2nd Int’l Workshop on Network Intelligence (NI 2019)
“Machine Learning for Networking”
in conjunction with IEEE INFOCOM 2019
29 April – 2 May 2019 – Paris, France
Technically Sponsored by IEEE Communications Society,
Technical Committee on Cognitive Networking,
Technical Committee on Big Data, and
IEEE Network Intelligence Emerging Technologies initiative (IEEE NI ETI)
Network Intelligence considers the embedding of Artificial Intelligence (AI) in future networks to fasten service delivery and operations, leverage Quality of Experience (QoE) and guarantee service availability, also allowing better agility, resiliency, faster customization and security. This concept inherits the solid background of autonomic networking, cognitive management, and artificial intelligence. It is envisioned as mandatory to manage, pilot and operate the forthcoming network built upon SDN, NFV and cloud.
The main goal of the Network Intelligence Workshop is to present state-of-the-art research results and experience reports in the area of network intelligence, addressing topics such as artificial intelligence techniques and models for network and service management; smart service orchestration and delivery, dynamic Service Function Chaining, Intent and policy based management, centralized vs. distributed control of SDN/NFV based networks, analytics and big data approaches, knowledge creation and decision making. This workshop offers a timely venue for researchers and industry partners to present and discuss their latest results in Network Intelligence.
The main topic of this NI 2019 edition is “Machine Learning for Networking” which puts the attention on the particular application of machine learning tools to the optimization of next generation networks. Machine and deep learning techniques become increasingly popular and achieve remarkable success nowadays in many application domains, e.g., speech recognition, bioinformatics and computer vision. Machine learning is capable to exploit the hidden relationship from voluminous input data to complicated system outputs, especially for some advanced techniques, like the deep learning. Moreover, some other techniques, e.g., reinforcement learning, could further adapt the learning results in the new environments to evolve automatically. These features perfectly match the complex, dynamic and time-varying nature of today’s networking systems.
This workshop is supported by IEEE ComSoc Emerging Technical Initiative on Network Intelligence, technically sponsored by IEEE Communications Society, Technical Committee on Cognitive Networking, and Technical Committee on Big Data.