1st Workshop on
Harnessing the Data Revolution in Networking
Workshop co-located with ICNP 2019 @ Chicago, Illinois, USA, October 7, 2019
The HDR-Nets workshop focuses on the application of the National Science Foundation’s (NSF) Harnessing the Data Revolution (HDR) Big Idea in the networking domain, particularly on the design, deployment, operation and management, and evolution of networking systems and services. This first-year workshop aims to bring together industrial practitioners and academic researchers to share ideas and visions on harnessing the latest data science and engineering technologies to solve the problems and challenges that network and service providers face today and in the near future. The workshop will feature technical sessions and invited speakers on these topics. Participants will gain in-depth understanding of the challenges in creating next-generation networking technologies, in managing increasingly large and complex networking systems, and in delivering highly reliable and high-performing networked services that meet users’ ever-rising expectations. This workshop will open the dialogue and promote participants to form collaborations in creating innovative solutions to address those challenges, as well as validating and driving technology adoption in operational environment.
Please see the main conference for the location of the workshop
Abstract: With 5G and Wi-Fi 6 deployments rolling out a natural question that arises is what are the frontiers of research in wireless networking today? This talk will outline some of the long-term research areas that are of interest for the National Science Foundation: machine learning for networking, platforms for experimental research and THz communication being a few.
Bio: Dr. Monisha Ghosh is program manager for: NSF/Intel Partnership on Machine Learning for Wireless Networking Systems (MLWiNS). She joined NSF as a Program Director in September 2017, in the Directorate of Computer & Information Science and Engineering (CISE). She manages wireless networking research within the Networking Technologies and Systems (NeTS) program at NSF. Dr. Ghosh is a Research Professor at the University of Chicago, with a joint appointment at the Argonne National Laboratories, where she conducts research on wireless technologies for the IoT, 5G cellular, next generation Wi-Fi systems and machine learning for predictive oncology. Prior to joining the University of Chicago in September 2015, she worked at Interdigital, Philips Research and Bell Laboratories, on various wireless systems such as the HDTV broadcast standard, cable standardization and on cognitive radio for the TV White Spaces. She is a Fellow of the IEEE. She received her Ph.D. in Electrical Engineering from the University of Southern California in 1991, and her B. Tech from the Indian Institute of Technology, Kharagpur (India) in 1986.
Nick Feamster is Neubauer Professor of Computer Science in the Department of
Computer Science and Director of the Center for Data and Computation at the
University of Chicago. His research focuses on network security and performance. He
received his Ph.D. in Computer Science from MIT and his S.B. and M.Eng. degrees in
Electrical Engineering and CS at MIT.
He is a Fellow of the Association for Computing Machinery (ACM), reserved for approximately the top 1% of all professionals in computer science, recognizing his work on data-driven approaches (including machine learning) to Internet security and performance.
He has extensive experience with technical and legal consulting, as a technical and testifying expert in software patent cases and cases related to Internet technologies.
In the 1990s, Feamster was one of the first software engineers at LookSmart, a directory-based Internet search engine later bought by AltaVista. His work on Internet codecs and streaming protocols in the late 1990s led to the development of one of the first real-time video transcoding algorithms, and one of the first streaming video systems to transmit live television over the Internet. In the 2000s, Feamster worked at AT&T to design and implement the Intelligent Route Control Service Point (IRSCP), a precursor to Software Defined Networking (SDN).
In 2008, he received the Presidential Early Career Award for Scientists and Engineers (PECASE) for his contributions to cybersecurity. His other honors include the Technology Review 35 "Top Young Innovators Under 35" award, a Sloan Research Fellowship, the NSF CAREER award, and award papers at SIGCOMM, NSDI, and USENIX Security.
Bio: Dr. Lixin Gao is a professor of Electrical and Computer Engineering at the University of Massachusetts at Amherst. She received a Ph.D. degree in Computer Science from the University of Massachusetts at Amherst. Her research interests include stability and scalability of Internet routing, network virtualization and cloud computing. Between May 1999 and January 2000, she was a visiting researcher at AT&T Research Labs and DIMACS. She was an Alfred P. Sloan Fellow between 2003 and 2005. She won the best paper award from IEEE INFOCOM 2010, and the test-of-time award in ACM SIGMETRICS 2010. Her paper in ACM Cloud Computing 2011 was honored with “Paper of Distinction”. She is a fellow of IEEE and was named a Fellow of the Association of Computing Machinery “for contributions to network protocols and internet routing.
