Untangling Co-Drift: Proactive Multi-Intent Failure Prediction and Root-Cause Disambiguation for Self-Driving Networks

2026-07-28

AI Summary

This work addresses the challenge of co-drift among monitoring, analysis, and enforcement intents in self-driving networks, which obscures the distinction between root-cause failure intents and symptomatic victim intents and leads to delayed threshold-based alerts. To overcome this, the paper proposes MILD, a novel framework that shifts intent assurance from passive drift detection to proactive failure prediction. MILD employs a teacher-augmented mixture-of-experts architecture to jointly optimize intent-level failure forecasting and root cause attribution. By integrating multi-timescale modeling with SHAP-based interpretability, it enables KPI-level diagnosis, disambiguates intent-level root causes, and dynamically assesses the urgency of intent failures. Experimental results across statistical benchmarks, microservice applications, and an SDN-enabled edge-cloud testbed demonstrate significant improvements in fault detection rate, root cause localization accuracy, and lead time for remediation.

Abstract

The vision of self-driving networks that monitor, reason, and act upon themselves with minimal human intervention relies on tightly coupled monitoring, analytics, and actuation functions. In this work, we treat these functions as three operational macro-intents: continuous telemetry, real-time analytics, and programmatic actuation, and formalize the health of each function as an intent that the network must continuously satisfy. A critical, yet underexplored, challenge stems from the causal coupling among these intents, where a singular fault within one macro-intent propagates as a co-drift and subsequently triggers cascading, symptomatic anomalies across the remaining intents. This ambiguity makes it exceedingly difficult for existing, reactive approaches to distinguish the true root-cause intent from symptomatic victim intents, and their reliance on threshold-crossing detection leaves insufficient time for proactive remediation. We introduce MILD, a novel framework that reformulates intent assurance from reactive drift detection to proactive failure prediction. Grounded in our three-macro-intent formulation of the self-driving control loop, MILD employs a teacher-augmented Mixture-of-Experts architecture with a hybrid objective that jointly optimizes intent failure prediction and root-cause attribution. MILD enables KPI-level diagnostics via SHAP explainability and dynamic intent failure urgency estimation via multi-horizon modeling. Our extensive evaluation of MILD across three environments of increasing realism, from a controlled statistical benchmark, to a microservices application, to an SDN-based edge-to-cloud testbed, demonstrates that MILD achieves high failure detection rates, strong remediation lead times, and accurate intent-level root-cause disambiguation. This positions MILD as a practical enabler of closed-loop assurance in next-generation autonomous networks.

Problem

• co-drift
• multi-intent failure
• root-cause disambiguation
• self-driving networks
• proactive prediction

Innovation

• proactive failure prediction
• root-cause disambiguation
• Mixture-of-Experts
• intent assurance
• self-driving networks