|Online video is the killer application of the Internet. It is predicted that more than 85% of the consumer traffic on the Internet will be video-related by 2016. Yet, the future economic viability of online videos rest squarely on our ability to understand how viewers interact with video content. For instance: (i) If a video fails to start up quickly, would the viewer abandon (ii) If a video freezes in the middle, would the viewer watch fewer minutes (iii) If a video fails to load, is the viewer less likely to return to the same site (iv) if a video plays at higher quality, will the viewer watch more minutes, etc.In this talk, we outline scientific answers to these and other such questions, establishing a causal link between video performance and viewer behavior. One of the largest such studies, our work analyzes the video viewing habits of over 65 million viewers who in aggregate watched almost 367 million videos. To go beyond correlation and to establish causality, we develop a novel technique based on Quasi-Experimental Designs (QEDs). While QEDs are well known in the medical and social sciences, our work represents its first use in network performance research and is of independent interest. Our work also a played key role in the recent net neutrality debate in assessing the likely impact of a “fast-lane” on the Internet.This talk is of general interest and is accessible to a broad audience
|Ramesh K. Sitaraman is a professor of computer science at the University of Massachusetts at Amherst and is Akamaiâ€™s chief consulting scientist. His research focuses on all aspects of Internet-scale distributed systems, including algorithms, architectures, performance, energy efficiency, user behavior, and economics. As a principal architect, he helped create the Akamaiâ€™s distributed delivery networks and was an Akamai Fellow. He is best known for his role in pioneering the first major content delivery networks (CDNs) that currently deliver a significant fraction of the worldâ€™s web content, streaming videos, and online applications. He received a PhD in Computer Science from Princeton University and a B. Tech from the Indian Institute of Technology, Madras.