ACM SIGCOMM 2018, Budapest, Hungary

ACM SIGCOMM 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks (Big-DAMA 2018)

Workshop Program

  • Monday, August 20, 2018, InterContinental

  • 9:00 am - 9:30 am Opening

  • 9:30 am - 10:30 am Keynote I: Automatic Network Optimization through Machine Learning

    Speaker:  Georg Carle (TU Munich, Germany)

  • 10:30 am - 11:00 am Tea/Coffee Break

    Location: InterContinental Pre-Function Area

  • 11:00 am - 12:15 pm Session I: Machine Learning based Network Security and Anomaly Detection

  • 11:00 am - 11:25 am

    Stream-based Machine Learning for Network Security and Anomaly Detection

    Pavol Mulinka (AIT, Austria and CTU, Czech Republic), Pedro Casas (AIT, Austria)

  • 11:25 pm - 11:50 am

    Finding Anomalies in Network System Logs with Latent Variables

    Kazuki Otomo (UTokyo, Japan), Satoru Kobayashi, Kensuke Fukuda (NII, Japan), Hiroshi Esaki (UTokyo, Japan)

  • 11:50 am - 12:15 pm

    Telemetry-based Stream-learning of BGP Anomalies

    Andrian Putina, Dario Rossi, Albert Bifet (ENST, France), Steven Barth, Drew Pletcher, Cristina Precup, Patrice Nivaggioli (Cisco, France)

  • 12:40 pm - 2:00 pm Lunch Break

    Location: InterContinental Pre-Function Area

  • 2:00 pm - 2:50 pm Session II: Data Analytics and Applications

  • 2:00 pm - 2:25 pm

    NetSlicer: Automated and Traffic-Pattern Based Application Clustering in Datacenters

    Liron Schiff, Ofri Ziv (GuardiCore, Israel), Manfred Jaeger (AAU, Denmark), Stefan Schmid (AAU, Denmark and Univie, Austria)

  • 2:25 pm - 2:50 pm

    Data Analytics Service Composition and Deployment on Edge Devices

    Jianxin Zhao, Tudor Tiplea, Richard Mortier, Jon Crowcroft, Liang Wang (Cambridge, UK)

  • 3:15 pm - 3:45 pm Tea/Coffee Break

    Location: InterContinental Pre-Function Area

  • 3:45 pm - 4:35 pm Keynote II: Taming the Video Star! Real-time Video Analytics at Scale

    Speaker: Ganesh Ananthanarayanan (Microsoft, USA)

  • 4:35 pm - 5:50 pm Session III: Deep Learning and Neural Networks for Network Analysis

  • 4:35 pm - 5:00 pm

    Deep Learning IP Network Representations

    Mingda Li (UCLA, USA), Cristian Lumezanu, Bo Zong, Haifeng Chen (NEC, USA)

  • 5:00 pm - 5:25 pm

    Learning and Generating Distributed Routing Protocols Using Graph-Based Deep Learning

    Fabien Geyer, Georg Carle (TU Munich, Germany)

  • 5:25 pm - 5:50 pm

    Understanding the Modeling of Computer Network Delays using Neural Networks

    Albert Mestres, Eduard Alarcón (UPC, Spain), Yusheng Ji (NII, Japan), Albert Cabellos (UPC, Spain)

  • 5:50 pm - 6:00 pm Closing

Call for Papers

Big-DAMA 2017

Big data and machine learning are transforming the world, and the data communication networks domain is not an exception. Network operators, practitioners and researchers have at their reach today a matchless opportunity to ride on the success of the big data wave. The complexity of today networks has dramatically increased in the last few years, making it more important and challenging to design scalable network measurement and analysis techniques and tools. Critical applications such as network monitoring, network security, or dynamic network management require fast mechanisms for on-line analysis of thousands of events per second, as well as efficient techniques for off-line analysis of massive historical data. Besides characterization, making operational sense out of the ever-growing amount of network measurements is becoming a major challenge.

Despite recent major advances of big data analysis frameworks, their application to the network measurements analysis domain remains poorly understood and investigated, and most of the proposed solutions are in-house and difficult to benchmark. Furthermore, machine learning and big data analytic techniques able to characterize, detect, locate and understand complex behaviors and complex systems promise to shed light on this enormous amount of data, but smart and scalable approaches must be conceived to make them applicable to the networking practice. Last but not least, the explosion in volume and heterogeneity of data measurements generated across the entire network stack is opening the door to innovative solutions and out-of-the-box ideas to improve current networks, and many other networking applications besides monitoring and analysis are becoming more data and measurements driven than ever.

The Big-DAMA workshop seeks for novel contributions in the field of machine learning and big data analytics applied to data communication network analysis, including scalable analytic techniques and frameworks capable of collecting and analyzing both on-line streams and off-line massive datasets, network traffic traces, topological data, and performance measurements. In addition, Big-DAMA looks for novel and out-of-the-box approaches and use cases related to the application of machine learning and big data in Networking. The workshop will allow researchers and practitioners to share their experiences on designing and developing big data applications for networking, to discuss the open issues related to the application of machine learning into networking problems and to share new ideas and techniques for big data analysis in data communication networks.

Topics of Interest

We encourage both mature and positioning submissions describing systems, platforms, algorithms and applications addressing all facets of the application of machine learning and big data to the analysis of data communication networks. We are particularly interesting in disruptive and novel ideas that permit to unleash the power of machine learning and big data in the networking domain. The following is a non-exhaustive list of topics:

  • Big networking data analysis
  • Machine learning, data mining and big data analytics in networking
  • Deep learning for networking
  • Application of reinforced-learning in networking
  • Data analytics for network measurements mining
  • Stream-based machine learning for networking
  • Big data analysis frameworks for network monitoring data
  • Distributed monitoring architectures for big networking data
  • Networking-based benchmarks for big data analysis solutions
  • Learning algorithms and tools for network anomaly detection and security
  • Network anomaly diagnosis through big networking data
  • Machine learning and big data analytics for network management
  • Big networking data integrity and privacy
  • Big data analytics and visualization for traffic analysis
  • Research challenges on machine learning and big data analytics for networking
  • Collection and processing systems for large-scale topology and performance measurements

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 ACM format. 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.

Submit your paper at

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 2018 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

  • August 20, 2018


  • June 10, 2018

    Camera-ready deadline

  • May 7, 2018

    Acceptance notification

  • April 1, 2018

    Paper registration/submission deadline (extended)


Contact the Big-DAMA chairs