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SUMMARY:CIS Seminar: "Practical Machine Learning for Networked Systems"
DESCRIPTION: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. \nMy 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.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-practical-machine-learning-for-networked-systems/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
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