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

TL;DR

This paper introduces a novel deep reinforcement learning framework for systematic design and evaluation of networking protocols, exemplified by a case study on WLAN MAC protocols, aiming to automate and optimize protocol configuration.

Contribution

It proposes a decoupled, parametric DRL-based framework for protocol design, enabling systematic analysis and adaptation, which is a significant advancement over prior parameter-tuning methods.

Findings

DeepMAC can adapt to different network scenarios.

The framework identifies effective protocol blocks across WLAN standards.

DeepMAC demonstrates potential for automated protocol design.

Abstract

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.