Title
Accelerating VNF-based Deep Packet Inspection with the use of GPUs
Abstract
Network Function Virtualization (NFV) replaces the hardware that supports packet processing in network operation from specific- by general-purpose ones, reducing costs and bringing more flexibility and agility to the network operation. However, this shift can cause performance losses due to the non-optimal packet processing capabilities of the general-purpose hardware. Moreover, supporting the line rate of optical network channels with Virtualized Network Functions (VNFs) is a challenging task. This work analyzes the benefits of using Graphics Processing Units (GPUs) to support the execution of a Deep Packet Inspection (DPI) VNF towards supporting the line rate of an optical channel. The use of GPUs in VNFs has a great potential to increase throughput, but the delay incurred might be an issue for some functions. Our simulation was performed using an Intrusion Detection Systems (IDS) which performs DPI deployed as a VNF under real-world traffic scaled to high bit rates. Results show that the packet processing speedup achieved by using GPUs can reach up to 19 times, at the expense of a higher packet delay.
Year
DOI
Venue
2018
10.1109/ICTON.2018.8473638
2018 20th International Conference on Transparent Optical Networks (ICTON)
Keywords
DocType
ISSN
Network Function Virtualization,Deep Packet Inspection,Graphics Processing Unit,Intrusion Detection System
Conference
2162-7339
ISBN
Citations 
PageRank 
978-1-5386-6606-7
1
0.38
References 
Authors
2
4
Name
Order
Citations
PageRank
Igor M. Araújo110.38
Carlos Natalino2137.26
Ádamo Lima de Santana310.38
Diego L. Cardoso435.81