Dan Taylor, profile picture
Uploaded byDan Taylor
PPTX, PDF351 views

Internet2 Bio IT 2016 v2

The document discusses Internet2, an advanced networking consortium that operates a 15,000 mile fiber optic network for research and education. It provides very high speed connectivity and collaboration technologies to facilitate large data sharing and frictionless research. Examples are given of life sciences projects utilizing Internet2's high-speed network for genomic research and agricultural applications involving terabytes of satellite and sensor data. The network is expanding to include cloud computing resources and supercomputing centers to enable global-scale distributed scientific computing and collaboration.

Embed presentation

Downloaded 10 times
Pushing Discovery with Internet2 Cloud to Supercomputing in Life Sciences DAN TAYLOR Director, Business Development, Internet2 BIO-IT WORLD 2016 BOSTON APRIL, 2016
2 – 8/30/ Internet2 Overview • An advanced networking consortium – Academia – Corporations – Government • Operates a best-in-class national optical network – 15,000 miles of dedicated fiber – 100G routers and optical transport systems – 8.8 Tbps capacity • For over 20 years, our mission has been to – Provide cost effective broadband and collaboration technologies to facilitate frictionless research in Big Science – broad collaboration, extremely large data sets – Create tomorrow’s networks & a platform for networking research – Engage stakeholders in • Bridging the IT/Researcher gap • Developing new technologies critical to their missions
[ 3 ] The 4th Gen Internet2 Network Internet2 Network by the numbers 17 Juniper MX960 nodes 31 Brocade and Juniper switches 49 custom colocation facilities 250+ amplification racks 15,717 miles of newly acquired dark fiber 2,400 miles of partnered capacity with Zayo Communications 8.8 Tbps of optical capacity 100 Gbps of hybrid Layer 2 and Layer 3 capacity 300+ Ciena ActiveFlex 6500 network elements
Technology • A Research Grade high speed network – optimized for “Elephant flows” • Layer 1 – secure point to point wavelength networking • Advanced Layer 2 Services – Open virtual network for Life Sciences with connectivity speeds up to 100 Gbs • SDN Network Virtualization customer trials now • Advanced Layer 3 Services – High speed IP connectivity to the world • Superior economics • Secure sharing of online research resource – federated identity management system
[ 5 ] Internet2 Members and Partners 255 Higher Education members 67 Affiliate members 41 R&E Network members 82 Industry members 65+ Int’l partners reaching over 100 Nations 93,000+ Community anchor institutions Focused on member technology needs since 1996 "The idea of being able to collaborate with anybody, anywhere, without constraint…" —Jim Bottum, CIO, Clemson University Community
6 – 8/30/ Strong international partnerships • Agreements with international networking partners offer interoperability and access • Enable collaboration between U.S. researchers and overseas counterparts in over 100 international R & E networks Community
Some of our Affiliate Members 7
[ 8 ] *Routers Stanford Computer Workstations Berkeley, Stanford Security Systems Univ of Michigan Security Systems Georgia Tech Social Media Harvard Network Caching MIT Search Stanford
[ 9 ] The Route to Innovation August 30, 2016 © 2016 Internet2 Abundant Bandwidth • Raw capacity now available on Internet2 Network a key imagination enabler • Incent disruptive use of new, advanced capabilities Software Defined Networking • Open up network layer itself to innovation • Let innovators communicate with and program the network itself • Allow developers to optimize the network for specific applications Science DMZ • Architect a special solution to allow higher-performance data flows • Include end-to-end performance monitoring server and software • Include SDN server to support programmability
Life Sciences Research Today • Sharing Big Data sets (genomic, environmental, imagery) key to basic and applied research • Reproducibility - need to capture methods as well as raw data – High variability in analytic processes and instruments – Inconsistent formats and standards • Lack of metadata & standards • Biological systems are immensely complicated and dynamic (S. Goff, CyVERSE/iPlant) • 21k human genes can make >100k proteins • >50% of genes are controlled by day-night cycles • Proteins have an average half-life of 30 hours • Several thousand metabolites are rapidly changing • Traits are environmentally and genetically controlled • Information Technology - High Performance Computing and Networking - now can explore these systems through simulation • Collaboration – Cross Domain, Cross Discipline – Distribution of systems and talent is global – Resources are public, private and academic
BIO-IT Trends in the Trenches 2015 with Chris Dagdigian Take Aways - Science is changing faster than IT funding cycle for data intensive computing environments - Forward looking 100G multi site , multi party collaborations required - Cloud adoption driven by capability vs cost - Centralized data center dead; future is distributed computing/data stores - Big pharma security challenge has been met - SDN is real and happening now; part of infrastructure automation wave - Blast radius more important than ever: DOE’s Science DMZ architecture is a solution https://youtu.be/U6i0THTxe4o http://www.slideshare.net/chrisdag/201 5-bioit-trends-from-the-frenches 2015 Bio-IT World Conference & Expo • Change • Networking • Cloud • Decentralized Collaboration • Security • Mission Networks
Change [ 12 ]
13 – 8/30/2016, © 2009 Internet2 Data Tsunami Physics Large Hadron Collider Life Sciences Next Generation Sequencers CERN Illumina
Networking [ 14 ]
15 – 8/30/20 2012: US – China 10 Gbps Link Fed Ex: 2 days Internet+ FTP: 26 hours China ‐ US 10G Link: 30 secs Dr. Lin Fang Dr. Dawei Lin Sample.fa (24GB)
NCBI/UC-Davis/BGI : First ultra high speed transfer of genomic data between China & US, June 2012 “The 10 Gigabit network connection is even faster than transferring data to most local hard drives,” said Dr. Lin [of UC, Davis]. “The use of a 10 Gigabit network connection will be groundbreaking, very much like email replacing hand delivered mail for communication. It will enable scientists in the genomics-related fields to communicate and transfer data more rapidly and conveniently, and bring the best minds together to better explore the mysteries of life science.” (BGI press release) Life Sciences Engagement 16 Community
Forward Looking 100G Networks & Multi Site Multi Party Collaboration Accelerating Discovery: USDA ARS Science Network 8/30/2016, © 2016 Internet2
[ 18 ] USDA Agriculture Research Services Science Network • USDA scope is far beyond human
USDA Agricultural Research Services Use Cases • Drought (Soil Moisture) Project – Challenging Volumes of Data – NASA satellite data storage - 7 TB/mo., 36mo mission – ARS Hydrology and Remote Sensing Lab analysis - 108 TB – Data completely re-process 3 to 5 times • Microbial Genomics Project – Computational Bottlenecks – Individual Strains of bacteria and microorganism communities related to Food Safety Animal Health Feed Efficiency
[ 20 ] ARS Big Data Initiative Big Data Workshop Recommendations, (February 2013) Three Pillars of the ARS Big Data Implementation Plan – Network, HPC, Virtual Research Support (April, 2014) • Develop a Science DMZ • Enable high-speed, low-latency transfer of research data to HPC and storage from ARS locations • Virtual Researcher Support Implementation Complete (Nov. 2015) Clay Center, NE; Albany, CA; Beltsville Labs/Nat’l Ag. Library, Beltsville, MD Stoneville, MS; Ft. Collins, CO Ames/NADC, IA • ARS Scientific Computing Assessment • Final Report March 2014
SCInet Locations and Gateways USDA AGRICULTURAL RESEARCH SERVICE Albany, CA Ft. Collins, CO Clay Center, NE Ames, IA Stoneville, MS Beltsville, MD 100 Gb 100 Gb 100 Gb 10 Gb 10 Gb10 Gb
Cloud & Distributed Research Computing @Scale [ 22 ] Community Internet2 Approach : Agile scaling of resources and capacity Access to multi-domain, multi-discipline expertise in one dynamic global community Offer a bottomless toolbox for Innovation for the researcher
[ 23 ] New High Speed Cloud Collaborations 8/30/20 16 23 10, x10G, x100G
Syngenta Science Network Bringing Plant Potential to Life through enhanced computing capacity
Syngenta Science Network • Syngenta is a leading agriculture company helping to improve global food security by enabling millions of farmers to make better use of available resources. • Key research challenge: How to grow plants more efficiently? • Internet2 members, especially land grant universities, are important research partners.
The Challenge – Increasing size of scientific data sets – Growing number of useful external resources and partners – Complexity of genomic analyses is increasing – Need for big data collaborations across the globe – Must Innovate
– Higher data throughput – High speed connectivity to AWS Direct Connect Surge HPC Collaborations with academic community – High speed connections to best-in-class supercomputing resources NCSA – University of Illinois Leverage NCSA expertise in building custom R&D workflows Leverage NCSA Industry Partnership Program A*Star Supercomputing Center in Singapore Supports a global, distributed, scientific computing capability – Global scale : creating a global fabric for computing and collaboration
“I want to be 15 minutes behind NCSA and 6 months ahead of my competition” - Keith Gray, BP [ 28 ] National Center for Supercomputing Applications
[ 29 ] *Better Designed* *More Durable* *Available Sooner* Theoretical & Basic Research Prototyping & Development Optimization & Robustification Commercialization
[ 30 ] NCSA Mayo Clinic @Scale Genome-Wide Association Study for Alzheimer’s disease • NCSA Private Sector Program – UIUC HPCBio – Mayo Clinic • BlueWatersteam and Swiss Institute of Bioinformatics worked together to identify which genetic variants interact to influence gene expression patterns that may associate with Alzheimer’s disease
[ 31 ] Big Data and Big Compute Problem • 50,011,495,056 pairs of variants • Each variant pair is tested against 181 subjects and 24,544 genic regions • Computationally large problem, PLINK: ~ 2 years at Mayo FastEpistasis: ~ 6 hours on BlueWaters • Can be a big data problem: - 500 PB if keep all results - 4 TB when using a conservative cutoff
