《Intelligent Defined Network Telemetry - Causal Inference or Machine Learning?》

Publisher:吴兆青Release Time:2018-06-06Times Views:48

LecturerProf. Yongning Tang (Illinoi State University)

Date and time2018-Jun-8, 10:00

LocationRoom 142, Computer Bd., Jiulonghu Campus

Abstract: Managing modern networks is becoming much more complicated: supported protocols and services are increasing; hosts and network functions are moving around; bandwidth are dynamically allocated caused by SLA and QoS. Self-driving networking is a promising solution to address such complexity. The concept of self-driving is built upon several key technologies, including knowledge defined network, network telemetry, and intent-based and software defined networking. In this talk, I will cover two related topics. In the first part, I will share our recent research on Intelligent Defined Network Telemetry or IDNT.IDNT aims at providing an intelligent-defined network monitoring framework, which could be seamlessly incorporated into the conceptual knowledge plane for self-driving network. In the second part, I will discuss a related but more fundamental question: what we can and should learn from highly visible networks that empowered by several recent advances on network telemetry? I will compare the differences on the knowledge obtained from causal inference and machine learning respectively.

Bio: Yongning Tang is a Professor in the School of Information Technology at Illinois State University. His research focuses on network telemetry, network analytics, and intelligent defined networking. Dr. Tang is an ISU computing research advisory board member, and is also in the board of directors for both the International Telecommunications Education and Research Association and the Biomathematics Computing Alliance.