Gen: A GPU-accelerated elastic framework for NFV

Network Function Virtualization (NFV) has the potential to enhance service delivery flexibility and reduce overall costs by provisioning software-based service function chains (SFCs) on commodity hardware. However, we observe that existing CPU-based SFC solutions cannot achieve both high performance and high elasticity simultaneously. To address such a critical challenge, we seek beyond CPU and exploit the capability of Graphics Processing Unit (GPU) to support NFV. We propose GEN, a GPU-based high performance and elastic framework for NFV. As opposed to pipeline-based SFCs in existing GPU-based NFV systems, GEN proposes to support RTC-based SFCs to improve processing performance. Meanwhile, GEN offers great elasticity of network function (NF) scaling up and down by allocating a different number of fine-grained GPU threads to an NF during runtime. We have implemented a prototype of GEN. Preliminary evaluation results demonstrate that GEN improves performance with RTC-based SFCs, and supports adaptive, precise, and fast NF scaling for NFV.