC across different networking scenarios and whether DeepMAC is able to adapt to network dynamics.

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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