Understanding user behavior in large-scale video-on-demand systems

TLDR

Video‑on‑demand over IP is a leading next‑generation internet application, yet research has largely depended on simulations because empirical data have been scarce. The study presents a large‑scale measurement of a China Telecom VOD system over 219 days with more than 150,000 users, aiming to analyze user behavior and content access patterns to inform streaming system design, and introduces a modified Poisson distribution to better model user arrivals. The authors conducted a measurement study of a China Telecom VOD system over 219 days with more than 150,000 users and developed a modified Poisson distribution to model user arrival rates. The results show that the traditional Poisson model overestimates large arrival groups, that video session length weakly inversely correlates with popularity, and that analysis of internal and external factors yields a deeper understanding of video popularity sources.

Abstract

Video-on-demand over IP (VOD) is one of the best-known examples of "next-generation" Internet applications cited as a goal by networking and multimedia researchers. Without empirical data, researchers have generally relied on simulated models to drive their design and developmental efforts. In this paper, we present one of the first measurement studies of a large VOD system, using data covering 219 days and more than 150,000 users in a VOD system deployed by China Telecom. Our study focuses on user behavior, content access patterns, and their implications on the design of multimedia streaming systems. Our results also show that when used to model the user-arrival rate, the traditional Poisson model is conservative and overestimates the probability of large arrival groups. We introduce a modified Poisson distribution that more accurately models our observations. We also observe a surprising result, that video session lengths has a weak inverse correlation with the video's popularity. Finally, we gain better understanding of the sources of video popularity through analysis of a number of internal and external factors.