IEEE ICNP Workshop on Machine Learning and Artificial Intelligence in Computer Networks (ML&AI @ Network 2017)
Oct. 10, 2017, Toronto, Canada.


The increase in complexity of nowadays networks makes it difficult to effectively monitor, model, audit, and control the traffic. Therefore, there is a need for more powerful methods to solve the challenges faced in network design, deployment, and management. Machine learning (ML), as well as other artificial intelligence (AI) techniques, which have been successfully applied to various domains, including computer vision, natural language processing, and voice recognition, could have strong potential in solving problems in computer networks.

However, research on ML or AI 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. IEEE ICNP Workshop on Machine Learning and Artificial Intelligence in Computer Networks (ML&AI@Network 2017) provides an opportunity for both researchers and practitioners in computer networks, systems, and machine learning to showcase their progress of tackling network problems using machine learning. We believe ML&AI@Network 2017 will be a good forum to bring together these research areas and elicit fruitful discussions.

Call for papers

ML&AI@Network 2017 provides a venue for presenting innovative ideas to discuss future research agendas on machine learning (ML) and artificial intelligence (AI) in computer networking. We encourage the submission of work-in-progress papers in the areas of applying ML or AI for network design, implementation, measurement, management, deployment, as well as implications of computer networks to ML or AI 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 We look for submissions of previously unpublished work on topics including, but not limited to, the following: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 31th, 2017 August 7th, 2017
  • Notification of decision: August 22th, 2017 August 28th, 2017
  • Camera-ready: September 2nd, 2017


Program Co-chairs:

  • John C.S. Lui, The Chinese University of Hong Kong, Hong Kong
  • Qun Huang, Huawei Future Network Theory Lab, Hong Kong

Program Committee: (continuously being updated)

  • Kai Chen, HKUST, HK
  • Minghua Chen, Chinese University of Hong Kong, HK
  • Xin Jin, Johns Hopkins University, USA
  • Weichao Li, Huawei Future Network Theory Lab, HK
  • Fangming Liu, Huazhong University of Science and Technology, China
  • Chen Qian, University of California Santa Cruz, USA
  • Wei Wang, HKUST, HK
  • Ying Zhang, Facebook, US
  • Zhi-Li Zhang, University of Minnesota, US

Steering Committee:

  • Rocky K. C. Chang, The Hong Kong Polytechnic University, HK
  • John Crowcroft, University of Cambridge, UK
  • John C.S. Lui, The Chinese University of Hong Kong, HK
  • David Meyer, Brocade Communications, USA
  • K. K. Ramakrishnan, UC Riverside, USA
  • Nicholas Zhang, Huawei Future Network Theory Lab, HK