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2010 IEEE 3rd International Conference on Cloud Computing
An Architecture for Distributed High Performance Video Processing in the Cloud
Miami, Florida
July 05-July 10
ISBN: 978-0-7695-4130-3
| ASCII Text | x | ||
| Rafael Pereira, Marcello Azambuja, Karin Breitman, Markus Endler, "An Architecture for Distributed High Performance Video Processing in the Cloud," 2012 IEEE Fifth International Conference on Cloud Computing, pp. 482-489, 2010 IEEE 3rd International Conference on Cloud Computing, 2010. | |||
| BibTex | x | ||
| @article{ 10.1109/CLOUD.2010.73, author = {Rafael Pereira and Marcello Azambuja and Karin Breitman and Markus Endler}, title = {An Architecture for Distributed High Performance Video Processing in the Cloud}, journal ={2012 IEEE Fifth International Conference on Cloud Computing}, volume = {0}, year = {2010}, isbn = {978-0-7695-4130-3}, pages = {482-489}, doi = {http://doi.ieeecomputersociety.org/10.1109/CLOUD.2010.73}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2012 IEEE Fifth International Conference on Cloud Computing TI - An Architecture for Distributed High Performance Video Processing in the Cloud SN - 978-0-7695-4130-3 SP482 EP489 A1 - Rafael Pereira, A1 - Marcello Azambuja, A1 - Karin Breitman, A1 - Markus Endler, PY - 2010 KW - Distributed Architectures KW - Cloud Computing KW - System Architectures KW - Service Orientation KW - Video Compression VL - 0 JA - 2012 IEEE Fifth International Conference on Cloud Computing ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CLOUD.2010.73
Video processing applications are notably data intense, time, and resource consuming. Upfront infrastructure investment is usually high, specially when dealing with applications where time-to- market is a crucial requirement, e.g., breaking news and journalism. Such infrastructures are often inefficient, because due to demand variations, resources may end up idle a good portion of the time. In this paper, we propose the Split&Merge architecture for high performance video processing, a generalization of the MapReduce paradigm that rationalizes the use of resources by exploring on demand computing. To illustrate the approach, we discuss an implementation of the Split&Merge architecture, that reduces video encoding times to fixed duration, independently of the input size of the video file, by using dynamic resource provisioning in the Cloud.
Index Terms:
Distributed Architectures, Cloud Computing, System Architectures, Service Orientation, Video Compression
Citation:
Rafael Pereira, Marcello Azambuja, Karin Breitman, Markus Endler, "An Architecture for Distributed High Performance Video Processing in the Cloud," cloud, pp.482-489, 2010 IEEE 3rd International Conference on Cloud Computing, 2010
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