Doing Research in the Cloud - NIH Workshop Dennis Gannon
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A presentation at the NIH Workshop on Advanced Networking for Data-Intensive Biomedical Research. The talk covers our work with the science community on using cloud computing to enhance and improve basic research for data analysis and scientific discovery
Transcript of Doing Research in the Cloud - NIH Workshop Dennis Gannon
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Melbourne
Sydney
Brazil
• Many Examples
• The Challenge: sustainability Data
Acquisition &
modelling
Collaboration
and
visualisation
Analysis &
data mining
Dissemination
& sharing
Archiving and
preserving
• The network is fast but …• The first mile problem
• FedX net.
• Use parallel streams to upload
• Don’t try to move it all
• Manage locality • Keep the hot data local on cloud disk
• Manage the working set over time
• The rest is archival
• Let the data accrue • Stream data directly to the cloud
• The Internet of Things = the Internet of Instruments
The Windows Azure for Research program:
·Free access to Windows Azure cloud computing and storage
(submit proposals for Windows Azure Research Awards)
· Windows Azure for Research training classes (20 classes
worldwide. )
· Support and technical resources
azure4research.com.
Real-time Catastrophe Risk Management on Windows Azure
Open assembly and analysis of large sequencing data sets
Text mining for identifying disease-gene-biological relationships
Bing for Genomes –Genomic Search and Comparison
Inference of gene networks studying human cancers on the cloud
Using data science approaches to map biologic processes to clinical
outcome
Cloud Based Drug Discovery for Malaria
Towards an interactive secondary analysis of RNA sequencing data
service in Widows Azure cloud with Apache Spark framework
User-Steering Phylogenetic Workflows in the Cloud
the use of the cloud as computational platform for genomic analysis
Enabling Data Parallelism for large-scale Biomedical Ontology Matching
Analysis and interpretation of human exome sequencing for clinical
diagnosis
Alzheimer Bio Project
Data analysis services for the molecular detection of emerging
pathogens
Azure-based Text Mining Tools for Genome-wide Association Studies
Scalable Protein Sequence Similarity Search for Metagenomics
Cloud-based Platform for Genome-scale Prediction of Protein Functional
Complex Structures at Experimental Quality
Data Analytics in Bio projects
