Uploaded February 2021 | Updated September 2026, 2 weeks ago
Abstract:
Networking algorithms often perform sequential decision making under uncertainty: They observe a network path and decide, e.g., how many packets to send or what to put in them. The Internet presents a particularly challenging setting: performance varies across several orders of magnitude and changes with time, control is decentralized, each node observes only a noisy sliver of the overall system, and accurate simulators do not exist.
Despite the recent progress in applying machine learning (ML) to networking research, sequential decision problems on the Internet continue to rely on hand-designed algorithms. Slow adoption of ML in these scenarios can be attributed to the requirement that control algorithms be not just performant, but also practical: robust, generalizable, real-time, and resource-efficient. Lack of research platforms for studying ML approaches in the real world exacerbates the problem.
This talk presents the platforms and algorithms we developed to achieve practical ML in the context of video streaming and congestion control. We describe Puffer, a free, publicly accessible website that live-streams television channels and operates as a randomized experiment of adaptive bitrate (ABR) algorithms. Puffer has attracted 130,000 real users and streamed 60 years of video across the Internet. Using Puffer, we developed an ML-based ABR algorithm, Fugu, that robustly outperformed existing schemes by learning in situ, on real data from its actual deployment environment.
Next, we describe Pantheon, a community "training ground" for Internet congestion-control research. It allows network researchers to benefit from and contribute to a common set of benchmark algorithms, a shared evaluation platform, and a public archive of results. Pantheon has assisted four algorithms from other research groups in publishing at NSDI 2018, ICML 2019, and SIGCOMM 2020. It also enabled our own ML-based congestion-control algorithm, Indigo, which was trained to imitate expert congestion-control algorithms we created in emulation and achieved good performance over the real Internet.
Speaker Bio:
Francis Y. Yan is a Senior Researcher at Microsoft Research and Azure for Operators, Office of the CTO. His research focuses on practical machine learning (ML) for networking, seeking to create ML algorithms that are deployable on real-world networked systems and build platforms for training and evaluating novel algorithms. He completed his Ph.D. in computer science at Stanford University, advised by Keith Winstein and Philip Levis. He has received the IRTF Applied Networking Research Prize, the USENIX NSDI Community Award, and the USENIX ATC Best Paper Award. Before his Ph.D., Francis graduated from Tsinghua University, where he received a B.S. in computer science from Yao Class and a B.A. in economics.
Abstract:
Networking algorithms often perform sequential decision making under uncertainty: They observe a network path and decide, e.g., how many packets to send or what to put in them. The Internet presents a particularly challenging setting: performance varies across several orders of magnitude and changes with time, control is decentralized, each node observes only a noisy sliver of the overall system, and accurate simulators do not exist.
Despite the recent progress in applying machine learning (ML) to networking research, sequential decision problems on the Internet continue to rely on hand-designed algorithms. Slow adoption of ML in these scenarios can be attributed to the requirement that control algorithms be not just performant, but also practical: robust, generalizable, real-time, and resource-efficient. Lack of research platforms for studying ML approaches in the real world exacerbates the problem.
This talk presents the platforms and algorithms we developed to achieve practical ML in the context of video streaming and congestion control. We describe Puffer, a free, publicly accessible website that live-streams television channels and operates as a randomized experiment of adaptive bitrate (ABR) algorithms. Puffer has attracted 130,000 real users and streamed 60 years of video across the Internet. Using Puffer, we developed an ML-based ABR algorithm, Fugu, that robustly outperformed existing schemes by learning in situ, on real data from its actual deployment environment.
Next, we describe Pantheon, a community "training ground" for Internet congestion-control research. It allows network researchers to benefit from and contribute to a common set of benchmark algorithms, a shared evaluation platform, and a public archive of results. Pantheon has assisted four algorithms from other research groups in publishing at NSDI 2018, ICML 2019, and SIGCOMM 2020. It also enabled our own ML-based congestion-control algorithm, Indigo, which was trained to imitate expert congestion-control algorithms we created in emulation and achieved good performance over the real Internet.
Speaker Bio:
Francis Y. Yan is a Senior Researcher at Microsoft Research and Azure for Operators, Office of the CTO. His research focuses on practical machine learning (ML) for networking, seeking to create ML algorithms that are deployable on real-world networked systems and build platforms for training and evaluating novel algorithms. He completed his Ph.D. in computer science at Stanford University, advised by Keith Winstein and Philip Levis. He has received the IRTF Applied Networking Research Prize, the USENIX NSDI Community Award, and the USENIX ATC Best Paper Award. Before his Ph.D., Francis graduated from Tsinghua University, where he received a B.S. in computer science from Yao Class and a B.A. in economics.
