In this work, we propose a regression method to predict the popularity of an online video measured by its number of views. Our method uses Support Vector Regression with Gaussian radial basis functions. We show that predicting popularity patterns with this approach provides more precise and more stable prediction results, mainly thanks to the nonlinear character of the proposed method as well as its robustness. We prove the superiority of our method against the state of the art using datasets containing almost 24 000 videos from YouTube and Facebook. We also show that using visual features, such as the outputs of deep neural networks or scene dynamics’ metrics, can be useful for popularity prediction before content publication. Furthermore, we show that popularity prediction accuracy can be improved by combining early distribution patterns with social and visual features and that social features represent a much stronger signal in terms of video popularity prediction than the visual ones.
The authors propose models for both temporal and spatial locality of reference in streams of requests arriving at Web servers. They show that simple models based on document popularity alone are insufficient for capturing either temporal or spatial locality. Instead, they rely on an equivalent, but numerical, representation of a reference stream: a stack distance trace. They show that temporal locality can be characterized by the marginal distribution of the stack distance trace, and propose models for typical distributions and compare their cache performance to the traces. They also show that spatial locality in a reference stream can be characterized using the notion of self-similarity. Self-similarity describes long-range correlations in the data set, which is a property that previous researchers have found hard to incorporate into synthetic reference strings. They show that stack distance strings appear to be strongly self-similar, and provide measurements of the degree of self-similarity in the traces. Finally, they discuss methods for generating synthetic Web traces that exhibit the properties of temporal and spatial locality measured in the data. The increasing availability of continuous-media data is provoking a significant change in Internet workloads. For example, video from news, sports, and entertainment sites, and audio from Internet broadcast radio, telephony, and peer-to-peer networks, are becoming commonplace. Compared with traditional Web workloads, multimedia objects can require significantly more storage and transmission bandwidth. As a result, performance optimizations such as streaming-media proxy caches and multicast delivery are attractive for minimizing the impact of streaming-media workloads on the Internet. However, because few studies of streaming-media workloads exist, the extent to which such mechanisms will improve performance is unclear. This paper (1) presents and analyzes a client-based streaming-media workload generated by a large organization, (2) compares media workload characteristics to traditional Web-object workloads, and (3) explores the effectiveness of performance optimizations on streaming-media workloads. To perform the study, we collected traces of streaming-media sessions initiated by clients from a large university to servers in the Internet. In the week-long trace used for this paper, we monitored and analyzed RTSP sessions from 4,786 clients accessing 23,738 distinct streaming-media objects from 866 servers. Our analysis of this trace provides a detailed characterization of streaming-media for this workload.