A GPU-accelerated network traffic monitoring and analysis system

Abstract

Data center networks are evolving toward the use of 40GE between access and aggregation layers, and 100GE at the core layer. With such high data rates, network traffic monitoring and analysis applications, particularly those involved in traffic scrutiny on a per-packet basis, require both enormous raw compute power and high I/O throughput. Many monitoring and analysis tools are facing extreme performance and scalability challenges as 40GE/100GE network environments emerge. Recently, GPU technology has been applied to accelerate general purpose scientific and engineering computing. The GPU architecture fits well with the features of packet-based network monitoring and analysis applications. At Fermilab, we have prototyped a GPU-accelerated architecture for network traffic capturing, monitoring, and analyzing. With a single Nvidia M2070 GPU, our system can handle 11 million+ packets per second without packet drops. In this paper, we will describe our architectural approach in developing a generic GPU-assisted packet capture and analysis capability.