Traffic recognition and Long-term traffic forecasting based on AI algorithms and metadata for 5G/IMT-2020 and beyond
5G/IMT-2020 and beyond networks will require robust smart algorithms to adapt network protocols and resource management for different services in different scenarios. Artificial intelligence (AI), which is defined as any process or device that realizes its environment and takes actions that maximize the opportunities of success for some predefined goal, is a practical solution for the design of an emerging complex communications system. The recent developments in deep learning, convolutional neural networks, and reinforcement learning hold important promise for the solution of very complex problems considered intractable until now. This talk will present AI algorithms and metadata-based approach for traffic recognition and classification with very high precision and following forecasting, to ensure ultra-reliable and ultra-low latency systems. This process should behold intelligently and include the flexible scaling methods according to the changing traffic, the geographic network position, and requirements. One key features to use the metadata of flows on the data plane at the same time with the analytical application of AI/ML algorithms is located on the service level and working with the SDN/NFV network via northbound API.
Speakers, Panelists and Moderators
ARTEM VOLKOV Researcher and PhD Student of the Department of Telecommunication NetworksSt. Petersburg State University of TelecommunicationsARTEM VOLKOVResearcher and PhD Student of the Department of Telecommunication NetworksSt. Petersburg State University of TelecommunicationsArtem Volkov - Researcher and PhD Student of the Department of Telecommunication Networks. He received a Bachelor of Science degree. (2017), M.Sc. (2019) and plans to defend his PhD thesis in 2021 at the St. Petersburg State University of Telecommunications. He is the author of several scientific articles that are indexed in the Scopus & WoS databases and also co-authored the book "Software-define networking" in Russian language. He specializes in SDN networking and other services such as IoT applications on the data plane and network management applications on the service plane, taking into account new approaches of AI implementation to networks. He is also an Editor of draft recommendations in ITU-T SG 11 and SG 20.
AMMAR MUTHANNAAssociate ProfessorSt. Petersburg State University of TelecommunicationsDr. Ammar Muthanna is an Associate Professor at the Department of Telecommunication networks and Head of SDN Laboratory. He received his B.Sc. (2009), M.Sc. (2011) and as well as Ph.D. (2016) degrees from Saint - Petersburg State University of Telecommunications. 2017-2019 he worked as Postdoctoral Researcher at RUDN University. In 2012 and 2013, he took part in the Erasmus student Program with the Faculty of electrical engineering, University of Ljubljana and in 2014 he was visitor researcher at Tampere University, Finland. His area of research includes, wireless communications, 5G/6G cellular systems, IoT applications, Edge computing and software defined networking.