论文标题
建立基于学习的框架，用于网络协议的自动驾驶设计
Towards A Learning-Based Framework for Self-Driving Design of Networking Protocols
论文作者
论文摘要
网络协议是通过长期和努力的人类努力设计的。已经为通信协议设计开发了机器学习（ML）解决方案，以避免手动努力调整单个协议参数。尽管其他提出的基于ML的方法主要集中于调整单个协议参数（例如，调整争夺窗口），但我们的主要贡献是提出一种新型的深入强化学习（DRL）基于基于系统设计和评估网络协议的框架。我们将协议解除到一组参数模块中，每个模块代表主要协议功能，该功能用作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进行分析。作为一个案例研究，我们介绍和评估DeepMac一个框架，其中MAC协议将其分解为802.11 WLAN的流行口味的一组块（例如802.11 b/a/a/a/g/n/ac）。我们有兴趣了解DeepMac在不同的网络方案中选择了哪些块，以及DeepMac是否能够适应网络动态。
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.
