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

2025-06-29

AI Summary

Rapid root-cause diagnosis of enterprise network failures has long been hindered by fragmented diagnostic scope—limited to either the data or control plane—and heavy reliance on manual analysis. Method: This paper proposes a model-based network diagnosis paradigm that, for the first time, systematically unifies hardware, firmware, and software-layer faults, jointly modeling both the data plane and distributed control plane. Leveraging network verification techniques, it constructs end-to-end forwarding and routing models, enabling automated root-cause inference from user-level symptoms within P4 switch and distributed routing software simulation environments. Contribution/Results: Evaluation demonstrates 100% diagnostic accuracy in simulation. On 33 real-world failure cases from a major cloud provider, the approach localizes root causes for 30 cases within seconds—achieving over two orders-of-magnitude speedup versus manual analysis—and significantly advances automation in enterprise network operations.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Probabilistic Inference

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Automates end-to-end diagnosis of network failures
• Covers data plane and control plane faults systematically
• Accelerates diagnosis compared to human operators

Innovation

• Model-based network diagnosis for automated root cause identification
• Systematic derivation from packet forwarding and routing models
• Proof-of-concept NetDx implementation with high accuracy