Prediction-Based Dynamic Resource Allocation for Video Transcoding in Cloud Computing

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Internal Authors/Editors

Publication Details

List of Authors: Fareed Jokhio, Adnan Ashraf, Sébastien Lafond, Ivan Porres, Johan Lilius
Editors: Kilpatrick, Peter and Milligan, Peter and Stotzka, Rainer
Publication year: 2013
Journal: Parallel, Distributed and Network-Based Processing
Publisher: IEEE
Book title: Parallel, Distributed and Network-Based Processing (PDP), 2013 21st Euromicro International Conference on
Start page: 254
End page: 261
Number of pages: 8
ISBN: 978-1-4673-5321-2
eISBN: 978-0-7695-4939-2
ISSN: 1066-6192


This paper presents prediction-based dynamic resource allocation algorithms to scale video transcoding service on a given Infrastructure as a Service cloud. The proposed algorithms provide mechanisms for allocation and deallocation of virtual machines (VMs) to a cluster of video transcoding servers in a horizontal fashion. We use a two-step load prediction method, which allows proactive resource allocation with high prediction accuracy under real-time constraints. For cost-efficiency, our work supports transcoding of multiple on-demand video streams concurrently on a single VM, resulting in a reduced number of required VMs. We use video segmentation at group of pictures level, which splits video streams into smaller segments that can be transcoded independently of one another. The approach is demonstrated in a discrete-event simulation and an experimental evaluation involving two different load patterns.


cloud computing, load prediction, resource allocation, Video transcoding

Last updated on 2020-07-08 at 08:00