Duffield receives NSF grant to explore network traffic classification

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.

Electrical and computer engineering former student named editor-in-chief of IET journal

Shiyan Hu, a former student from the Department of Electrical and Computer Engineering at Texas A&M University, was named editor-in-chief of the Institute of Technology’s (IET) newly launched journal, Cyber-Physical Systems: Theory & Application.

Cyber-Physical Systems (CPS) include smart washing machines, self-driving cars, medical devices and smart grid meters. As the digital world becomes more than handheld, researchers seek to get a better understanding of the interface between cyberspace and the tangible elements.

Hu is an expert in CPS and cybersecurity, and is director of Center for Cyber-Physical Systems at Michigan Tech Institute of Computer and Cybersystems. As founding editor, Hu will lead a team of associate editors who are leading experts worldwide, including several from Carnegie Mellon, Stanford, the University of Illinois, National Taiwan University and The University of Tokyo.

In the journal they will address the close interactions and feedback loop between cyber components (such as embedded sensing systems) and physical components (such as energy systems) in a system. The CPS research topics include smart energy systems, smart home/building/community/city, connected and autonomous vehicle systems and smart health.

Cyber-Physical Systems: Theory & Application is dedicated to all aspects of the fundamental and applied research in the design, implementation and operation of CPS systems, considering performance, energy, user experience, security, reliability, fault tolerance, flexibility and extensibility. Its scope also includes innovative big data analytics for cyber-physical systems such as large-scale analytical modeling, complex stochastic optimization, statistical machine learning, formal methods and verification and real-time intelligent control, which are all critical to the success of CPS developments.

As an elected Fellow of IET, Hu leads this journal and also chairs the IEEE Technical Committee on Cyber-Physical Systems (www.ieee-cps.org), an authoritative constituency overseeing all CPS related activities within IEEE. He has published more than 100 research papers (about 30 in the premier IEEE Transactions), received numerous awards recognizing his research impact to the field and served as associate editor or guest editor for seven IEEE/ACM Transactions.

IET is the largest engineering society in Europe with more than 180,000 members. Visit Cyber-Physical Systems: Theory & Application.

Gratz receives Association of Former Students Distinguished Achievement Award in Teaching

Dr. Paul V. Gratz, associate professor in the Department of Electrical and Computer Engineering at Texas A&M University, was awarded the 2016 Association of Former Students (AFS) Distinguished Achievement Award in Teaching — College Level. He is one of four faculty members in the college of engineering selected to receive the award.

Since 1982, the AFS teaching award has been presented to faculty members who are renowned for their expertise and exemplary dedication to the education of their students.

Dr. Miroslav Begovic, electrical and computer engineering department head, said Gratz deserves the award because he has been an early adopter of blended learning within the department and college, having restructured ECEN 350 as a blended learning class.

The restructured class features live, recorded lectures published online and online quizzes replacing traditional homework, among other enhancements. Those efforts have yielded two benefits — a two to three week increase in material covered during a semester as well as improvements in student retention from a traditionally high drop-rate class.

Gratz has also been a leader in the department’s efforts to develop a distance learning masters program. His ECEN 676 class during spring 2016 served as the pilot class for the distance learning masters program. Based on his experiences he is developing a set of distance learning training sessions for faculty.

Gratz is a member of the computer engineering and systems group. He received his Ph.D. in electrical and computer engineering from the University of Texas at Austin in 2008. His research interests include energy-efficiency, reliability and performance in processor microarchitectures, memory systems and on-chip interconnection networks.

He has received a Teaching Excellence Award from The Texas A&M University System and a Best Paper Award from the ASPLOS’09 conference.

The AFS teaching award will be formally presented to all recipients in spring 2017 at the annual college awards banquet.

Computer Engineering and Systems Group’s Faculty and Staff Award winners recognized

M. Katherine Banks, vice chancellor and dean of engineering and director of the Texas A&M Engineering Experiment Station (TEES), recognized two Faculty and Staff Award winners in the Computer Engineering and Systems Group during the 2016 Faculty and Staff Awards banquet.

Banks presented Carolyn Warzon with a Staff Excellence Award and Alex Sprintson with the William O. & Montine P. Head Memorial Research Fund Award for Contributions.

Sprintson, who joined the department in 2006, received his B.Sc. degree (summa cum laude), M.Sc. and Ph.D. degrees in electrical engineering from the Technion – Israel Institute of Technology in Haifa, Israel, in 1995, 2001 and 2003, respectively. From 2003 to 2005 he was a postdoctoral research fellow at the California Institute of Technology. His honors include the Prof. Andrew Viterbi post-doctoral fellowship, the Wolf Award for his Ph.D. studies, the Miriam and Aaron Gutwirth Fellowship for Special Excellence in Graduate Studies and numerous academic awards of excellence.

Sprintson’s research interests are in the broad area of communication networks with a focus on algorithmic and Information-theoretic aspects of networking, network coding and its applications in communication networks, and Quality of Service (QoS) routing.

Warzon, administrative coordinator for the Computer Engineering and Systems Group, joined the department in 1996. Warzon has been in the college of engineering since 1985. Other honors she has received include the 2005 Presidents Meritorious Service Award, the Dean’s Staff Achievement Award from the college of engineering, and the department’s Outstanding Staff Award.

Researchers in electrical and computer engineering receive award for brain-inspired computing

Two graduate students and their adviser in the Department of Electrical and Computer Engineering at Texas A&M University received the Honorary Mention Best Paper Award from the 2016 IEEE International Symposium on Circuits and Systems (ISCAS).

Qian Wang, Youjie Li and their thesis adviser, Dr. Peng Li, received the award for their paper titled, “Liquid state machine based pattern recognition on FPGA with firing-activity dependent power gating and approximate computing,” at ISCAS, which was held in Montreal, Canada. This award was conferred by the Neural Systems and Applications Technical Committee of IEEE Circuits and Systems (CAS) Society.

For a very long time, the human brain has been a great inspiration for building efficient intelligent systems. Nevertheless, mimicking the information processing capabilities of the brain in VLSI-based computing systems is a completely nontrivial task and entails the development of efficient processor architectures and hardware-friendly learning mechanisms. The researchers’ paper demonstrates how the liquid state machine (LSM), a biologically plausible recurrent spiking neural network model, can be used to enable brain-inspired neural processors. An LSM processor architecture with integrated on-chip learning capability has been demonstrated on the reconfigurable FPGA platform for pattern and speech recognition applications. This work also investigates novel firing activity-based low power and approximate computing techniques to boost system energy efficiency.

Wang and Li are part of Dr. Li’s research group, and have recently passed their Ph.D. and M.S. thesis defenses, respectively. Dr. Li is a professor in the department, a faculty member of the Texas A&M Institute for Neuroscience and Texas A&M Health Science Center, and an IEEE Fellow. In addition to this award, Dr. Li has received five other best paper awards from premier IEEE/ACM conferences.

ISCAS is the world’s premier networking forum of leading researchers in the highly active fields of theory, design and implementation of circuits and systems.