Bio: Dr. Heather Zheng is the Neubauer Professor of Computer Science at University of Chicago. She received her PhD in Electrical and Computer Engineering from University of Maryland, College Park in 1999. She joined University of Chicago after spending 6 years in industry labs (Bell-Labs, NJ and Microsoft Research Asia), and 12 years at University of California at Santa Barbara. At UChicago, she co-directs the SAND Lab (Systems, Algorithms, Networking and Data) together with Prof. Ben Y. Zhao. She was selected as one of the MIT Technology Review's TR 35 (2005) for her work on Cognitive Radios; her work was featured by MIT Technology Review as one of the 10 Emerging Technologies (2006). She is a fellow of the World Technology Network, and an IEEE Fellow (class'15).
Bio: Ming Zhang is a Senior Director / Principal Engineer in Alibaba Cloud where he leads the development of automation and intelligence systems that keep Alibaba’s global datacenter networks running reliably, efficiently and at scale. In addition, he leads the networking research team which explores and builds next-generation data-center and wireless networking technologies for Alibaba. Before joining Alibaba, he was a Senior Researcher at Microsoft Research Redmond for 10 years, during which time he delivered multiple key technologies that power the massive cloud networks of Microsoft Azure. He holds 20+ US patents, and his research was featured in influential media outlets such as BBC, CNN, and MIT Tech Review. He received his Ph.D. from Princeton University in 2005 and B.S. from Nanjing University in 1999.
Bio: Jennifer Yates is an assistant vice president of inventive science at AT&T Labs. She heads the networking and service quality management research organization. Her team of researchers focuses on inventing, prototyping, and driving new technologies that enable new services, enhance customer networks, drive new levels of automation, and address cross-layer issues. Jennifer works closely with academia, internal AT&T teams, and broader industry collaborators. Her team’s cutting-edge innovations are widely deployed and used across AT&T’s global networks and the broader industry. While studying for her doctorate in electrical and electronic engineering, Jen was a semi-professional musician. She was honored with the AT&T Fellow Award in 2012, the Science and Technology Medal in 2006, the Victorian Photonics Network Achievement Award in 2004, and a Top Young Innovator by "MIT Technology Review" in 2003. Jen holds over 30 patents for her groundbreaking research in networking.
Abstract: Networking research in the era of AI/ML is experiencing a growing chasm between a select few groups of researchers in industry and the large number of academic researchers. While the former can leverage access to their global-scale production networks in their data-driven efforts to develop and evaluate new learning models, the latter not only struggle to get their hands on real-world data sets but find it almost impossible to adequately train and evaluate their learning models under realistic conditions. This talk outlines a vision for democratizing networking research in the era of AI/ML and defines a research agenda for creating a more level playing field for academic researchers.
Bio: Dr. Walter Willinger is Chief Scientist at NIKSUN. Prior to joining NIKSUN, he
worked at AT&T Labs-Research from 1996-2013 and before that at Bellcore Applied
Research from 1986-1996. He is a Fellow of ACM (2005), Fellow of IEEE (2005), AT&T
Fellow (2007), and Fellow of SIAM (2009), co-recipient of the 1995 IEEE
Communications Society W.R. Bennett Prize Paper Award and the 1996 IEEE W.R.G. Baker
Prize Award, and co-recipient of the 2005 and 2016 ACM/SIGCOMM Test-of-Time Paper
Awards. His paper "On the Self-Similar Nature of Ethernet Traffic" is featured in
"The Best of the Best - Fifty Years of Communications and Networking Research," a
2007 IEEE Communications Society book compiling the most outstanding papers
published in the field of communications and networking in the last half century.
Dr. Willinger received his Dipl. Math degree from the ETH Zurich and his M.S. and Ph.D. degrees in Operations Research and Industrial Engineering from Cornell University.
Bio: Jia Wang is currently a Lead Inventive Scientist at AT&T Labs – Research. Her research interests lie in computer networking. She works in the areas of network measurement and service performance analysis, network design and management, software define network, big data for networking. Her research projects expands from traditional IP network, routing, network security, packet classification and firewall optimization, Internet video and social networks, and more recently mobile networks and software defined networks. Jia Wang received her MS and PhD degrees in Computer Science from Cornell University in May 1999 and January 2001, respectively. She is a Fellow of IEEE and a member of ACM.
