Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
IEEE/ACM Transactions on Networking (TON)
Measurement, modeling, and analysis of a peer-to-peer file-sharing workload
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Capacitated facility location problem with general setup cost
Computers and Operations Research
Video-on-Demand Equipment Allocation
NCA '06 Proceedings of the Fifth IEEE International Symposium on Network Computing and Applications
Understanding user behavior in large-scale video-on-demand systems
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
On the optimization of storage capacity allocation for content distribution
Computer Networks: The International Journal of Computer and Telecommunications Networking
Video-on-demand server selection and placement
ITC20'07 Proceedings of the 20th international teletraffic conference on Managing traffic performance in converged networks
End-to-end analysis of distributed video-on-demand systems
IEEE Transactions on Multimedia
Minimizing delivery cost in scalable streaming content distribution systems
IEEE Transactions on Multimedia
Video-on-Demand Networks: Design Approaches and Future Challenges
IEEE Network: The Magazine of Global Internetworking
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Video-on-Demand (VoD) services are very user-friendly, but also complex and resource demanding. Deployments involve careful design of many mechanisms where content attributes and usage models should be taken into account. We define, and propose a methodology to solve, the VoD Equipment Allocation Problem of determining the number and type of streaming servers with directly attached storage (VoD servers) to install at each potential location in a metropolitan area network topology such that deployment costs are minimized. We develop a cost model for VoD deployments based on streaming, storage and transport costs and train a parametric function that maps the amount of available storage to a worst-case hit ratio. We observe the impact of having to determine the amount of storage and streaming cojointly, and determine the minimum demand required to deploy replicas as well as the average hit ratio at each location. We observe that common video-on-demand server configurations lead to the installation of excessive storage, because a relatively high hit-ratio can be achieved with small amounts of storage so streaming requirements dominate.