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UID:422@lincs.fr
DTSTART;TZID=Europe/Paris:20190116T140000
DTEND;TZID=Europe/Paris:20190116T140000
DTSTAMP:20220210T135903Z
URL:https://www.lincs.fr/events/internet-video-quality-inference-from-encr
 ypted-network-traffic/
SUMMARY:Internet Video Quality Inference from Encrypted Network Traffic
DESCRIPTION:\n Accurately monitoring application performance is becoming
 more important for Internet Service Providers (ISPs)\, as users
 increasingly expect their networks to consistently deliver acceptable
 application quality. At the same time\, the rise of end-to-end encryption
 makes it difficult for network operators to determine video stream quality
 --- including metrics such as startup delay\, resolution\, rebuffering\,
 and resolution changes --directly from the traffic stream. In this talk we
 present general methods to infer streaming video quality metrics from
 encrypted traffic using lightweight features. Our evaluation shows that our
 models are not only as accurate as previous approaches\, but they also
 generalize across multiple popular video services\, including Netflix\,
 YouTube\, Amazon Instant Video\, and Twitch. The ability of our models to
 rely on lightweight features points to promising future possibilities for
 implementing such models at a variety of network locations along the
 end-to-end network path\, from the edge to the core.Stemming from the
 outcomes of this analysis\, in the second part of the talk we present
 Network Microscope\, a novel lightweight system running at the home getaway
 that analyzes traffic generated by DASH on-demand and live video streams.
 By first separating video flows from other sources of traffic by mapping
 DNS requests\, the system tracks traffic patterns to infer key video
 quality metrics such as average bitrate and re-buffering events. Moreover\,
 the system exploits novel algorithms that use simple probing techniques\,
 i.e. lightweight pings and traceroutes\, to take advantage of the home
 network vantage point to pinpoint where potential root causes hampering the
 streaming process might be located. \n\n\n\nBio\n\n\n\n\n\nFrancesco
 Bronzino is a Research Scientist in the Network Protocol and Systems
 Research department at Bell Labs Paris-Saclay. Francesco received his Ph.D.
 in Electrical and Computer Engineering from WINLAB (Wireless Information
 Network Lab) at Rutgers University\, working on designing and developing
 name-based services for future Internet and mobile network architectures.
 For his thesis work he was awarded the "Graduate Program Academic
 Achievement Award” from the department. Before joining Bell Labs\,
 Francesco spent two years as a Post-Doctoral research fellow in the MiMove
 group at Inria\, Paris\, where worked on developing network systems aimed
 at supporting and enhancing network services from home and access networks.
 Francesco's research interests broadly focus on the Internet infrastructure
 and the services that populate it\, with interest in understanding
 systems\, protocols and new technologies that can enhance service
 development and performance.\n\n\n\n\n\n\nhttps://youtu.be/9A8XSBQYIC0\n\n
CATEGORIES:Seminars,Youtube
LOCATION:LINCS / EIT Digital\, 23 avenue d'Italie\, 75013 Paris\, France
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 Paris\, France;X-APPLE-RADIUS=100;X-TITLE=LINCS / EIT Digital:geo:0,0
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