San Diego Supercomputing Center [ 32
UCSC Cancer Genomics Hub: Large Data Flows to End Users 1G 8G 15G Cumulative TBs of CGH Files Downloaded Data Source: David Haussler, Brad Smith, UCSC; Larry Smarr, CalIT2 30 PB http://blogs.nature.com/news/2012/05/us-cancer-genome-repository-hopes-to-speed-research.html
[ 34 ] SDSC Protein Data Base Archive • Repository of atomic coordinates and other information describing proteins and other important biological macromolecules. Structural biologists use methods such as X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy to determine the location of each atom relative to each other in the molecule. Information is annotated and publicly released into the archive by the wwPDB.
SDSC • Expertise – Bioinformatics programming and applications support. – Computational chemistry methods. – Compliance requirements, e.g., for dbGaP, FISMA and HIPAA. – Data mining techniques, machine learning and predictive analytics – HPC and storage system architecture and design. – Scientific workflow systems and informatics pipelines. • Education and Training – Intensive Boot camps for working professionals - Data Mining, Graph Analytics, and Bioinformatics and Scientific Worflows. – Customized, on-site training sessions/programs. – Data Science Certificate program. – “Hackathon” events in data science and other topics.
8/30/20 16 Sherlock Cloud: A HIPAA-Compliant Cloud Healthcare IT Managed Services - SDSC Center of Excellence 36 • Expertise in Systems, Cyber Security, Data Management, Analytics, Application Development, Advanced User Support and Project Management • Operating the first & largest FISMA Data Warehouse platform for Medicaid fraud, waste and abuse analysis • Leveraged FISMA experience to offer HIPAA- Compliant managed hosting for UC and academia • Supporting HHS CMS, NIH, UCOP and other UC Campuses • Sherlock services : Data Lab, Analytics, Case Management and Compliant Cloud
Lawrence Livermore National Lab [ 37 ]
38 – 8/30/2016, © Internet2 Lawrence Livermore NL HPC Innovation Center Cardioid Electrophysiology human heart simulations allowing exploration of causes of • Arrhythmia • Sudden cardiac arrest • Predictive drug interactions. Depicts activation of each heart muscle cell and the cell-to-cell transfer of the voltage of up to 3 billion cells - in near-real time. Metagenomic analysis with Catalyst: • Comparing short genetic fragments in a query dataset against a large searchable index (14 million genomes - 3x larger than those currently in use) of genomes to determine the threat an organism poses to human health
Community Data Science Resources renci RADII and GWU HIVE Driving Infrastructure Virtualization Enabling Reproducibility For FDA Submissions [ 39 ]
RADII Resource Aware Datacentric collaboratIve Infrastructure Goal Make data-driven collaborations a ‘turn-key’ experience for domain researchers and a ‘commodity’ for the science community Approach A new cyber-infrastructure to manage data-centric collaborations based upon natural models of collaborations that occur among scientists. RENCI: Claris Castillo, Fan Jiang, Charles Schmidt, Paul Ruth, Anirban Mandal ,Shu Huang, Yufeng Xin, Ilya Baldin, Arcot Rajasekar SDSC: Amit Majumdar DUKE: Erich Huang Workflows - especially data-driven workflows and workflow ensembles - are becoming a centerpiece of modern computational science.
RADII Rationale • Multi-institutional research teams grapple with multitude of resources – Policy-restricted large data sets – Campus compute resources – National compute resources – Instruments that produce data • Interconnected by networks – Campus, regional, national providers • Many options, much complexity • Data and infrastructure are treated separately RADII Creates A cyberinfrastructure that integrates data and resource management from the ground up to support data-centric research. RADII allows scientists to easily map collaborative data-driven activities onto a dynamically configurable cloud infrastructure.
Infrastructure management have no visibility into data resources Data management solutions have no visibility into the infrastructure RADII: Foundational technologies Data-grids present distributed data under a one single abstraction and authorization layer Networked Infrastructure as a Service (NIaaS) for rapid deployment of programmable network virtual infrastructure (clouds). Disjoint solutions Incompatible resource abstractions Gap to reduce the data-infrastructure management gap
RADII System – Virtualizing Data, Compute and Network for Collaboration 43 Novel mechanisms to represent data-centric collaborations using DFD formalism Data-centric resource management mechanisms for provisioning and de- provisioning resources dynamically through out the lifecycle of collaborations Novel mechanisms to map data processes, computations, storage and organization entities onto infrastructure
FDA and George Washington University Big Data Decisions: Linking Regulatory and Industry Organizations with HIVE Bio-Compute Objects [ 44 ] Presented by: Dan Taylor, Internet 2 | Bio IT | Boston | 2016
EI H V From Jan 2016: Vahan Simonyan, Raja Mazumder lecture NIH: Frontiers in Data Science Series https://videocast.nih.gov/summary.asp?Live=18299&bhcp=1 High-performance Integrated Virtual Environment A regulatory NGS data analysis platform
BIG DATA – From a range of samples and instruments to approval for use analysis and review sample archival sequencing run file transfer regulation computation pipelines produced files are massive in size transfer is slow too large to keep forever; not standardized difficult to validate difficult to visualize and interpret how do we avoid mistakes? NGS lifecycle: from a biological sample to biomedical research and regulation
• Data Size: petabyte scale, soon exa-bytes • Data Transfer: too slow over existing networks • Data Archival: retaining consistent datasets across many years of mandated evidence maintenance is difficult • Data Standards: floating standards, multiplicity of formats, inadequate communication protocols • Data Complexity: sophisticated IT framework needed for complex dataflow • Data Privacy: constrictive legal framework and ownership issues across the board from the patient bedside to the FDA regulation • Data Security: large number of complicated security rules and data protection tax IT subsystems and cripple performance • Computation Size: distributed computing, inefficiently parallelized, requires large investment of hardware, software and human-ware • Computation Standards: non canonical computation protocols, difficult to compare, reproduce, rely on computations • Computation Complexity: significant investment of time and efforts to learn appropriate skills and avoid pitfalls in complex computational pipelines • Interpretation: large outputs from enormous computations are difficult to visualize and summarize • Publication: peer review and audit requires communication by massive amount of information ... and how do we avoid mistakes ? software challenges and needs
HIVE is an End to End Solution • Data retrieval from anywhere in the world • Storage of extra large scale data • Security approved by OIM • Integrator platform to bring different data and analytics together • Tailor made analytics designed around needs • Visualization made to help in interpretation of data • Support of the entire hard-, soft-ware and knowledge infrastructure • Expertise accumulated in the agency • Bio-Compute objects repository to provide reproducibility and interoperability and long term referable storage of computations and results HIVE is not • an application to perform few tasks • yet another database • a computer cluster or a cloud or a data center • an IT subsystem More: http://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm491 893.htm
Instantiation DataTypeDefinitions Definitions of metadata types Data Typing Engine Definitions of computations metadata Data Bio-compute Definitions of algorithms and pipeline descriptions Computational protocols Verifiable results within acceptable uncertainty/er ror Scientifically reliable interpretation HIVE data universe
industry FDA regulatory analysis 2. compute 3. submit 1. data- forming 6. issues resubmits 5. regulatory decision 4. SOPP/prot ocols consumer $ millions of dollars 7. yes 7. no regulatory iterations ~$800 Million R&D dollars for a single drug ~$2.6 Billion total cost
industr y FD A HIVE public-HIVEGalaxy CLC DNA-nexus 2. compute 3. submit1. data-forming 6. issues resubmits 5. bio- compute 2. HIVE SOPP/protocols 4. SOPP/prot ocols consumer 7. yes 7 .no 4. submit bio-compute integration 3. compute Facilitate integration $ millions of dollars bio-compute as a way to link regulatory and industry organizations
Federated Identity [ 52 ]
[ 53 ] Community-developed framework of trust enables: • Secure, streamlined sharing of protected resources • Consolidated management of user identities and access • Delivery of an integrated portfolio of community-developed solutions [ 53 ] Trusted Identity in Research The standard for over 600 higher education institutions—and counting!
[ 54 ] 15 425+ 2 160+ 0 2000+ 7.8 million Academic Participants Sponsored Partners Registered Service Providers Individuals served by federated IdM Foundation for Trust & Identity 54 ®
• Eric Boyd, Internet2 • Stephen Wolff, Internet2 • Stephen Goff, PhD, CyVERSE/iPlant, University of Arizona • Chris Dagdigian, BioTeam • Daiwei Lin, PhD, NIAID, NIH • Paul Gibson, USDA ARS • Paul Travis, Syngenta • Evan Burness, NCSA • Sandeep Chandra, SDSC • Jonathan Allen, PhD, Lawrence Livermore National Lab • Claris Castillo, PhD, RENCI • Vahan Simonyan, PhD, FDA • Raja Mazumder, PhD, George Washington University • Eli Dart, ESNET, US Department of Energy • BGI • Nature [ 55 ] Acknowledgements
Thank you! Daniel Taylor, Director, Business Development Internet2 dbt3@internet2.edu 703-517-2566
Back up slides Science DMZ [ 57 ]
[ 58 ] Rising expectations Network throughput required to move y bytes in x time. (US Dept of Energy - http://fasterdata.es.net). should be easy This year
3/30/16, © 2016 Internet2 Science DMZ* and perfSONAR Design pattern to address the most common bottlenecks to moving data * fasterdata.es.net 59

