NeuroRisk is a physics-informed deep unrolled optimizer for risk-aware traffic engineering that achieves small optimality gaps and 100-100000x speedup over solvers while outperforming neural baselines on throughput.
InACM SIGCOMM
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.NI 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
RNG is the first flat datacenter network deployed in production, based on quasi-random graphs with a scalable routing protocol and optical shuffling device, matching fat tree performance at up to 45% lower cost and now default at Amazon for most workloads.
Symphony detects step misalignments in ring collectives via lightweight in-network tracking and mitigates them by throttling outpacing flows with congestion signals, yielding up to 54% better communication times in Astra-Sim simulations and a Tofino2 prototype.
citing papers explorer
-
NeuroRisk: Physics-Informed Neural Optimization for Risk-Aware Traffic Engineering
NeuroRisk is a physics-informed deep unrolled optimizer for risk-aware traffic engineering that achieves small optimality gaps and 100-100000x speedup over solvers while outperforming neural baselines on throughput.
-
RNG: Flat Datacenter Networks at Scale
RNG is the first flat datacenter network deployed in production, based on quasi-random graphs with a scalable routing protocol and optical shuffling device, matching fat tree performance at up to 45% lower cost and now default at Amazon for most workloads.
-
Symphony: Taming Step Misalignments in the Network for Ring-based Collective Operations
Symphony detects step misalignments in ring collectives via lightweight in-network tracking and mitigates them by throttling outpacing flows with congestion signals, yielding up to 54% better communication times in Astra-Sim simulations and a Tofino2 prototype.