NDLI: GPU-assisted hybrid network traffic model

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GPU-assisted hybrid network traffic model

Content Provider	ACM Digital Library
Author	Du, Zhihui Liu, Jason Liu, Yuan Li, Ting
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.
Starting Page	63
Ending Page	74
Page Count	12
File Format	PDF
ISBN	9781450327947
DOI	10.1145/2601381.2601382
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-05-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Network simulation Fluid model Gpu
Content Type	Text
Resource Type	Article

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