Bio: Anwar Walid is Director of Network Intelligence and Distributed Systems Research and a Distinguished Member of the Research Staff at Nokia Bell Labs (Murray Hill, N.J.). He also served at Bell Labs as Head of the Mathematics of System Research Department, and Director of University Research Partnerships. His research interests are in control and optimization of distributed systems, learning models and algorithms with applications to Internet of Things (IoT), digital health, smart transportations, cloud computing and software defined networking (SDN). He received the B.S. and M.S. from New York University in Electrical and Computer Engineering and the Ph.D. from Columbia University. He has over 20 US and international granted patents on various aspects of networking and computing. He received awards from the IEEE and ACM, including the 2019 ACM SIGCOMM Networking Systems Award for "development of a networking system that has had a significant impact on the world of computer networking", the 2017 IEEE Communications Society William R. Bennett Prize, and best paper awards including the ACM SIGMETRICS. Dr. Walid has served on the editorial boards of IEEE and ACM journals including IEEE IoT Journal - 2019 Special Issue on AI-Enabled Cognitive Communications and Networking for IoT, IEEE/ACM Transactions on Cloud Computing, IEEE Transactions on Network Science and IEEE/ACM Transactions on Networking. He served as General Chair of 2018 IEEE/ACM Conference on Connected Health (CHASE), and as Technical Program Chair of IEEE INFOCOM 2012. He is an adjunct Professor at Columbia University Electrical Engineering department. Dr. Walid is a Fellow of the IEEE and an elected member of the IFIP (International Federation for Information Processing) Working Group 7.3 and Tau Beta Pi Engineering Honor Society.
Bio: Dr. Carlee Joe-Wong received her Ph.D. (and M.A., and A.B.) from Princeton University. She primarily works on mathematical aspects of computer and information networks/systems, with an emphasis on economic and incentive considerations. She is particularly interested in applying theoretical insights to practical system deployments. At CMU, she leads the LIONS research group.
Bio: Junchen Jiang is an Assistant Professor of Computer Science at University of Chicago. He received his Ph.D. degree from Computer Science Department at Carnegie Mellon University in 2017, and his Bachelor’s degree from Tsinghua University in 2011. He visited Microsoft Research between 2017 and 2018. His research interests are in the use of machine learning in networked systems, Internet quality of experience, and edge computing. He is a recipient of Google Faculty Research Award in 2019. His doctoral dissertation, titled “Enabling Data-Driven Optimization of Quality of Experience in Internet Applications,” was among the first systematic applications of data-driven approach to improving Internet QoE, and had led to real-world deployment and impact. His dissertation won the CMU SCS Doctoral Dissertation Award and was nominated for ACM Dissertation Award.
Call for Papers
Artificial Intelligence (AI) and Machine Learning (ML) technologies have achieved remarkable success nowadays in many application domains, e.g., natural language processing, voice recognition, and computer vision. Meanwhile, the ever increasing complexity and scale of today’s networks keep posing new challenges for network measurement and analysis techniques and tools. Advances in the CPU/GPU performance and progress in ML methods—particularly using neural networks—have made ML/AI capable of shedding light on the enormous amount of operational and systems data. Therefore, AI/ML has been effectively used in many critical networking data analytic functions, such as fault isolation, intrusion detection, event correlation, log analysis, capacity planning, and design optimization, just to name a few.
Moreover, networking has recently undergone a huge transformation enabled by new models resulting from softwarization, virtualization, and cloud computing. This has led to a number of novel architectures supported by emerging technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), edge computing, IoT, and 5G. On the other hand, maturing ML techniques, such as reinforcement learning and transfer learning, can potentially serve as basis for incorporating learning into automated network control. The emergence of enhanced design coupled with the increased complexity in networking systems and protocols has fueled the need for improved network autonomy in agile infrastructures, which can be combined with AI/ML techniques to execute efficient, rapid, trustworthy management operations. For example, the coupling of the programmable control of SDN with scientific innovations in AI/ML promises unprecedented opportunities for querying high-volume and high-velocity, distributed streaming data at scale. This new technical capability can provide the necessary information to the many different network monitoring and control tasks to enable efficient automation of autonomous networks .
The above directions can be seen to collectively fall into the National Science Foundations’ (NSF) Harnessing the Data Revolution (HDR) Big Idea, a national-scale activity to enable new modes of data-driven discovery that will allow new fundamental questions to be addressed at the frontiers of science and engineering, with the focus in computer and communication networks. In this workshop, we invite submissions of high-quality original technical and survey papers, which have not been published previously, on artificial intelligence and machine learning techniques and their applications to computer and communication networks, including but not limited to following topics:
Technical Program Committee (More to join)
Submissions must be original, unpublished work, and not under consideration at another conference or journal. Submitted papers must be at most six (6) pages long, including all figures, tables, references, and appendices in two-column 10pt IEEE format. Papers must include authors’ names and affiliations for single-blind peer reviewing by the PC. All accepted papers must be presented by one of the authors. Please submit your paper via https://hdr-nets19.hotcrp.com
When preparing the camera-ready version of the invited paper, please follow the following instructions: