P43: Deep Packet/Flow Analysis Using GPUs
SessionPoster Reception
Authors
Event Type
ACM Student Research Competition
Poster
Reception
TimeTuesday, November 14th5:15pm -
7pm
LocationFour Seasons Ballroom
DescriptionDeep packet inspection (DPI) faces severe challenges in
high-speed networks as it requires high I/O throughputs
and intensive computations. The parallel architecture of
GPUs fits exceptionally well for per-packet traffic
processing. However, TCP data stream need to be
reconstructed in a per-flow level to deliver a
consistent content analysis. Since the flow-centric
operations are naturally anti-parallel and often require
large memory space for buffering out-of-sequence
packets, they can be problematic for GPUs. Here, we
present a highly efficient DPI framework, which includes
a purely GPU-implemented TCP flow tracking and stream
reassembly. Instead of buffering till the TCP packets
become in sequence, we process the packets in batch with
pattern matching states between consecutive batches
connected by a Aho-Corasick with a prefix-/suffix- tree
method. Evaluation shows that our code can reassemble
tens of millions of packets per second and conduct a
signature-based DPI at 55 Gbit/s using an NVIDIA K40
GPU.