More Related Content

PPTX
Toward a National Research Platform
byLarry Smarr
 
PPT
100503 bioinfo instsymp
byNick Jones
 
PPT
UK e-Infrastructure: Widening Access, Increasing Participation
byNeil Chue Hong
 
PDF
The pulse of cloud computing with bioinformatics as an example
byEnis Afgan
 
PPT
BeSTGRID OpenGridForum 29 GIN session
byNick Jones
 
PPTX
Toward a Global Research Platform for Big Data Analysis
byLarry Smarr
 
PPTX
2016 09 cxo forum
byChris Dwan
 
PPT
The Pacific Research Platform: a Science-Driven Big-Data Freeway System
byLarry Smarr
 
Toward a National Research Platform
byLarry Smarr
 
100503 bioinfo instsymp
byNick Jones
 
UK e-Infrastructure: Widening Access, Increasing Participation
byNeil Chue Hong
 
The pulse of cloud computing with bioinformatics as an example
byEnis Afgan
 
BeSTGRID OpenGridForum 29 GIN session
byNick Jones
 
Toward a Global Research Platform for Big Data Analysis
byLarry Smarr
 
2016 09 cxo forum
byChris Dwan
 
The Pacific Research Platform: a Science-Driven Big-Data Freeway System
byLarry Smarr
 

What's hot

PPTX
The Pacific Research Platform: Building a Distributed Big-Data Machine-Learni...
byLarry Smarr
 
PPT
The Pacific Research Platform
byLarry Smarr
 
PPTX
Advanced Global-Scale Networking Supporting Data-Intensive Artificial Intelli...
byLarry Smarr
 
PPTX
The Pacific Research Platform
byLarry Smarr
 
PPT
Advanced Cyberinfrastructure Enabled Services and Applications in 2021
byLarry Smarr
 
PPTX
The Pacific Research Platform:a Science-Driven Big-Data Freeway System
byLarry Smarr
 
PPTX
The Pacific Research Platform
byLarry Smarr
 
PPT
UC-Wide Cyberinfrastructure for Data-Intensive Research
byLarry Smarr
 
PPTX
Building the Pacific Research Platform: Supernetworks for Big Data Science
byLarry Smarr
 
PPTX
Berkeley cloud computing meetup may 2020
byLarry Smarr
 
PPT
Cyberistructure
byLab Southwest
 
PPTX
Bonazzi commons bd2 k ahm 2016 v2
byVivien Bonazzi
 
PPTX
EMBL Australian Bioinformatics Resource AHM - Data Commons
byVivien Bonazzi
 
PPTX
Global Research Platforms: Past, Present, Future
byLarry Smarr
 
PPTX
Peering The Pacific Research Platform With The Great Plains Network
byLarry Smarr
 
PPTX
Pacific Research Platform Science Drivers
byLarry Smarr
 
PPTX
Security Challenges and the Pacific Research Platform
byLarry Smarr
 
PPTX
Looking Back, Looking Forward NSF CI Funding 1985-2025
byLarry Smarr
 
PDF
Sgci esip-7-20-18
byNancy Wilkins-Diehr
 
PPTX
Massive-Scale Analytics Applied to Real-World Problems
byinside-BigData.com
 
The Pacific Research Platform: Building a Distributed Big-Data Machine-Learni...
byLarry Smarr
 
The Pacific Research Platform
byLarry Smarr
 
Advanced Global-Scale Networking Supporting Data-Intensive Artificial Intelli...
byLarry Smarr
 
The Pacific Research Platform
byLarry Smarr
 
Advanced Cyberinfrastructure Enabled Services and Applications in 2021
byLarry Smarr
 
The Pacific Research Platform:a Science-Driven Big-Data Freeway System
byLarry Smarr
 
The Pacific Research Platform
byLarry Smarr
 
UC-Wide Cyberinfrastructure for Data-Intensive Research
byLarry Smarr
 
Building the Pacific Research Platform: Supernetworks for Big Data Science
byLarry Smarr
 
Berkeley cloud computing meetup may 2020
byLarry Smarr
 
Cyberistructure
byLab Southwest
 
Bonazzi commons bd2 k ahm 2016 v2
byVivien Bonazzi
 
EMBL Australian Bioinformatics Resource AHM - Data Commons
byVivien Bonazzi
 
Global Research Platforms: Past, Present, Future
byLarry Smarr
 
Peering The Pacific Research Platform With The Great Plains Network
byLarry Smarr
 
Pacific Research Platform Science Drivers
byLarry Smarr
 
Security Challenges and the Pacific Research Platform
byLarry Smarr
 
Looking Back, Looking Forward NSF CI Funding 1985-2025
byLarry Smarr
 
Sgci esip-7-20-18
byNancy Wilkins-Diehr
 
Massive-Scale Analytics Applied to Real-World Problems
byinside-BigData.com
 

Viewers also liked

PPTX
Big Process for Big Data
byIan Foster
 
PPT
Public.Cdsc.Middleton
byHarvard Medical School, Partners Healthcare
 
PPTX
re:Invent 2013-foster-madduri
byRavi Madduri
 
ODP
Experiences In Building Globus Genomics Using Galaxy, Globus Online and AWS
byEd Dodds
 
PPTX
Role of Amyloid Burden in cognitive decline
byRavi Madduri
 
PPTX
Jsm madduri-august-2015
byRavi Madduri
 
ODP
Big Data and Genomics
byAl Costa
 
PPT
HL7: Clinical Decision Support
byHarvard Medical School, Partners Healthcare
 
PDF
ADAS&ME presentation @ the SCOUT project expert workshop (22-02-2017, Brussels)
byjoseplaborda
 
PPTX
Effective ansible
byWu Bigo
 
PPTX
CI4CC sustainability-panel
byRavi Madduri
 
PDF
Supporting Barack Obama for President
byHarvard Medical School, Partners Healthcare
 
PPTX
May 2013 HUG: Apache Sqoop 2 - A next generation of data transfer tools
byYahoo Developer Network
 
PDF
Raskar UIST Keynote 2015 November
byCamera Culture Group, MIT Media Lab
 
PPTX
Leap Motion Development (Rohan Puri)
byCamera Culture Group, MIT Media Lab
 
PDF
Stereo and 3D Displays - Matt Hirsch
byCamera Culture Group, MIT Media Lab
 
PPTX
What is Media in MIT Media Lab, Why 'Camera Culture'
byCamera Culture Group, MIT Media Lab
 
PPTX
Multiview Imaging HW Overview
byCamera Culture Group, MIT Media Lab
 
PPTX
Google Glass Breakdown
byCamera Culture Group, MIT Media Lab
 
PDF
What is SIGGRAPH NEXT? Intro by Ramesh Raskar
byCamera Culture Group, MIT Media Lab
 
Big Process for Big Data
byIan Foster
 
Public.Cdsc.Middleton
byHarvard Medical School, Partners Healthcare
 
re:Invent 2013-foster-madduri
byRavi Madduri
 
Experiences In Building Globus Genomics Using Galaxy, Globus Online and AWS
byEd Dodds
 
