IEEE ICNP Workshop on Machine Learning in Computer Networks (NetworkML 2016)
Nov. 8, 2016, Singapore.


Machine learning has seen wild success in solving problems from various domains, ranging from classical examples such as computer vision, natural language processing, voice recognition, to recent adventures such as self-driving cars and strategic game playing (Go).

We believe machine learning has strong potential in solving problems in computer networks. Networks are becoming more and more complicated with the growing demand for cloud computing and big data. A production network usually involves a multitude of devices, runs a multitude of protocols, and supports a multitude of applications. Traditional approaches of designing, deploying, and managing protocols face significant challenges in these complex networks. Machine learning represents a different and potentially rewarding approach to solving these challenges: its data-driven nature allows it to intelligently learn the complicated network environment and dynamically adjust protocols, with little manual effort.

Research on machine learning in networks is still at an early stage. There is in general a lack of venue dedicated for discussion, promotion, and dissemination of research on machine learning in computer networks. NetworkML aims to bring together researchers and practitioners in computer networks, systems, and machine learning to engage in a lively debate on the theory and practice of using machine learning in computer networking research.


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Call for papers

NetworkML 2016 provides a venue for presenting innovative ideas to discuss future research agendas on machine learning in computer networking. We encourage the submission of work-in-progress papers in the areas of applying machine learning for network design, implementation, measurement, management, deployment, as well as implications of computer networks to machine learning algorithms. We look for submissions of previously unpublished work on topics including, but not limited to, the following:

  • Protocol design and optimization using machine learning
  • Resource allocation for shared/virtualized networks using machine learning
  • Fault-tolerant network protocols using machine learning
  • Machine learning aided network management
  • Experiences and best-practices using machine learning in operational networks
  • Security, performance, and monitoring applications using machine learning
  • Implications and challenges brought by computer networks to machine learning theory and algorithms
  • Data-driven network architecture design
  • Application-driven network architecture design
  • Data analytics for network information mining
  • Deep learning and reinforcement learning in network control
  • Learning-based network optimization

Submission Instructions

Submitted papers must be no longer than 6 pages (US letter size, 10 point font, 12 point leading, 7 inch by 9.25 inch text block) including all content and references. The sig-alternate-10pt.cls style file satisfies the formatting requirements. Compile your source with options that produce letter page size. All submissions must include names and affiliations of all authors on the title page (no anonymization). Papers must contain novel ideas and must differ significantly in content from previously published papers and papers under simultaneous submission.

Please upload your submissions to the workshop submission page.

Important dates

  • Paper submission: July 21, 2016 (23:59 HKT) July 31, 2016 (23:59 HKT) (extended)
  • Notification of decision: August 21, 2016
  • Camera-ready: September 2, 2016 (same as the ICNP main conference)


Program Co-chairs:

Program Committee: (continuously being updated)

  • Kai Chen, HKUST, HK
  • Minghua Chen, Chinese University of Hong Kong, HK
  • Yuling Chen, Cisco, USA
  • Zhitang Chen, Huawei Noah’s Ark Lab, HK
  • Zygmunt J. Haas, Cornell University
  • Sheng Jiang, Huawei
  • Guanfeng Liang, Facebook, USA
  • Fangming Liu, Huazhong University of Science and Technology, China
  • John Lui, Chinese University of Hong Kong, HK
  • Xue Liu, McGill University, Canada
  • Brian O’Connor, ON.Lab, USA
  • Pascal Poupart, University of Waterloo, Canada
  • Chen Qian, University of California Santa Cruz, USA
  • Prem Sankar, Ericsson
  • George Trimponias, Huawei Noah’s Ark Lab, HK
  • Wei Wang, HKUST, HK
  • Chuan Wu, University of Hong Kong, HK
  • Xinyu Zhang, University of Wisconsin at Madison, USA

Steering Committee:

  • Baochun Li, University of Toronto, Canada
  • John C.S. Lui, Chinese University of Hong Kong, HK
  • Henry Xu, City University of Hong Kong, HK
  • Yanhui Geng, Huawei Noah’s Ark Lab, HK