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.
In: Proceedings of the 2016 ACM SIGCOMM Conference, pp
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AdvNet generates adversarial network environments via ML optimization to expose performance issues, bugs, and limitations in 27 congestion control protocol implementations.
SCENIC delivers a programmable 200G SmartNIC with offloaded protocol stacks, stream compute units, and full OS transparency that matches commercial performance for custom offloads like collective communication and GPU data partitioning.
iHAC hybrid cluster architecture reduces average HTTP page response time by over 40% from five to under three seconds while lowering latency and raising throughput in Riverbed Modeler simulations.
citing papers explorer
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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.
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AdvNet: Revealing Performance Issues in Network Protocols by Generating Adversarial Environments
AdvNet generates adversarial network environments via ML optimization to expose performance issues, bugs, and limitations in 27 congestion control protocol implementations.
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SCENIC: Stream Computation-Enhanced SmartNIC
SCENIC delivers a programmable 200G SmartNIC with offloaded protocol stacks, stream compute units, and full OS transparency that matches commercial performance for custom offloads like collective communication and GPU data partitioning.
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iHAC: A Hybrid Cluster Architecture for Enhanced Performance and Resilience
iHAC hybrid cluster architecture reduces average HTTP page response time by over 40% from five to under three seconds while lowering latency and raising throughput in Riverbed Modeler simulations.