Migrating e-Science Applications to the Cloud: Methodology and Evaluation - Cloud Computing with e-Science Applications - page 112

Andrikopoulos, V., T. Binz, F. Leymann, and S. Strauch (2013). How to adapt applica-
tions for the cloud environment. Computing 95(6), 493-535.
Armbrust, M., A. Fox, R. Griffith, et al. (2009). Above the Clouds: A Berkeley View of Cloud
Computing . Technical Report UCB/EECS-2009-28. Berkeley: EECS Department,
University of California, Berkeley.
Berander, P., L.-O. Damm, J. Eriksson, et al. (2005). Software Quality Attributes and
Trade-Offs . Technical report. Karlskrona, Sweden: Blekinge Institute of Technology.
Cala, J., H. Hiden, S. Woodman, and P. Watson (2013). Cloud computing for fast pre-
diction of chemical activity. Future Generation Computer Systems 29(7), 1860-1869.
Case, G., and G. Spalding (2011). ITIL Continual Service Improvement . London: TSO,
The Stationery Office.
Cate, F. (1994). The EU Data Protection Directive, information privacy, and the public
interest. Iowa Law Review 80, 431.
CMMI Product Team (2010). CMMI for Development, Version 1.3 (CMU/SEI-2010-
TR-033). Software Engineering Institute, Carnegie Mellon University. http://
www.sei.cmu.edu/library/abstracts/reports/10tr033.cfm.
Codd, E. F. (1970). A relational model of data for large shared data banks.
Communications of the ACM 13(6), 377-387.
Daniel, G. (2004). Software Quality Assurance: From Theory to Implementation . Upper
Saddle River, NJ: Pearson Education.
Deelman, E., G. Singh, M. Livny, B. Berriman, and J. Good (2008). The cost of doing
science on the cloud: the montage example. In Proceedings of the 2008 ACM/IEEE
Conference on Supercomputing , pp. 50:1-50:12. New York: IEEE Press.
De Roure, D., C. Goble, and R. Stevens (2009). The design and realisation of the My
Experiment Virtual Research Environment for Social Sharing of Workflows.
Future Generation Computer Systems 25, 561-567.
Fowler, M., et al. (2002, November). Patterns of Enterprise Application Architecture .
Boston: Addison-Wesley Professional.
Google (2013a). Google App Engine—migrating to the high replication datastore.
http://developers.google.com/appengine/docs/adminconsole/migration.
Google (2013b). Google App Engine—uploading and downloading data. http://
developers.google.com/appengine/docs/python/tools/uploadingdata?hl=en.
Google (2013c). Google Cloud SQL—importing and exporting data. http://developers.
google.com/cloud-sql/docs/import_export.
Görlach, K., M. Sonntag, D. Karastoyanova, F. Leymann, and M. Reiter (2011). Conven-
tional workflow technology for scientific simulation. In Guide to e-Science ,
pp. 323-352. New York: Springer.
Hey, A. J., S. Tansley, K. M. Tolle, et al. (2009). The Fourth Paradigm: Data-Intensive
Scientific Discovery . Redmond, WA: Microsoft Research.
Jung, H.-W., S.-G. Kim, and C.-S. Chung (2004). Measuring software product quality:
a survey of ISO/IEC 9126. IEEE Software 21(5), 88-92.
Khajeh-Hosseini, A., I. Sommerville, J. Bogaerts, and P. Teregowda (2011). Decision
support tools for cloud migration in the enterprise. In Proceedings of CLOUD'11 ,
pp. 541-548. New York: IEEE Press.
Laszewski, T., and P. Nauduri (2011). Migrating to the Cloud: Oracle Client/Server Modern-
ization . New York: Elsevier.
Louridas, P. (2010). Up in the air: moving your applications to the cloud. IEEE Software
27(4), 6-11.