Cloud Computing

Cloud computing offers a unique model of computation with its ability to provide “unlimited” compute resources as needed. This provides the flexibility to scale experiments based on needs. Scientific applications can benefit from cloud computing’s “pay-as-you-go” model as it can provide a mid-scale scientist with the resources he needs, in addition to his grid resources. LEAD II explores challenges for executing geo-science applications on cloud resources with the combined expertise of computer, atmospheric, and climate scientists.

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Articles in Cloud Computing:


	HTRC: Transition to Outreach Phase II
	Exploiting MapReduce and Data Compression for Data-intensive Applications
	Hierarchical MapReduce Programming Model and Scheduling Algorithms
	Beth Plale, BigData Steering Committee Chair: Call for Papers/Workshops 2013 IEEE International Conference on Big Data (IEEE BigData 2013)	The Program Committees of the 2013 IEEE International Conference on Big Data (IEEE BigData 2013) invite proposals for Workshops and high-quality original research papers.
	New Legal Mandate in Hand, Data-Sharing Advocates Tackle Cultural Obstacles	With its hands-on practical approach, the work of the Research Data Alliance may also help lay the groundwork for agreement on technical standards on an international basis, said Beth A. Plale, a professor of computer science at Indiana University at Bloomington who participated in the founding session this week in Göteborg.
	Professor Beth A. Plale co-leads U.S. involvement in new international Research Data Alliance	U.S. involvement is led by Rensselaer Polytechnic Institute Computer Science Professor Francine Berman and Professor Beth A. Plale, of the School of Informatics and Computing at Indiana University.
	PRAGMA: Building the PRAGMA Multi-Cloud	PRAGMA Students Online Seminar Series, Philip Papadopoulos, San Diego Supercomputer Center, Speaker.
	Cloud for Climate: Advancing Data & Resource Management	This project develops a pipeline framework for running ensemble simulations on the cloud; the framework has two key components: ensemble deployment and metadata harvest. Regarding the former, on commercial cloud platforms typically a much smaller number of jobs than desired can be started at any one time. An ensemble run will need to be pipelined to a cloud resource, that is, executed in well-controlled batches over a period of time.
	SI2-SSE Pipeline Framework for Ensemble Runs on the Cloud	Poster, quad chart and abstract for SI2 project meeting.
	D2I visualizes Vortex2 data set in World Wide Telescope (WWT)	Great resource for understanding the D2I-Vortex2 data set. All 173 Vortex2 records are visualized with exact coordinate information. Based on this data, the Data to Insight Center successfully executed 214 workflows, used 109,568 CPU hours, generated 215 GB of data and over 9100 2D products.