Role of Amyloid Burden in cognitive decline
byRavi Madduri
 
Jsm madduri-august-2015
byRavi Madduri
 
Big Data and Genomics
byAl Costa
 
HL7: Clinical Decision Support
byHarvard Medical School, Partners Healthcare
 
ADAS&ME presentation @ the SCOUT project expert workshop (22-02-2017, Brussels)
byjoseplaborda
 
Effective ansible
byWu Bigo
 
CI4CC sustainability-panel
byRavi Madduri
 
Supporting Barack Obama for President
byHarvard Medical School, Partners Healthcare
 
May 2013 HUG: Apache Sqoop 2 - A next generation of data transfer tools
byYahoo Developer Network
 
Raskar UIST Keynote 2015 November
byCamera Culture Group, MIT Media Lab
 
Leap Motion Development (Rohan Puri)
byCamera Culture Group, MIT Media Lab
 
Stereo and 3D Displays - Matt Hirsch
byCamera Culture Group, MIT Media Lab
 
What is Media in MIT Media Lab, Why 'Camera Culture'
byCamera Culture Group, MIT Media Lab
 
Multiview Imaging HW Overview
byCamera Culture Group, MIT Media Lab
 
Google Glass Breakdown
byCamera Culture Group, MIT Media Lab
 
What is SIGGRAPH NEXT? Intro by Ramesh Raskar
byCamera Culture Group, MIT Media Lab
 

Similar to Internet2 Bio IT 2016 v2

PDF
Internet2 Support for Biomedical Research
byEd Dodds
 
PDF
Internet2 Support for Biomedical Research
byEd Dodds
 
PDF
High-Performance Networking Use Cases in Life Sciences
byAri Berman
 
PDF
From the Benchtop to the Datacenter: IT and Converged Infrastructure in Life ...
byAri Berman
 
PDF
BioTeam Trends from the Trenches - NIH, April 2014
byAri Berman
 
PDF
Facilitating Collaborative Life Science Research in Commercial & Enterprise E...
byChris Dagdigian
 
PPTX
(Em)Powering Science: High-Performance Infrastructure in Biomedical Science
byAri Berman
 
PPT
Aaas Data Intensive Science And Grid
byIan Foster
 
PPTX
BioIT Trends - 2014 Internet2 Technology Exchange
byChris Dagdigian
 
PDF
2013: Trends from the Trenches
byChris Dagdigian
 
PDF
2015 CDC Workshop on ScienceDMZ
byChris Dagdigian
 
PPTX
Data-intensive bioinformatics on HPC and Cloud
byOla Spjuth
 
PDF
Bio-IT Asia 2013: Informatics & Cloud - Best Practices & Lessons Learned
byChris Dagdigian
 
PPT
The Thinking Behind Big Data at the NIH
byPhilip Bourne
 
PDF
NREN 3.0
byEd Dodds
 
PPTX
2016 05 sanger
byChris Dwan
 
PPTX
2015 Bio-IT Trends From the Trenches
byChris Dagdigian
 
PPT
AMIA 2014
byPhilip Bourne
 
PDF
High Performance Cyberinfrastructure and Data Services
byJerry Sheehan
 
PPTX
Service and Support for Science IT -Peter Kunzst, University of Zurich
byMind the Byte
 
Internet2 Support for Biomedical Research
byEd Dodds
 
Internet2 Support for Biomedical Research
byEd Dodds
 
High-Performance Networking Use Cases in Life Sciences
byAri Berman
 
From the Benchtop to the Datacenter: IT and Converged Infrastructure in Life ...
byAri Berman
 
BioTeam Trends from the Trenches - NIH, April 2014
byAri Berman
 
Facilitating Collaborative Life Science Research in Commercial & Enterprise E...
byChris Dagdigian
 
(Em)Powering Science: High-Performance Infrastructure in Biomedical Science
byAri Berman
 
Aaas Data Intensive Science And Grid
byIan Foster
 
BioIT Trends - 2014 Internet2 Technology Exchange
byChris Dagdigian
 
2013: Trends from the Trenches
byChris Dagdigian
 
2015 CDC Workshop on ScienceDMZ
byChris Dagdigian
 
Data-intensive bioinformatics on HPC and Cloud
byOla Spjuth
 
Bio-IT Asia 2013: Informatics & Cloud - Best Practices & Lessons Learned
byChris Dagdigian
 
The Thinking Behind Big Data at the NIH
byPhilip Bourne
 
NREN 3.0
byEd Dodds
 
2016 05 sanger
byChris Dwan
 
2015 Bio-IT Trends From the Trenches
byChris Dagdigian
 
AMIA 2014
byPhilip Bourne
 
High Performance Cyberinfrastructure and Data Services
byJerry Sheehan
 
Service and Support for Science IT -Peter Kunzst, University of Zurich
byMind the Byte
 

