Quanta: Scaling Packet-Level Network Simulation by Exploiting Execution Redundancy

Packet-level network simulation provides high-fidelity modeling but suffers from severe scalability bottlenecks. Existing scaling approaches remain inefficient for modern data-center and AI-training networks. Spatial parallelism requires substantial hardware resources, while temporal-skipping approaches become less effective under bursty traffic. We observe that homogeneous data-center deployments introduce substantial execution redundancy during simulation. This paper presents Quanta, a redundancy-aware simulation framework that eliminates repeated execution in packet-level simulation. Quanta reduces the dependence of simulation cost on physical network scale. Our evaluation shows that Quanta accelerates large-scale simulations by up to 90 ×, synergizes with parallel execution for a 320 × combined speedup.