A GPU-assisted NFV framework for intrusion detection system

The network function virtualization (NFV) paradigm advocates the replacement of specific-purpose hardware supporting packet processing by general-purpose ones, reducing costs and bringing more flexibility and agility to the network operation. However, this shift can degrade the network performance due to the non-optimal packet processing capabilities of the general-purpose hardware. Meanwhile, graphics processing units (GPUs) have been deployed in many data centers (DCs) due to their broad use in, e.g., machine learning (ML). These GPUs can be leveraged to accelerate the packet processing capability of virtual network functions (vNFs), but the delay introduced can be an issue for some applications. Our work proposes a framework for packet processing acceleration using GPUs to support vNF execution. We validate the proposed framework with a case study, analyzing the benefits of using GPU to support the execution of an intrusion detection system (IDS) as a vNF and evaluating the traffic intensities where using our framework brings the most benefits. Results show that the throughput of the system increases from 50 Mbps to 1 Gbps by employing our framework while reducing the central process unit (CPU) resource usage by almost 40%. The results indicate that GPUs are a good candidate for accelerating packet processing in vNFs.(1)

Automated summary

NFV advocates replacing specific-purpose hardware with general-purpose hardware to reduce costs and increase network operation flexibility. GPUs deployed in data centers can accelerate the packet processing capability of vNFs, but may introduce delays. Our framework proposes using GPUs to support vNF execution for improved throughput and reduced CPU resource usage.