Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
MapReduce for Data Intensive Scientific Analyses
ESCIENCE '08 Proceedings of the 2008 Fourth IEEE International Conference on eScience
A Service for Data-Intensive Computations on Virtual Clusters
INTENSIVE '09 Proceedings of the 2009 First International Conference on Intensive Applications and Services
MapReduce: a flexible data processing tool
Communications of the ACM - Amir Pnueli: Ahead of His Time
Nephele: efficient parallel data processing in the cloud
Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers
All-Pairs: An Abstraction for Data-Intensive Computing on Campus Grids
IEEE Transactions on Parallel and Distributed Systems
An Architecture for Distributed High Performance Video Processing in the Cloud
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Cloud computing paradigms for pleasingly parallel biomedical applications
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
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Cloud computing enables us to create applications that take advantage of large computer infrastructures on demand. Data intensive computing frameworks leverage these technologies in order to generate and process large data sets on clusters of virtualized computers. MapReduce provides an highly scalable programming model in this context that has proven to be widely applicable for processing structured data. In this paper, we present an approach and implementation that utilizes this model for the processing of audiovisual content. The application is capable of analyzing and modifying large audiovisual files using multiple computer nodes in parallel and thereby able to dramatically reduce processing times. The paper discusses the programming model and its application to binary data. Moreover, we summarize key concepts of the implementation and provide a brief evaluation.