Internet2 Bio IT 2016 v2

  • 1.
    Pushing Discovery with Internet2 Cloudto Supercomputing in Life Sciences DAN TAYLOR Director, Business Development, Internet2 BIO-IT WORLD 2016 BOSTON APRIL, 2016
  • 2.
    2 – 8/30/ Internet2 Overview •An advanced networking consortium – Academia – Corporations – Government • Operates a best-in-class national optical network – 15,000 miles of dedicated fiber – 100G routers and optical transport systems – 8.8 Tbps capacity • For over 20 years, our mission has been to – Provide cost effective broadband and collaboration technologies to facilitate frictionless research in Big Science – broad collaboration, extremely large data sets – Create tomorrow’s networks & a platform for networking research – Engage stakeholders in • Bridging the IT/Researcher gap • Developing new technologies critical to their missions
  • 3.
    [ 3 ] The4th Gen Internet2 Network Internet2 Network by the numbers 17 Juniper MX960 nodes 31 Brocade and Juniper switches 49 custom colocation facilities 250+ amplification racks 15,717 miles of newly acquired dark fiber 2,400 miles of partnered capacity with Zayo Communications 8.8 Tbps of optical capacity 100 Gbps of hybrid Layer 2 and Layer 3 capacity 300+ Ciena ActiveFlex 6500 network elements
  • 4.
    Technology • A ResearchGrade high speed network – optimized for “Elephant flows” • Layer 1 – secure point to point wavelength networking • Advanced Layer 2 Services – Open virtual network for Life Sciences with connectivity speeds up to 100 Gbs • SDN Network Virtualization customer trials now • Advanced Layer 3 Services – High speed IP connectivity to the world • Superior economics • Secure sharing of online research resource – federated identity management system
  • 5.
    [ 5 ] Internet2Members and Partners 255 Higher Education members 67 Affiliate members 41 R&E Network members 82 Industry members 65+ Int’l partners reaching over 100 Nations 93,000+ Community anchor institutions Focused on member technology needs since 1996 "The idea of being able to collaborate with anybody, anywhere, without constraint…" —Jim Bottum, CIO, Clemson University Community
  • 6.
    6 – 8/30/ Strong internationalpartnerships • Agreements with international networking partners offer interoperability and access • Enable collaboration between U.S. researchers and overseas counterparts in over 100 international R & E networks Community
  • 7.
    Some of ourAffiliate Members 7
  • 8.
    [ 8 ] *Routers Stanford Computer Workstations Berkeley,Stanford Security Systems Univ of Michigan Security Systems Georgia Tech Social Media Harvard Network Caching MIT Search Stanford
  • 9.
    [ 9 ] TheRoute to Innovation August 30, 2016 © 2016 Internet2 Abundant Bandwidth • Raw capacity now available on Internet2 Network a key imagination enabler • Incent disruptive use of new, advanced capabilities Software Defined Networking • Open up network layer itself to innovation • Let innovators communicate with and program the network itself • Allow developers to optimize the network for specific applications Science DMZ • Architect a special solution to allow higher-performance data flows • Include end-to-end performance monitoring server and software • Include SDN server to support programmability
  • 10.
    Life Sciences ResearchToday • Sharing Big Data sets (genomic, environmental, imagery) key to basic and applied research • Reproducibility - need to capture methods as well as raw data – High variability in analytic processes and instruments – Inconsistent formats and standards • Lack of metadata & standards • Biological systems are immensely complicated and dynamic (S. Goff, CyVERSE/iPlant) • 21k human genes can make >100k proteins • >50% of genes are controlled by day-night cycles • Proteins have an average half-life of 30 hours • Several thousand metabolites are rapidly changing • Traits are environmentally and genetically controlled • Information Technology - High Performance Computing and Networking - now can explore these systems through simulation • Collaboration – Cross Domain, Cross Discipline – Distribution of systems and talent is global – Resources are public, private and academic
  • 11.
    BIO-IT Trends inthe Trenches 2015 with Chris Dagdigian Take Aways - Science is changing faster than IT funding cycle for data intensive computing environments - Forward looking 100G multi site , multi party collaborations required - Cloud adoption driven by capability vs cost - Centralized data center dead; future is distributed computing/data stores - Big pharma security challenge has been met - SDN is real and happening now; part of infrastructure automation wave - Blast radius more important than ever: DOE’s Science DMZ architecture is a solution https://youtu.be/U6i0THTxe4o http://www.slideshare.net/chrisdag/201 5-bioit-trends-from-the-frenches 2015 Bio-IT World Conference & Expo • Change • Networking • Cloud • Decentralized Collaboration • Security • Mission Networks
  • 12.
  • 13.
    13 – 8/30/2016,© 2009 Internet2 Data Tsunami Physics Large Hadron Collider Life Sciences Next Generation Sequencers CERN Illumina
  • 14.
  • 15.
    15 – 8/30/20 2012: US– China 10 Gbps Link Fed Ex: 2 days Internet+ FTP: 26 hours China ‐ US 10G Link: 30 secs Dr. Lin Fang Dr. Dawei Lin Sample.fa (24GB)
  • 16.
    NCBI/UC-Davis/BGI : Firstultra high speed transfer of genomic data between China & US, June 2012 “The 10 Gigabit network connection is even faster than transferring data to most local hard drives,” said Dr. Lin [of UC, Davis]. “The use of a 10 Gigabit network connection will be groundbreaking, very much like email replacing hand delivered mail for communication. It will enable scientists in the genomics-related fields to communicate and transfer data more rapidly and conveniently, and bring the best minds together to better explore the mysteries of life science.” (BGI press release) Life Sciences Engagement 16 Community
  • 17.
    Forward Looking 100GNetworks & Multi Site Multi Party Collaboration Accelerating Discovery: USDA ARS Science Network 8/30/2016, © 2016 Internet2
  • 18.
    [ 18 ] USDAAgriculture Research Services Science Network • USDA scope is far beyond human
  • 19.
    USDA Agricultural ResearchServices Use Cases • Drought (Soil Moisture) Project – Challenging Volumes of Data – NASA satellite data storage - 7 TB/mo., 36mo mission – ARS Hydrology and Remote Sensing Lab analysis - 108 TB – Data completely re-process 3 to 5 times • Microbial Genomics Project – Computational Bottlenecks – Individual Strains of bacteria and microorganism communities related to Food Safety Animal Health Feed Efficiency
  • 20.
