Dr. Nick Duffield, a professor in the Department of Electrical and Computer Engineering at Texas A&M University, was awarded a grant from the National Science Foundation (NSF) that will allow him to research network traffic classification.
Duffield, in collaboration with Dr. Minlan Yu from Yale University, received the grant, which is titled “Distributed Approximate Packet Classification.” It is funded from 2016 to 2019 with a budget of $350,000.
Network traffic classification — assigning incoming packets to classes for processing based on pattern-matching rules — is critical for many network management tasks, including performance monitoring and fault diagnosis. However, as the number of classification tasks grows, the resources required to store and apply the rules, switch memory in particular, can become scarce. Duffield’s project takes an end-to-end view of traffic classification, observing that in addition to the memory usage at switches, other cheaper resources are involved in packet processing, specifically bandwidth to transfer selected packets to the receivers and downstream receivers that run applications. Trading off resources and even classification accuracy amongst these resources can lead to a better overall performance once the needs of downstream applications are factored in.
“The big research challenge now is how to realize these benefits in large and complex communications networks, such as in data centers, which can encompass millions of servers connected by hundreds of thousands of switches,” Duffield said.
Duffield, who also has a courtesy appointment in the Department of Computer Science and Engineering and is director of the Texas A&M Engineering Big Data Initiative, received his bachelor’s degree in natural sciences in 1982 and a master’s in mathematics in 1983 from the University of Cambridge. He received his Ph.D. in mathematical physics from the University of London in 1987. His research focuses on data and network science, particularly applications of probability, statistics, algorithms and machine learning to the acquisition, management and analysis of large datasets in communications networks and beyond.
Before joining the department, Duffield worked at AT&T Labs-Research, Florham Park, New Jersey, where he held the position of distinguished member of technical staff and was an AT&T Fellow. He previously held post-doctoral and faculty positions in Dublin, Ireland, and Heidelberg, Germany.
Duffield, the author of over 150 refereed journal and conference papers and inventor of 50 U.S patents, is co-inventor of the smart sampling technologies that lie at the heart of AT&T’s scalable Traffic Analysis Service. He is specialty editor-in-chief for Big Data of the journal Frontiers in ICT and he was charter chair of the IETF working group on packet sampling. Duffield is an IEEE Fellow, an IET Fellow and serves on the board of directors of ACM SIGMETRICS. He is an associate member of the Oxford-Man Institute of Quantitative Finance. He is a Texas A&M principal investigator on the DARPA funded consortium DEDUCE: Distributed Enclave Defense Using Configurable Edges, and has received faculty research awards from Google and Intel.