Francis Yan (Microsoft Research): “Practical Machine Learning for Networked Systems”

Presentation Abstract:

The growing complexity and heterogeneity of networked systems have spurred a plethora of machine learning (ML) solutions, each promising a tantalizing improvement in performance. However, their path to real-world adoption is fraught with obstacles due to concerns from system operators about ML’s generalization, transparency, robustness, and efficiency.

My research takes a holistic approach to enabling practical ML for networked systems: 1) building open research platforms to lay the foundation for ML-based algorithms; 2) complementing ML with classical techniques (e.g., time-tested heuristics, control algorithms, or optimization methods) for enhanced deployability; and 3) validating ML-augmented methods through extensive empirical evidence gathered from real users or production systems. In this talk, I will demonstrate this research approach using three studies: Puffer/Fugu learns to adapt video bitrate in situ on a live streaming service we developed (with over 280,000 users to date), Autothrottle learns to assist resource management for cloud microservices, and Teal learns to accelerate traffic engineering on wide-area networks. Finally, I will conclude by outlining my research agenda for further pushing the boundaries of practical ML in networked systems.

Speaker Bio: 

Francis Y. Yan is a Senior Researcher at Microsoft Research in Redmond and the Office of the CTO, Azure for Operators. His research is primarily in networked systems, with a focus on enhancing them with practical machine learning algorithms. Francis received his Ph.D. in computer science from Stanford University, advised by Keith Winstein and Philip Levis. Before that, he completed his undergraduate studies at Tsinghua University (Yao Class) and MIT. His work has engaged hundreds of thousands of real users and also found wide use in academia, aiding researchers in publishing many papers at top-tier conferences. He is a recipient of an IRTF Applied Networking Research Prize, a USENIX NSDI Community Award, a USENIX ATC Best Paper Award, and an APNet Best Paper Award.