Magellan Final Report - Office of Science - U.S. Department of Energy

Magellan Final Report
[13] J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. In OSDI ’04,
pages 137–150, 2004.
[14] E. Dede, M. Govindaraju, D. Gunter, and L. Ramakrishnan. Riding the elephant: Managing ensembles
with hadoop. In 4th Workshop on Many-Task Computing on Grids and Supercomputers (MTAGS),
2011.
[15] E. Deelman, G. Singh, M. Livny, B. Berriman, and J. Good. The cost of doing science on the cloud: the
montage example. In Proceedings of the 2008 ACM/IEEE conference on Supercomputing, pages 1–12.
IEEE Press, 2008.
[16] J. Demmel, L. Grigori, M. F. Hoemmen, and J. Langou. Communication-optimal parallel and sequential
qr and lu factorizations. Technical Report UCB/EECS-2008-89, EECS Department, University of
California, Berkeley, Aug 2008.
[17] N. Desai, R. Bradshaw, and J. Hagedorn. System management methodologies with bcfg2. ;login;,
31(1):11–18, February 2006.
[18] N. Desai and C. Lueninghoener. Configuration Management with Bcfg2. Usenix Association, Berkeley,
CA, 2008.
[19] Amazon Elastic Block Store. http://aws.amazon.com/ebs/.
[20] Amazon Elastic Compute Cloud. http://aws.amazon.com/ec2/.
[21] J. Ekanayake, H. Li, B. Zhang, T. Gunarathne, S. Bae, J. Qiu, and G. Fox. Twister: A runtime for
iterative mapreduce. In Proceedings of the 19th ACM International Symposium on High Performance
Distributed Computing, pages 810–818. ACM, 2010.
[22] J. Ekanayake, H. Li, B. Zhang, T. Gunarathne, S.-H. Bae, J. Qiu, and G. Fox. Twister: a runtime for
iterative mapreduce. In HPDC, pages 810–818, 2010.
[23] C. Evangelinos and C. Hill. Cloud Computing for parallel Scientific HPC Applications: Feasibility of
running Coupled Atmosphere-Ocean Climate Models on Amazons EC2. ratio, 2(2.40):2–34, 2008.
[24] Z. Fadika, E. Dede, M. Govindaraju, and L. Ramakrishnan. Benchmarking mapreduce implementations
for application usage scenarios. Grid 2011: 12th IEEE/ACM International Conference on Grid
Computing, 0:1–8, 2011.
[25] Z. Fadika, E. Dede, M. Govindaraju, and L. Ramakrishnan. Mariane: Mapreduce implementation
adapted for hpc environments. Grid 2011: 12th IEEE/ACM International Conference on Grid Computing,
0:1–8, 2011.
[26] Z. Fadika and M. Govindaraju. Lemo-mr: Low overhead and elastic mapreduce implementation optimized
for memory and cpu-intensive applications. Cloud Computing Technology and Science, IEEE
International Conference on, 0:1–8, 2010.
[27] Federal Risk and Authorization Management Program (FedRAMP) Document http://www.gsa.gov/
portal/category/102371.
[28] M. Fenn, S. Goasguen, and J. Lauret. Contextualization in practice: The clemson experience. In ACAT
Proceedings, 2010.
[29] I. Foster, T. Freeman, K. Keahey, D. Scheftner, B. Sotomayor, and X. Zhang. Virtual clusters for grid
communities. In Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the
Grid, pages 513–520. Citeseer, 2006.
134