beat365系列講座菁英論壇第34期---Practical Machine Learning for Networked Systems

報告題目(Title)：Practical Machine Learning for Networked Systems

時間 (Date & Time)：2024.09.05 10:00 – 11:00am

地點 (Location)：燕園大廈820報告廳 Lecture Hall 820, Yanyuan Building

主講人 (Speaker)：Francis Y. Yan (Microsoft Research)

邀請人 (Host)；Chenren Xu

報告視頻會議 (Zoom Link)：ID 818 8001 1875, Password 2ZsMWj

報告摘要 (Abstract)：

The growing complexity and heterogeneity of networked systems have spurred a plethora of machine learning (ML) policies, 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) to enhance deployability; and 3) validating ML-based policies 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 360,000 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.

主講人簡介(Bio)：

Francis Y. Yan is a Senior Researcher at Microsoft Research Redmond and an incoming Assistant Professor of Computer Science at the University of Illinois at Urbana-Champaign (UIUC). His research primarily focuses on enhancing networked systems with practical ML algorithms. Francis received his Ph.D. in computer science from Stanford University and completed his undergraduate studies at Tsinghua University (Yao Class) and MIT. His work has engaged hundreds of thousands of real users and has also found wide use in academia, recognized with an IRTF Applied Networking Research Prize, a USENIX NSDI Community Award, a USENIX NSDI Outstanding Paper Award, a USENIX ATC Best Paper Award, and an APNet Best Paper Award. Francis is recruiting multiple Ph.D. students for Fall 2025 to join his research lab at UIUC (more information can be found at https://francisyyan.org).

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