SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Interpreting the data: Parallel analysis with Sawzall
Scientific Programming - Dynamic Grids and Worldwide Computing
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OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Pig latin: a not-so-foreign language for data processing
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Cloud Computing Boosts Business Intelligence of Telecommunication Industry
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
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Data intensive applications are widely existed, such as massive data mining, search engine and high-throughput computing in bioinformatics, etc. Data processing becomes a bottleneck as the scale keeps bombing. However, the cost of processing the large scale dataset increases dramatically in traditional relational database, because traditional technology inclines to adopt high performance computer. The boost of cloud computing brings a new solution for data processing due to the characteristics of easy scalability, robustness, large scale storage and high performance. It provides a cost effective platform to implement distributed parallel data processing algorithms. In this paper, we proposed CPLDP (Cloud based Parallel Large Data Processing System), which is an innovative MapReduce based parallel data processing system developed to satisfy the urgent requirements of large data processing. In CPLDP system, we proposed a new method called operation dependency analysis to model data processing workflow and furthermore, reorder and combine some operations when it is possible. Such optimization reduces intermediate file read and write. The performance test proves that the optimization of processing workflow can reduce the time and intermediate results.