GPU-assisted hybrid network traffic model

Abstract

Large-scale network simulation imposes extremely high computing demand. While parallel processing techniques allows network simulation to scale up and benefit from contemporary high-end computing platforms, multi-resolutional modeling techniques, which differentiate network traffic representations in network models, can substantially reduce the computational requirement. In this paper, we present a novel method for offloading computationally intensive bulk traffic calculations to the background onto GPU, while leaving CPU to simulate detailed network transactions in the foreground. We present a hybrid traffic model that combines the foreground packet-oriented discrete-event simulation on CPU with the background fluid-based numerical calculations on GPU. In particular, we present several optimizations to efficiently integrate packet and fluid flows in simulation with overlapping computations on CPU and GPU. These optimizations exploit the lookahead inherent to the fluid equations, and take advantage of batch runs with fix-up computation and on-demand prefetching to reduce the frequency of interactions between CPU and GPU. Experiments show that our GPU-assisted hybrid traffic model can achieve substantial performance improvement over the CPU-only approach, while still maintaining good accuracy.