MapReduce programming model for .NET-based cloud computing

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5704 LNCS, Issue , 2009, Pages 417-428

  Jin, Chao ^a   Buyya, Rajkumar ^a  

^a UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CLOUD COMPUTING; DATA CENTERS; DATA-INTENSIVE APPLICATION; LARGE-SCALE COMPUTER SYSTEMS; PERFORMANCE EVALUATION; PROGRAMMING MODELS; RUNTIME SYSTEMS; WEB SEARCHES;

ARTIFICIAL INTELLIGENCE; BIOINFORMATICS; COMPUTER SCIENCE; WORLD WIDE WEB;

SATELLITE COMMUNICATION SYSTEMS;

EID: 70350639431     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-03869-3_41     Document Type: Conference Paper

References (15)

1. McNabb, A.W., Monson, C.K., Seppi, K.D.: Parallel PSO Using MapReduce. In: Proc. of the Congress on Evolutionary Computation (2007)
2. Apache. Hadoop, http://lucene.apache.org/hadoop/
3. Jin, C., Vecchiola, C., Buyya, R.: MRPGA: An Extension of MapReduce for Parallelizing Genetic Algorithms. In: Proc. of 4th International Conference on e-Science (2008)
4. Ranger, C., Raghuraman, R., Penmetsa, A., Bradski, G., Kozyrakis, C.: Evaluating MapReduce for Multi-core and Multiprocessor Systems. In: Proc. of the 13th Intl. Symposium on High-Performance Computer Architecture (2007)
5. Patterson, D.A.: Technical perspective: the data center is the computer. Communications of the ACM 51(1), 105 (2008)
6. Gregor, D., Lumsdaine, A.: Design and Implementation of a High-Performance MPI for C# and the Common Language Infrastructure. In: Proc. of the 13th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (2008)
7. Yang, H.C., Dasdan, A., Hsiao, R.L., Stott Parker, D.: Map-Reduce-Merge: simplified relational data processing on large clusters. In: Proc. of SIGMOD (2007)
8. Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. In: Proc. of the 6th Symposium on Operating System Design and Implementation (2004)
9. Varia, J.: Cloud Architectures. White Paper of Amazon (2008), jineshvaria.s3.amazonaws.com/public/cloudarchitectures-varia.pdf
10. Isard, M., Budiu, M., Yu, Y., Birrell, A., Fetterly, D.: Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks. In: Proc. of European Conference on Computer Systems, EuroSys (2007)
11. Kruijf, M., Sankaralingam, K.: MapReduce for the Cell B.E. Architecture. TR1625, Technical Report, The University of Wisconsin-Madison (2007)
12. Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility. Future Generation Computer Systems 25(6), 599-616 (2009)
13. Bryant, R.E.: Data-Intensive Supercomputing: The Case for DISC. CMU-CS-07-128, Technical Report, Carnegie Mellon University (2007)
14. Chen, S., Schlosser, S.W.: Map-Reduce Meets Wider Varieties of Applications. IRP-TR-08-05, Technical Report, Intel Research Pittsburgh (2008)
15. Hey, T., Trefethen, A.: The data deluge: an e-Science perspective. In: Grid Computing: Making the Global Infrastructure a Reality, pp. 809-824 (2003)