G-NET: Effective GPU Sharing in NFV Systems

Network Function Virtualization (NFV) virtualizes software network functions to offer flexibility in their design, management and deployment. Although GPUs have demonstrated their power in significantly accelerating network functions, they have not been effectively integrated into NFV systems for the following reasons. First, GPUs are severely underutilized in NFV systems with existing GPU virtualization approaches. Second, data isolation in the GPU memory is not guaranteed. Third, building an efficient network function on CPUGPU architectures demands huge development efforts.

In this paper, we propose G-NET, an NFV system with a GPU virtualization scheme that supports spatial GPU sharing, a service chain based GPU scheduler, and a scheme to guarantee data isolation in the GPU. We also develop an abstraction for building efficient network functions on G-NET, which significantly reduces development efforts. With our proposed design, G-NET enhances overall throughput by up to 70.8% and reduces the latency by up to 44.3%, in comparison with existing GPU virtualization solutions.