    [ 20 ] ARSBig Data Initiative Big Data Workshop Recommendations, (February 2013) Three Pillars of the ARS Big Data Implementation Plan – Network, HPC, Virtual Research Support (April, 2014) • Develop a Science DMZ • Enable high-speed, low-latency transfer of research data to HPC and storage from ARS locations • Virtual Researcher Support Implementation Complete (Nov. 2015) Clay Center, NE; Albany, CA; Beltsville Labs/Nat’l Ag. Library, Beltsville, MD Stoneville, MS; Ft. Collins, CO Ames/NADC, IA • ARS Scientific Computing Assessment • Final Report March 2014
  • 21.
    SCInet Locations andGateways USDA AGRICULTURAL RESEARCH SERVICE Albany, CA Ft. Collins, CO Clay Center, NE Ames, IA Stoneville, MS Beltsville, MD 100 Gb 100 Gb 100 Gb 10 Gb 10 Gb10 Gb
  • 22.
    Cloud & DistributedResearch Computing @Scale [ 22 ] Community Internet2 Approach : Agile scaling of resources and capacity Access to multi-domain, multi-discipline expertise in one dynamic global community Offer a bottomless toolbox for Innovation for the researcher
  • 23.
    [ 23 ] NewHigh Speed Cloud Collaborations 8/30/20 16 23 10, x10G, x100G
  • 24.
    Syngenta Science Network BringingPlant Potential to Life through enhanced computing capacity
  • 25.
    Syngenta Science Network •Syngenta is a leading agriculture company helping to improve global food security by enabling millions of farmers to make better use of available resources. • Key research challenge: How to grow plants more efficiently? • Internet2 members, especially land grant universities, are important research partners.
  • 26.
    The Challenge – Increasingsize of scientific data sets – Growing number of useful external resources and partners – Complexity of genomic analyses is increasing – Need for big data collaborations across the globe – Must Innovate
  • 27.
    – Higher datathroughput – High speed connectivity to AWS Direct Connect Surge HPC Collaborations with academic community – High speed connections to best-in-class supercomputing resources NCSA – University of Illinois Leverage NCSA expertise in building custom R&D workflows Leverage NCSA Industry Partnership Program A*Star Supercomputing Center in Singapore Supports a global, distributed, scientific computing capability – Global scale : creating a global fabric for computing and collaboration
  • 28.
    “I want tobe 15 minutes behind NCSA and 6 months ahead of my competition” - Keith Gray, BP [ 28 ] National Center for Supercomputing Applications
  • 29.
    [ 29 ] *BetterDesigned* *More Durable* *Available Sooner* Theoretical & Basic Research Prototyping & Development Optimization & Robustification Commercialization
  • 30.
    [ 30 ] NCSAMayo Clinic @Scale Genome-Wide Association Study for Alzheimer’s disease • NCSA Private Sector Program – UIUC HPCBio – Mayo Clinic • BlueWatersteam and Swiss Institute of Bioinformatics worked together to identify which genetic variants interact to influence gene expression patterns that may associate with Alzheimer’s disease
  • 31.
    [ 31 ] BigData and Big Compute Problem • 50,011,495,056 pairs of variants • Each variant pair is tested against 181 subjects and 24,544 genic regions • Computationally large problem, PLINK: ~ 2 years at Mayo FastEpistasis: ~ 6 hours on BlueWaters • Can be a big data problem: - 500 PB if keep all results - 4 TB when using a conservative cutoff
  • 32.
  • 33.
    UCSC Cancer GenomicsHub: Large Data Flows to End Users 1G 8G 15G Cumulative TBs of CGH Files Downloaded Data Source: David Haussler, Brad Smith, UCSC; Larry Smarr, CalIT2 30 PB http://blogs.nature.com/news/2012/05/us-cancer-genome-repository-hopes-to-speed-research.html
  • 34.
    [ 34 ] SDSCProtein Data Base Archive • Repository of atomic coordinates and other information describing proteins and other important biological macromolecules. Structural biologists use methods such as X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy to determine the location of each atom relative to each other in the molecule. Information is annotated and publicly released into the archive by the wwPDB.
  • 35.
    SDSC • Expertise – Bioinformaticsprogramming and applications support. – Computational chemistry methods. – Compliance requirements, e.g., for dbGaP, FISMA and HIPAA. – Data mining techniques, machine learning and predictive analytics – HPC and storage system architecture and design. – Scientific workflow systems and informatics pipelines. • Education and Training – Intensive Boot camps for working professionals - Data Mining, Graph Analytics, and Bioinformatics and Scientific Worflows. – Customized, on-site training sessions/programs. – Data Science Certificate program. – “Hackathon” events in data science and other topics.
  • 36.
    8/30/20 16 Sherlock Cloud: AHIPAA-Compliant Cloud Healthcare IT Managed Services - SDSC Center of Excellence 36 • Expertise in Systems, Cyber Security, Data Management, Analytics, Application Development, Advanced User Support and Project Management • Operating the first & largest FISMA Data Warehouse platform for Medicaid fraud, waste and abuse analysis • Leveraged FISMA experience to offer HIPAA- Compliant managed hosting for UC and academia • Supporting HHS CMS, NIH, UCOP and other UC Campuses • Sherlock services : Data Lab, Analytics, Case Management and Compliant Cloud
  • 37.
  • 38.
    38 – 8/30/2016,© Internet2 Lawrence Livermore NL HPC Innovation Center Cardioid Electrophysiology human heart simulations allowing exploration of causes of • Arrhythmia • Sudden cardiac arrest • Predictive drug interactions. Depicts activation of each heart muscle cell and the cell-to-cell transfer of the voltage of up to 3 billion cells - in near-real time. Metagenomic analysis with Catalyst: • Comparing short genetic fragments in a query dataset against a large searchable index (14 million genomes - 3x larger than those currently in use) of genomes to determine the threat an organism poses to human health
  • 39.
    Community Data ScienceResources renci RADII and GWU HIVE Driving Infrastructure Virtualization Enabling Reproducibility For FDA Submissions [ 39 ]
  • 40.
    RADII Resource Aware DatacentriccollaboratIve Infrastructure Goal Make data-driven collaborations a ‘turn-key’ experience for domain researchers and a ‘commodity’ for the science community Approach A new cyber-infrastructure to manage data-centric collaborations based upon natural models of collaborations that occur among scientists. RENCI: Claris Castillo, Fan Jiang, Charles Schmidt, Paul Ruth, Anirban Mandal ,Shu Huang, Yufeng Xin, Ilya Baldin, Arcot Rajasekar SDSC: Amit Majumdar DUKE: Erich Huang Workflows - especially data-driven workflows and workflow ensembles - are becoming a centerpiece of modern computational science.
  • 41.
