An Image Processing System Based on Heterogeneous Embedded Multicore Processors

Video and Image processing applications are notably time consuming, especially for large number of small image files and huge video files. Cloud computing gives a new processing method for such requirement, but almost all of the cloud computing platforms are based on super computers. In this paper, we propose a new method for one big image file's and large number of small image files' processing based on MapReduce and give a new idea of constructing the cloud computing platform based on heterogeneous embedded multi-core processors. From the experiment results, it can be seen that the proposed method is feasible and after analysis, we find the problems of embedded cloud computing platform based on Hadoop with MapReduce, and conclude the future research direction.

[1]  Justin Talbot,et al.  Phoenix++: modular MapReduce for shared-memory systems , 2011, MapReduce '11.

[2]  Dhabaleswar K. Panda,et al.  Performance Analysis and Evaluation of InfiniBand FDR and 40GigE RoCE on HPC and Cloud Computing Systems , 2012, 2012 IEEE 20th Annual Symposium on High-Performance Interconnects.

[3]  Robert Morris,et al.  Optimizing MapReduce for Multicore Architectures , 2010 .

[4]  Jeremy Singer,et al.  Building a Java MapReduce Framework for Multi-core Architectures , 2010 .

[5]  Karin K. Breitman,et al.  An Architecture for Distributed High Performance Video Processing in the Cloud , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[6]  Mladen A. Vouk,et al.  Cloud Computing – Issues, Research and Implementations , 2008, CIT 2008.

[7]  Karthikeyan Sankaralingam,et al.  MapReduce for the Cell B.E. Architecture , 2007 .

[8]  Naga K. Govindaraju,et al.  Mars: A MapReduce Framework on graphics processors , 2008, 2008 International Conference on Parallel Architectures and Compilation Techniques (PACT).

[9]  Christian Keimel,et al.  Video quality evaluation in the cloud , 2012, 2012 19th International Packet Video Workshop (PV).

[10]  Gang Liu,et al.  Cloud transcoder: bridging the format and resolution gap between internet videos and mobile devices , 2012, NOSSDAV '12.

[11]  Luca P. Carloni,et al.  A broadband embedded computing system for MapReduce utilizing Hadoop , 2012, 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings.

[12]  Christoforos E. Kozyrakis,et al.  Evaluating MapReduce for Multi-core and Multiprocessor Systems , 2007, 2007 IEEE 13th International Symposium on High Performance Computer Architecture.

[13]  Michael Stonebraker,et al.  MapReduce: A major step backwards , 2014 .

[14]  K. Bakshi,et al.  Considerations for big data: Architecture and approach , 2012, 2012 IEEE Aerospace Conference.

[15]  Jonathan Schaeffer,et al.  Parallel Sorting by Regular Sampling , 1992, J. Parallel Distributed Comput..