NeuroRisk: Physics-Informed Neural Optimization for Risk-Aware Traffic Engineering

TL;DR

NeuroRisk is a physics-informed neural optimizer that efficiently manages risk-aware traffic engineering in WANs, significantly reducing computation time while maintaining near-optimal solutions.

Contribution

The paper introduces NeuroRisk, a novel deep unrolled optimizer leveraging the structure of Sort-and-Select for risk-aware traffic engineering in WANs.

Findings

NeuroRisk achieves 10^2 to 10^5 times speedup over traditional solvers.

It maintains small optimality gaps relative to exact solutions.

Outperforms neural baselines on nominal throughput in WAN scenarios.

Abstract

In production Wide-Area Networks (WANs), correlated failures dominate availability losses, forcing operators to reserve large safety margins that leave substantial capacity underutilized. Achieving high utilization under strict availability targets therefore requires risk-aware Traffic Engineering (TE) over dozens to hundreds of probabilistic failure scenarios-yet solving this problem at operational timescales remains elusive. We demonstrate that existing risk-aware formulations can be unified under an embedded Sort-and-Select structure, exposing a fundamental trade-off between expressiveness and tractability: classical optimizers either restrict scenario selection for efficiency or incur prohibitive decomposition costs. While deep learning appears promising, prior Deep TE methods mainly target maximum link utilization and rely on scaling-based feasibility, which fundamentally breaks under explicit capacity constraints and scenario-dependent risk. We present NeuroRisk, a physics-informed deep unrolled optimizer that exploits the structure of Sort-and-Select. NeuroRisk enforces feasibility via gated edge-local reservations and represents scenario sets through permutation-invariant, gradient-aligned cues. Evaluations on production-style WANs show that NeuroRisk achieves small optimality gaps relative to the solver with orders of magnitude speedup $(10^2- 10^5 \times)$ on risk objectives, while outperforming neural baselines on nominal throughput.