Tracer: A Tool for Race Detection in Software Defined Network Models

TL;DR

Tracer is a tool that automatically detects and explains data races in SDN models by analyzing message passing between control and data planes using formal DyNetKAT semantics.

Contribution

It introduces Tracer, a novel tool leveraging DyNetKAT's formal framework and Lamport vector clocks for effective race detection in SDN environments.

Findings

Tracer successfully detects data races in SDN models.

The tool provides explanations for race occurrences.

Tracer is publicly available for use and testing.

Abstract

Software Defined Networking (SDN) has become a new paradigm in computer networking, introducing a decoupled architecture that separates the network into the data plane and the control plane. The control plane acts as the centralized brain, managing configuration updates and network management tasks, while the data plane handles traffic based on the configurations provided by the control plane. Given its asynchronous distributed nature, SDN can experience data races due to message passing between the control and data planes. This paper presents Tracer, a tool designed to automatically detect and explain the occurrence of data races in DyNetKAT SDN models. DyNetKAT is a formal framework for modeling and analyzing SDN behaviors, with robust operational semantics and a complete axiomatization implemented in Maude. Built on NetKAT, a language leveraging Kleene Algebra with Tests to express data plane forwarding behavior, DyNetKAT extends these capabilities by adding primitives for communication between the control and data planes. Tracer exploits the DyNetKAT axiomatization and enables race detection in SDNs based on Lamport vector clocks. Tracer is a publicly available tool.