ACM SIGCOMM 2020, New York City, USA

ACM SIGCOMM 2020 Workshop on Network Meets AI & ML (NetAI 2020)

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Workshop program

  • Monday, August 10, 2020 EDT

  • 10:00 - 11:00 am EDT Opening + Keynote

  • What Deep Generative Models Can Do for You: Opportunities, Challenges, and Open Questions

    Giulia Fanti (CMU)

    • Abstract: Data-driven generative data models produce random samples from a target distribution (e.g. autoregressive models, Gaussian mixtures, etc.). Despite decades of study in multiple communities, generative models have historically struggled to capture the complex correlations found in most real data. However, recent breakthroughs in deep learning have led to a significant jump in their performance, albeit mostly in the image and video domains. In this talk, we give a primer on state-of-the-art deep generative models, and discuss three use cases where the networking community may be able to benefit from these tools: (1) synthetic data generation, (2) vulnerability testing of black-box protocols and systems, and (3) extracting qualitative insights from unstructured data. We will discuss results in each of these application domains, highlight challenges and tradeoffs, and conclude with some open questions.


      Bio: Giulia Fanti is an Assistant Professor of Electrical and Computer Engineering at Carnegie Mellon University. Her research interests relate to cooperation in the absence of trust. She is a fellow for the World Economic Forum’s Global Future Council on Cybersecurity and has received best paper awards from ACM Sigmetrics and ACM MobiHoc, as well as Faculty Research Awards from Google and JP Morgan Chase. She obtained her Ph.D. in EECS from U.C. Berkeley and her B.S. in ECE from Olin College of Engineering.


  • 11:00 am - 12:10 pm EDT Session 1: Routing

    Session chair: Xin Jin
  • SmartEntry: Mitigating Routing Update Overhead with Reinforcement Learning for Traffic Engineering

    Junjie Zhang (Fortinet); Zehua Guo (Beijing Institute of Technology); Minghao Ye, H. Jonathan Chao (New York University)

  • Neural Packet Routing

    Shihan Xiao, Haiyan Mao, Bo Wu, Wenjie Liu, Fenglin Li (Huawei)

  • DeepBGP: A Machine Learning Approach for BGP Configuration Synthesis

    Mahmoud Bahnasy, Fenglin Li (Huawei Canada); Shihan Xiao, Xiangle Cheng (Huawei Technologies Co., Ltd.)

  • 12:10 - 12:30 pm EDT Break

  • 12:30 - 1:40 pm EDT Session 2: Machine Learning and Systems

    Session chair: Behnaz Arzani
  • Is Network the Bottleneck of Distributed Training?

    Zhen Zhang (Johns Hopkins University); Chaokun Chang, Haibin Lin, Yida Wang (Amazon Web Services); Raman Arora, Xin Jin (Johns Hopkins University)

  • Challenges in Using ML for Networking Research: How to Label If You Must

    Yukhe Lavinia, Ramakrishnan Durairajan, Reza Rejaie (University of Oregon); Walter Willinger (NIKSUN, Inc.)

  • Pitfalls of data-driven networking: A case study of latent causal confounders in video streaming

    P. C. Sruthi, Sanjay Rao, Bruno Ribeiro (Purdue University)

  • 1:40 - 2:00 pm EDT Break

  • 2:00 - 3:10 pm EDT Session 3: Analytics and Optimization

    Session chair: Sangeetha Abdu Jyothi
  • SAM: Self-Attention based Deep Learning Method for Online Traffic Classification

    Guorui Xie (Tsinghua University, Peng Cheng Laboratory); Qing Li (Southern University of Science and Technology, Peng Cheng Laboratory); Yong Jiang, Tao Dai, Gengbiao Shen, Rui Li (Tsinghua University); Richard Sinnott (University of Melbourne); Shutao Xia (Tsinghua University)

  • A Deep Learning Approach for IP Hijack Detection Based on ASN Embedding

    Tal Shapira (Tel Aviv University); Yuval Shavitt (Department of Electrical Engineering, Tel Aviv University)

  • An Adaptive Tree Algorithm to Approach Collision-Free Transmission in Slotted ALOHA

    Molly Zhang, Luca de Alfaro, J.J. Garcia-Luna-Aceves (University of California, Santa Cruz)

Call for Papers

In recent years, we have witnessed: (1) development of fully programmable, protocol-independent data planes and languages for programming them; and (2) the emergence of new platforms, tools, and algorithms for Artificial Intelligence (AI) and Machine Learning (ML). These technological advancements and scientific innovations create exciting new opportunities. On the one hand, the scientific innovations in the area of AI/ML have the potential to simplify network management (monitoring as well as control). On the other hand, recent advancements in the area of networking technology have the potential to improve the performance of AI and ML systems.

AI/ML for Networking. AI & ML have been successfully applied to various perceptual domains, including computer vision, natural language processing, and voice recognition. In addition, ML techniques are showing impressive results in new domains such as medicine, finance, and astronomy, to name a few. This success in non-perceptual domains suggests that ML techniques could be successfully applied to simplify network management. For at least a decade, networking researchers, equipment vendors, and Internet service providers alike have argued for “autonomous” or “self-driving” networks, where network management and control decisions are made in real time and in an automated fashion. Yet, building such “self-driving” networks that are practically deployable has largely remained unrealized. The coupling of the programmable control of software-defined networking (SDN) with scientific innovations in AI and 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 that self-driving networks should perform automatically and autonomously. Building a self-driving network is one of the “grand challenges” of networking research today. Realizing this vision will require incorporating the collective expertise and input from the networking research community.