    RADII Rationale • Multi-institutionalresearch teams grapple with multitude of resources – Policy-restricted large data sets – Campus compute resources – National compute resources – Instruments that produce data • Interconnected by networks – Campus, regional, national providers • Many options, much complexity • Data and infrastructure are treated separately RADII Creates A cyberinfrastructure that integrates data and resource management from the ground up to support data-centric research. RADII allows scientists to easily map collaborative data-driven activities onto a dynamically configurable cloud infrastructure.
  • 42.
    Infrastructure management have novisibility into data resources Data management solutions have no visibility into the infrastructure RADII: Foundational technologies Data-grids present distributed data under a one single abstraction and authorization layer Networked Infrastructure as a Service (NIaaS) for rapid deployment of programmable network virtual infrastructure (clouds). Disjoint solutions Incompatible resource abstractions Gap to reduce the data-infrastructure management gap
  • 43.
    RADII System –Virtualizing Data, Compute and Network for Collaboration 43 Novel mechanisms to represent data-centric collaborations using DFD formalism Data-centric resource management mechanisms for provisioning and de- provisioning resources dynamically through out the lifecycle of collaborations Novel mechanisms to map data processes, computations, storage and organization entities onto infrastructure
  • 44.
    FDA and GeorgeWashington University Big Data Decisions: Linking Regulatory and Industry Organizations with HIVE Bio-Compute Objects [ 44 ] Presented by: Dan Taylor, Internet 2 | Bio IT | Boston | 2016
  • 45.
    EI H V From Jan2016: Vahan Simonyan, Raja Mazumder lecture NIH: Frontiers in Data Science Series https://videocast.nih.gov/summary.asp?Live=18299&bhcp=1 High-performance Integrated Virtual Environment A regulatory NGS data analysis platform
  • 46.
    BIG DATA –From a range of samples and instruments to approval for use analysis and review sample archival sequencing run file transfer regulation computation pipelines produced files are massive in size transfer is slow too large to keep forever; not standardized difficult to validate difficult to visualize and interpret how do we avoid mistakes? NGS lifecycle: from a biological sample to biomedical research and regulation
  • 47.
    • Data Size:petabyte scale, soon exa-bytes • Data Transfer: too slow over existing networks • Data Archival: retaining consistent datasets across many years of mandated evidence maintenance is difficult • Data Standards: floating standards, multiplicity of formats, inadequate communication protocols • Data Complexity: sophisticated IT framework needed for complex dataflow • Data Privacy: constrictive legal framework and ownership issues across the board from the patient bedside to the FDA regulation • Data Security: large number of complicated security rules and data protection tax IT subsystems and cripple performance • Computation Size: distributed computing, inefficiently parallelized, requires large investment of hardware, software and human-ware • Computation Standards: non canonical computation protocols, difficult to compare, reproduce, rely on computations • Computation Complexity: significant investment of time and efforts to learn appropriate skills and avoid pitfalls in complex computational pipelines • Interpretation: large outputs from enormous computations are difficult to visualize and summarize • Publication: peer review and audit requires communication by massive amount of information ... and how do we avoid mistakes ? software challenges and needs
  • 48.
    HIVE is anEnd to End Solution • Data retrieval from anywhere in the world • Storage of extra large scale data • Security approved by OIM • Integrator platform to bring different data and analytics together • Tailor made analytics designed around needs • Visualization made to help in interpretation of data • Support of the entire hard-, soft-ware and knowledge infrastructure • Expertise accumulated in the agency • Bio-Compute objects repository to provide reproducibility and interoperability and long term referable storage of computations and results HIVE is not • an application to perform few tasks • yet another database • a computer cluster or a cloud or a data center • an IT subsystem More: http://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm491 893.htm
  • 49.
    Instantiation DataTypeDefinitions Definitions of metadata types DataTyping Engine Definitions of computations metadata Data Bio-compute Definitions of algorithms and pipeline descriptions Computational protocols Verifiable results within acceptable uncertainty/er ror Scientifically reliable interpretation HIVE data universe
  • 50.
    industry FDA regulatory analysis 2.compute 3. submit 1. data- forming 6. issues resubmits 5. regulatory decision 4. SOPP/prot ocols consumer $ millions of dollars 7. yes 7. no regulatory iterations ~$800 Million R&D dollars for a single drug ~$2.6 Billion total cost
  • 51.
    industr y FD A HIVE public-HIVEGalaxy CLC DNA-nexus 2. compute 3. submit1.data-forming 6. issues resubmits 5. bio- compute 2. HIVE SOPP/protocols 4. SOPP/prot ocols consumer 7. yes 7 .no 4. submit bio-compute integration 3. compute Facilitate integration $ millions of dollars bio-compute as a way to link regulatory and industry organizations
  • 52.
  • 53.
    [ 53 ] Community-developedframework of trust enables: • Secure, streamlined sharing of protected resources • Consolidated management of user identities and access • Delivery of an integrated portfolio of community-developed solutions [ 53 ] Trusted Identity in Research The standard for over 600 higher education institutions—and counting!
  • 54.
    [ 54 ] 15425+ 2 160+ 0 2000+ 7.8 million Academic Participants Sponsored Partners Registered Service Providers Individuals served by federated IdM Foundation for Trust & Identity 54 ®
  • 55.
    • Eric Boyd,Internet2 • Stephen Wolff, Internet2 • Stephen Goff, PhD, CyVERSE/iPlant, University of Arizona • Chris Dagdigian, BioTeam • Daiwei Lin, PhD, NIAID, NIH • Paul Gibson, USDA ARS • Paul Travis, Syngenta • Evan Burness, NCSA • Sandeep Chandra, SDSC • Jonathan Allen, PhD, Lawrence Livermore National Lab • Claris Castillo, PhD, RENCI • Vahan Simonyan, PhD, FDA • Raja Mazumder, PhD, George Washington University • Eli Dart, ESNET, US Department of Energy • BGI • Nature [ 55 ] Acknowledgements
  • 56.
    Thank you! Daniel Taylor,Director, Business Development Internet2 dbt3@internet2.edu 703-517-2566
  • 57.
  • 58.
    [ 58 ] Risingexpectations Network throughput required to move y bytes in x time. (US Dept of Energy - http://fasterdata.es.net). should be easy This year
  • 59.
    3/30/16, © 2016 Internet2 ScienceDMZ* and perfSONAR Design pattern to address the most common bottlenecks to moving data * fasterdata.es.net 59

