Describing and forecasting video access patterns

Abstract—Computer systems are increasingly driven by workloads that reflect large-scale social behavior, such as rapid changes in the popularity of media items like videos. Capacity planners and system designers must plan for rapid, massive changes in workloads when such social behavior is a factor. In this paper we make two contributions intended to assist in the design and provisioning of such systems. We analyze an extensive dataset consisting of the daily access counts of hundreds of thousands of YouTube videos. In this dataset, we find that there are two types of videos: those that show rapid changes in popularity, and those that are consistently popular over long time periods. We call these two types rarely-accessed and frequently-accessed videos, respectively. We observe that most of the videos in our data set clearly fall in one of these two types. For each type of video we ask two questions: first, are there relatively simple models that can describe its daily access patt...

• Type: Journal
• Year: 2011
• Where: INFOCOM
• Authors: Gonca Gürsun, Mark Crovella, Ibrahim Matta
• Keywords: Access Patterns, Communications, Daily Access, Daily Access Patterns, INFOCOM 2011