Networking for AI/ML. Distributed processing systems for Artificial Intelligence (AI) and Machine Learning (ML), such as Hadoop, Spark, Storm, GraphLab, TensorFlow etc., are widely used by industry. Networking is a well-known bottleneck for AI & ML systems. Though the recent technological advances, such as reconfigurable switches, programmable NICs, RDMA- over-converged Ethernet (RoCE) and GPU direct, etc., provide exciting opportunities to improve the performance of AI and ML solutions, but the ever-increasing complexity of networks composed of a heterogeneous set of targets makes effective monitoring, modeling, auditing, and overall control of network traffic difficult if not impossible. Hence there is a need for more powerful methods to solve the challenges faced in the design, deployment, and management of networks for distributed processing systems for AI and ML.

This workshop will provide a forum for networking researchers to present and share their latest research on building self-driving networks and coupling the technological advances in networking with scientific innovations in AI and ML. This workshop seeks contributions from experts in areas such as network programming, formal methods, control theory, distributed systems, machine learning, data science, data structures and algorithms, and optimization who share in the excitement of realizing the vision of self-driving networks as well as improving the performance of AI and ML solutions.

Topics of Interest

  • Design and implementation of systems for flexible and scalable network monitoring
  • Design and implementation of closed-loop systems (modular) that use monitoring to drive network control (e.g., congestion control, TE, QoE, QoS, etc.) with minimal human intervention
  • Unified programming languages/abstractions for expressing both network monitoring (streaming as well as offline) and control tasks
  • Algorithms to train learning models for inferring network attacks, device/service fingerprinting, congestion, failures, QoE metrics, etc. in (real time) at scale
  • New data structures, algorithms, network protocols, and switch architectures for storing and/or processing network monitoring data (single-site and/or distributed settings)
  • Query-planning algorithms to scale the execution of network-monitoring queries
  • Techniques to collect and analyze network data in a privacy-preserving manner
  • Learning models to capture the relationship between network events and control actions
  • Design data structures and algorithms for consistently and correctly updating the distributed states (e.g., forwarding table entries)
  • Examples of design choices informed by control-theoretic findings (e.g., hard limits, unavoidable tradeoffs)
  • New use cases for self-driving networks in DCs, WANs, IXPs, wireless networks, cloud networks, CDNs, home networks, etc.
  • Case studies demonstrating (dis)advantages of choosing AI/ML techniques for networking over more traditional ones
  • New topology, algorithms, network protocols, and switch architectures for AI/ML applications
  • Techniques to optimize distributed AI, ML and graph processing algorithms/systems with new networking options (e.g., PISA switches, SmartNICs, RDMA, NVLink, etc.)
  • Measurement and analysis of network traffic for AI & ML systems

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, excluding references, in two-column 10pt ACM format. This six-page limit does not include the reproducibility section (see below). Papers must include authors names and affiliations for single-blind peer reviewing by the PC. Authors of accepted papers are expected to present their papers at the workshop. Please submit your paper via

Research Reproducibility

As the interest in coupling networking and AI/ML is growing, we recognize that reproducible work becomes ever more important, in particular, due to the somewhat inherent lack of interpretability associated with ML methods.

  • To encourage reproducibility, authors are allowed an additional but optional page in addition to the six pages. This additional space is meant to be used to explicitly discuss the reproducibility and interpretability of their solution. In general, all authors are encouraged to consult the reproducibility checklist to verify that their submission is reproducible.

  • In NetAI, we want to encourage authors to go the extra mile and release their research artifacts. We plan to reward the reproducible work with special ACM badging. All the accepted papers at the workshop are eligible for artifacts review.

Note: While NetAI encourages reproducibility, the reviewers will not place any additional preferences, while reviewing, towards papers with a reproducibility section.

Authors Take Note

The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to TWO WEEKS prior to the first day of the conference. The official publication date affects the deadline for any patent filings related to published work.


Attendance of the workshop is by open registration and subject to the same registration fees and rules as all the other SIGCOMM 2020 workshops. The registrants of the workshop may freely attend any workshop on the same day.

Camera-ready instructions

For the final paper to be published, please refer to Camera-ready instructions for workshops.

Important Dates

  • April 24, 2020 11:59 PST

    Paper submission deadline

  • May 25, 2020 11:59 PST

    Paper acceptance notification

  • June 10, 2020 11:59 PST

    Camera-ready deadline


  • Steering Committee
  • Marco Canini


  • Jon Crowcroft

    University of Cambridge

  • Nick Feamster

    Princeton University

  • Jennifer Rexford

    Princeton University

  • Walter Willinger

    NIKSUN Inc.

  • Nicholas Zhang


  • Program Committee Chairs
  • Behnaz Arzani

    Microsoft Research

  • Xin Jin

    Johns Hopkins University

  • Program Committee Members
  • Theophilus Benson

    Brown University

  • Daniel Berger


  • Marco Canini


  • Jiasi Chen


  • Kai Chen


  • Yong Cui


  • Soudeh Ghorbani


  • Kevin Hsieh


  • Junchen Jiang

    University of Chicago

  • Sangeetha Abdu Jyothi

    UC Irvine

  • Changhoon Kim


  • Vincent Liu

    University of Pennsylvania

  • Marco Mellia

    Politecnico di Torino

  • Masoud Moshref


  • Stefan Schmid

    University of Vienna

  • Yibo Zhu


  • Ying Zhang


  • Zhi-Li Zhang

    University of Minnesota - Twin Cities

  • Noa Zilberman

    University of Oxford