TupleMerge: Building Online Packet Classifiers by Omitting Bits

CSE Lecturer Candidate Seminar.
Packet classification is an important part of many networking devices, such as routers and firewalls. Software defined networks require online packet classification where classifiers receive a mixed stream of packets to classify and rules to update and both operations must be completed as efficiently as possible without knowledge of future operations. This rules out many classifiers, such as HyperCuts, HyperSplit, and their derivatives, which do not support fast updates. We build upon Tuple Space Search, the packet classifier used by Open vSwitch, to create TupleMerge. TupleMerge improves upon Tuple Space Search by combining hash tables which contain rules with similar characteristics. This greatly reduces classification time by producing fewer tables. We compared TupleMerge to PartitionSort, the current state-of-the-art online packet classifier, on rulelists generated by ClassBench. TupleMerge outperforms PartitionSort at both classifying packets and rule update. Specifically, on average, it is 34.2% faster at classifying packets and 30% faster at updating than PartitionSort.
James Daly is currently a doctoral candidate at Michigan State University. His research interests include network security, algorithm design, and computer science pedagogy. He earned his BS from Hope College in 2008.