Understanding user behavior in large-scale video-on-demand systems
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
Measurement and analysis of a streaming-media workload
USITS'01 Proceedings of the 3rd conference on USENIX Symposium on Internet Technologies and Systems - Volume 3
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Youtube traffic characterization: a view from the edge
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
International Journal of Advanced Media and Communication
The stretched exponential distribution of internet media access patterns
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
Computer Networks: The International Journal of Computer and Telecommunications Networking
Combining replica placement and caching techniques in content distribution networks
Computer Communications
Understanding Internet Video sharing site workload: A view from data center design
Journal of Visual Communication and Image Representation
The tube over time: characterizing popularity growth of youtube videos
Proceedings of the fourth ACM international conference on Web search and data mining
Design, optimization and performance evaluation of a content distribution overlay for streaming
Computer Communications
A Case Study of Load Sharing Based on Popularity in Distributed VoD Systems
IEEE Transactions on Multimedia
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Streaming media is becoming one of the major components of Internet traffic. Therefore, a better understanding of users' video request patterns is essential, in order to design an effective and efficient video distribution system (caching, storage capacity, bandwidth, etc.). In this paper, the core issue will be the analysis and modeling of video requests temporal redundancy. The study will be centered on a News-on-Demand (NoD) service, which provides support to a wide variety of digital newspaper editions from different regions of Spain. Specifically, six digital newspapers with a high number of requests were analyzed during a period of one year. The level of redundancy has been measured by a global (gR) and a partial redundancy (pR) method, which is new in this type of services. As a result, the main contribution of our paper is a global and partial redundancy model for each digital newspaper, which would allow us to forecast the level of video requests likely to be repeated in the near future. The model turned out to be user independent and with a timeless effect. The validation process shows that all the models successfully pass the hypothesis test, which means that there were no significant differences between the model and the real data. The pR models could predict between 1% and 6% of video requests temporal redundancy with a level of accuracy which varies between 88% and 100%.