1st Workshop on
Harnessing the Data Revolution in Networking

Workshop co-located with ICNP 2019 @ Chicago, Illinois, USA, October 7, 2019

 

Welcome

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.

Program

  • Keynote Speeches:
    • Monisha Ghosh, NSF

      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.

    • Jennifer Yates, AT&T Labs — Research

      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.

  • Invited Talks From Industry:
    • Walter Willinger, NIKSUN

      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.

    • Anwar Walid, Bell Lab - Research Group

      Dr. Anwar Walid is a Distinguished Member of Technical Staff with the Mathematics of Networks and Communications Research Department in the Mathematical and Algorithmic Sciences Research Center at Bell Laboratories, Alcatel-Lucent , Murray Hill, NJ. He joined Bell Labs in 1991. He received the B.S. degree in Electrical Engineering from Polytechnic University of New York, and the Ph.D. degree in Electrical Engineering from Columbia University, New York. He developed theory and algorithms for resource management and QoS support for several products. He holds six patents (and five pending patents) on network feedback congestion control, VoIP, packet scheduling, admission control and information processing in IP/MPLS networks. He received multi-year research funding from DARPA on traffic engineering and routing, and network modeling and optimization. He received the Best Paper Award in ACM SIGMETRICS/IFIP Performance 1995 on statistical modeling and analysis of multi-media traffic. He contributed to the Internet Engineering Task Force (IETF), wrote RFC's and helped in creating the Traffic Engineering Working Group. He served on NSF panels and on executive and technical program committees of IEEE and MPLS conferences, and was a guest editor of IEEE JSAC. He gave invited tutorials in IEEE INFOCOM and in INFORMS Telecommunications Conferences. He was an adjunct Professor of Electrical and Computer Engineering in Polytechnic Institute of New York University (NYU). He am an elected member of Tau Beta Pi National Engineering Honor Society, Sigma Xi Scientific Research Society and IFIP Working Group 7.3. He is a Fellow of IEEE.

    • Jia Wang, AT&T Labs — Research

      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.

    • Ming Zhang, Alibaba

      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.

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  • Invited Talks from Academia:
    • Lixin Gao, U Mass

      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.

    • Nick Feamster, University of Chicago

      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.

    • Heather Zheng, University of Chicago

      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).

    • Junchen Jiang, University of Chicago

      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.

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  • Technical Session
    • Classification-assisted Query Processing for Network Telemetry
      Gioacchino Tangari, Marinos Charalambides (University College London), Daphne Tuncer (Imperial College London), George Pavlou (University College London) paper
    • When NFV Meets ANN: Rethinking Elastic Scaling for ANN-based NFs
      Menghao Zhang, Jiasong Bai, Guanyu Li, Zili Meng (Tsinghua University), Hongda Li, Hongxin Hu (Clemson University), Mingwei Xu (Tsinghua University) paper
    • Anomaly Noise Filtering with Logistic Regression and a New Method for Time Series Trend Computation for Monitoring Systems
      Qing Gao, Yuxin Lin, Limin Zhu (ANT FINANCIAL SERVICES GROUP) paper
    • A Reinforcement Learning Approach for Online Service Tree Placement in Edge Computing
      Yimeng Wang, Yongbo Li, Tian Lan (George Washington University), Nakjung Choi (Nokia Bell Labs) paper
  • The workshop attendees can also attend the reception in the evening of Monday October 7th. There will be a poster & demo session during the reception.

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:


Important Dates


Origanizing Committees

Technical Program Committee (More to join)


Submission Instructions

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

Camera-Ready Instructions

When preparing the camera-ready version of the invited paper, please follow the following instructions:

  • Revise your manuscript to address the comments of the reviewers.
  • Generate a camera-ready version for your paper, which must be PDF-formatted, 6 pages including references, follow the IEEE formatting guidelines and verify the PDF is IEEE explore compatible (see the "Paper Formatting" and "Generating and Submitting IEEE Xplore-Compatible PDF" parts of detailed instructions on https://icnp19.cs.ucr.edu/camera.html).
  • Complete the IEEE Electronic Copyright Form (eCF) (see the "Electronic Copyright Form" part of detailed instructions on https://icnp19.cs.ucr.edu/camera.html).
  • The deadline of the camera-ready version is Saturday August 31th, 2019. The camera-ready version should be submitted by sending an email to [email protected] with the paper attached.
  • Every paper must be presented by an author at the conference. Please make sure that at least one of the authors registers for the workshop, to ensure publication of the paper. The early bird cut-off date for registration of accepted papers is September 3; please do not miss it. IF you attend only the workshop, then you DO NOT need to register for the main conference. (The registration website: https://icnp19.cs.ucr.edu/registration.html)