A Survey on Data Plane Programming with P4: Fundamentals, Advances, and Applied Research

TL;DR

This survey comprehensively reviews data plane programming with P4, covering fundamentals, recent advances, and applied research, highlighting its role in network customization and rapid prototyping.

Contribution

It provides a detailed tutorial on P4, categorizes extensive applied research, and analyzes how P4's features benefit various network research domains.

Findings

P4 is widely adopted for programmable data planes across platforms.

Research demonstrates P4's effectiveness in network customization and prototyping.

The survey identifies future directions for P4 technology development.

Abstract

Programmable data planes allow users to define their own data plane algorithms for network devices including appropriate data plane application programming interfaces (APIs) which may be leveraged by user-defined software-defined networking (SDN) control. This offers great flexibility for network customization, be it for specialized, commercial appliances, e.g., in 5G or data center networks, or for rapid prototyping in industrial and academic research. Programming protocol-independent packet processors (P4) has emerged as the currently most widespread abstraction, programming language, and concept for data plane programming. It is developed and standardized by an open community, and it is supported by various software and hardware platforms. In the first part of this paper we give a tutorial of data plane programming models, the P4 programming language, architectures, compilers, targets, and data plane APIs. We also consider research efforts to advance P4 technology. In the second part, we categorize a large body of literature of P4-based applied research into different research domains, summarize the contributions of these papers, and extract prototypes, target platforms, and source code availability. For each research domain, we analyze how the reviewed works benefit from P4's core features. Finally, we discuss potential next steps based on our findings.