Harnessing Deep Learning for Mobile Service Traffic Decomposition to Support Network Slicing
Network slicing enables operators to isolate and customize mobile network resources on a per-service basis. Provisioning resources to slices requires real-time information about the traffic demands generated by individual services, which is challenging to acquire. This talk will first present an original deep learning approach to service-level demand estimation that relies only on total traffic aggregates. Experimental results with metropolitan-scale network measurements will reveal that the proposed solution infers per-service traffic demands with 99% accuracy. The talk will then introduce a dynamic convolution operator that resolves the grid-structural data requirements of existing spatiotemporal inference models recently proposed for mobile traffic analysis. Further, it will be shown that this operator can be easily plugged into traditional LSTM architectures to deliver accurate long-term traffic predictions.
Speakers, Panelists and Moderators
ALEXIS DUQUEResearch AssociateNet AIAlexis Duque is a Research Associate at the Univesity of Edinburgh in Paul Patras's team, where is deeply involved in developing Microscope, a patent-pending technology launched by Net AI, a university spinout. Since 2015, he drives the research and innovation strategy in an IoT design house where he collaborates with academia to bridge the gap between IoT, cybersecurity, and machine learning. He received a Ph.D. in Computer Science from University of Lyon and a Master of Engineering in Telecommunication from INSA Lyon. During his Ph.D., he developed a patented technology allowing bidirectional visible light communication for IoT devices, now commercialized under the brand Kiwink. His research interests are at the crossroad of wireless communication, Internet of Things, machine learning, and cybersecurity.