Pufferisaresearch projectinthe computer science department at Stanford University. Please find more detailsinthe FAQ and our research paper (USENIX NSDI '20 Community Award, IRTF Applied Networking Research Prize '21).
PufferisaStanford University research study about using machine learning to improve video-streaming algorithms: the kind of algorithms used by services such as YouTube, Netflix, and Twitch. We are trying to figure out how to teachacomputer to design new algorithms that reduce glitches and stallsinstreaming video (especially over wireless networks and those with limited capacity, such as ...
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The plots show 95% confidence intervals. Confidence intervals are particularly importantin Puffer: As discussedinthe research paper, we find that the variable and heavy-tailed nature of video streaming requires remarkably large amounts of datainorder to draw statistically significant conclusions aboutascheme’s performance.
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Nov 12, 2020 ·Pufferimplements ABR on the server, so this pause was simulated to avoid introducing BOLA-specific logicinthe server. Specifically, the authors suggested that if all objectives are negative, BOLA-BASIC "v2" should choose the chunk with highest utility rather than highest objective.
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5 days ago ·Selected date (UTC): 2026-04-22T11_2026-04-23T11 (shareapermalink) Full results: Storage bucket Retrained model: 20260422-1.tar.gz video_sent measurement: video ...
