WORKSHOP 2a

Network Machine Learning

  • Monday, 27 June 2016, 09:00-12:30, Room Aphrodite B

  • Organisers:

    • Sheng Jiang (Huawei Technologies Co. Ltd, China)
    • Panagiotis Demestichas (University of Piraeus, Greece)

 

Motivation and Background

Machine learning technologies can learn from historical data, and make predictions or decisions, rather than following strictly static program instructions. They can dynamically adapt to a changing situation and enhance their own intelligence with by learning from new data. This approach has been successful in many applications and area. It also has potential in the network technology area. It can be used to intelligently learn the various environments of networks and react to dynamic situations better than a fixed algorithm. When it becomes mature, it would be greatly accelerate the development of autonomic networking.

The primary goal of this workshop is to inspire the potential of machine learning technologies for networks. In particular, work on potential approaches that apply machine learning technologies in network control, network management, and supplying network data for upper-layer applications would be in priority. The use cases and solutions of applying machine learning mechanism in network control and management would be presented and discussed.

The secondary goal of this workshop is to raise further awareness about the ongoing research and standardization activities in the scope of the IRTF Network Machine Learning Research Group on future networks with the European and worldwide initiatives.

 

 

Structure

Session I: 9:20-10:30
  1. NMLRG Dash - 5 min/9:20 - 9:25, by co-chairs
  2. IRTF NMLRG Introduction & Standardization - 15 min/9:25 - 9:40, by Sheng Jiang
  3. Mobile network state characterization and prediction - 30 min/9:40 - 10:10, Panagiotis Demestichas
  4. Learning How to Route - 20 min/10:10 - 10:30, Albert Cabellos
Break 10:30~11:30
Session II: 11:00-12:30
  1. NML in Inria High Security Lab: overview and datasets - 30 min/11:00 - 11:30, by Jérôme François
  2. Use Cases of Applying Machine Learning Mechanism with Network Traffic - 30 min/11:30 - 12:00, by Bing Liu
  3. Panel Discussion on the Potential Standardization of Network Machine Learning - 25 min/12:00 - 12:25, by Panagiotis Demestichas, Albert Cabellos, Bing Liu, Jérôme François, Sheng Jiang
  4. Summary & NMLRG Future Activities - 5 min/12:25 -12:30, by co-chairs
 

Presentations available!