Authors:
Shaiful Alam Chowdhury
and
Dwight Makaroff
Affiliation:
University of Saskatchewan, Canada
Keyword(s):
Workload Characterization, Multimedia Applications, Content Distribution, Time-series Clustering.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Metadata and Metamodeling
;
Sensor Networks
;
Signal Processing
;
Society, e-Business and e-Government
;
Soft Computing
;
Software Agents and Internet Computing
;
Web 2.0 and Social Networking Controls
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
Abstract:
Understanding the growth pattern of content popularity has become a subject of immense interest to Internet service providers, content makers and on-line advertisers. This understanding is important for the sustainable deployment of content distribution systems. A significant amount of research has been done in analyzing the popularity growth patterns of YouTube videos. Unfortunately, little work has been done that investigates the popularity patterns of YouTube videos based on video object category. In this paper, we perform an in-depth analysis of the popularity pattern of YouTube videos, considering video categories. We find that the time varying popularity of different YouTube categories are different from each other. For some categories, views at early ages can be used to predict future popularity, whereas for some other categories, predicting future popularity is a challenging task and requires more sophisticated techniques (e.g. time-series clustering). The outcomes of these a
nalyses can be instrumental towards designing a reliable workload generator, which can be further used to evaluate different caching policies and distribution mechanism for YouTube and similar sites.
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