Model-Based Diagnosis: Automating End-to-End Diagnosis of Network Failures

TL;DR

This paper introduces a model-based approach for automated network diagnosis that systematically derives procedures from network models, enabling rapid and accurate identification of faults across hardware, software, and control/data planes.

Contribution

It presents a novel paradigm for network diagnosis that automates root cause analysis from end-to-end symptoms using a comprehensive model-based framework, covering multiple fault types.

Findings

100% fault diagnosis accuracy in validation tests

Diagnoses completed in seconds instead of hours

Successfully deployed on P4 switch network emulator

Abstract

Fast diagnosis and repair of enterprise network failures is critically important since disruptions cause major business impacts. Prior works focused on diagnosis primitives or procedures limited to a subset of the problem, such as only data plane or only control plane faults. This paper proposes a new paradigm, model-based network diagnosis, that provides a systematic way to derive automated procedures for identifying the root cause of network failures, based on reports of end-to-end user-level symptoms. The diagnosis procedures are systematically derived from a model of packet forwarding and routing, covering hardware, firmware, and software faults in both the data plane and distributed control plane. These automated procedures replace and dramatically accelerate diagnosis by an experienced human operator. Model-based diagnosis is inspired by, leverages, and is complementary to recent work on network verification. We have built NetDx, a proof-of-concept implementation of model-based network diagnosis. We deployed NetDx on a new emulator of networks consisting of P4 switches with distributed routing software. We validated the robustness and coverage of NetDx with an automated fault injection campaign, in which 100% of faults were diagnosed correctly. Furthermore, on a data set of 33 faults from a large cloud provider that are within the domain targeted by NetDx, 30 are efficiently diagnosed in seconds instead of hours.