This paper develops a framework for studying the popularity dynamics of user-generated videos, presents a characterization of the popularity dynamics, and proposes a model that captures the key properties of these dynamics. We illustrate the biases that may be introduced in the analysis for some choices of the sampling technique used for collecting data; however, sampling from recently-uploaded videos provides a dataset that is seemingly unbiased. Using a dataset that tracks the views to a sample of recently-uploaded YouTube videos over the first eight months of their lifetime, we study the popularity dynamics. We find that the relative popularities of the videos within our dataset are highly non-stationary, owing primarily to large differences in the required time since upload until peak popularity is finally achieved, and secondly to popularity oscillation. We propose a model that can accurately capture the popularity dynamics of collections of recently-uploaded videos as they age, including key measures such as hot set churn statistics, and the evolution of the viewing rate and total views distributions over time.

We propose EMO, a model to evaluate Delay Tolerant Networks (DTN) and opportunistic systems, which focuses on simulating encounter events between mobile radios, rather than node locations as done in existing models and simulators. Our approach introduces a more accurate simulation of DTNs on the main system timescale (the encounter timescale), while trading off some accuracy at the bit-level, through an abstraction of radio propagation simulation. To design EMO, we extract and characterize the necessary parameters from experimental data and propose a method to generate synthetic node encounter traces based on this characterization. The output of the model is validated using hold-out cross-validation method. Our validation results indicate that EMO is able to maintain the statistical properties of experimental data over a wide range of time (simulation duration) and space (number of nodes) scales, with mean square errors of less than 3% for the main system parameters.

International audienceThis paper considers the problem of adapting the BitTorrent protocol for on-demand streaming. BitTorrent is a popular peer-to-peer file sharing protocol that efficiently accommodates a large number of requests for file downloads. Two components of the protocol, namely the rarest-first piece selection policy and the tit-for-tat algorithm for peer selection, are acknowledged to contribute toward the protocol's efficiency with respect to time to download files and its resilience to free riders. Rarest-first piece selection, however, is not suitable for on-demand streaming. In this paper, we present a new adaptive window-based piece selection policy that balances the need for piece diversity, which is provided by the rarest-first algorithm, with the necessity of in-order piece retrieval. We also show that this simple modification to the piece selection policy allows the system to be efficient with respect to utilization of available upload capacity of participating peers, and does not break the tit-for-tat incentive scheme which provides resilience to free riders