Editor's Notes

  • #2 Greetings I’m Dan Taylor from Internet2 – thanks for joining us. I’m going to talk a bit about internet2 and the work we’re doing with clouds and other compute resources in our community. There are a lot of slides and I’ll move quickly so pls stop by our booth or download the slides if you have questions.
  • #3 Internet2 is the Research and education network for the US. We’re a membership consortium of academia , government and corporations. Internet2 is an advanced networking consortium comprised of 221 U.S. universities, in cooperation with 45 leading corporations, 66 government agencies, laboratories and other institutions of higher learning, 35 regional and state research and education networks and more than 100 national research and education networking organizations representing over 50 countries Internet2 actively engages our stakeholders in the development of important new technologies including middleware, security, network research and performance measurement capabilities which are critical to the achievement of the mission goals of our members. Throughout our first 15 years, Internet2 has served a unique role among networking organizations, pioneering the use of advanced network applications and technologies, and facilitating their development—to facilitate the work of the research community. Internet2 operates an advanced national optical network based on 17,500 miles of dedicated fiber and utilizes the latest 100G routers and optical transport systems with 8.8Tbps of system capacity
  • #4 Goal: Deepen and extend, advance, sustain, advance digital resources ecosystem. Value: Growing portfolio of resources and services: advanced computing, high-end visualization, data analysis, and other resources and services. Interoperability with other infrastructures.
  • #6 membership numbers as of 2014-03-27 Campus Champions (200 at 175 institutions) 14,000 participants in training workshops (online and in person).
  • #7 Absolutely key to our success is the global partnerships we have formed. [>>] Internet2 partners with over 50 national research and education networks including our friends in Canada to enable connectivity to more than 100 international networks. These partnerships provide the basis for understanding how to facilitate collaborations between the US Internet2 community and counterparts in other countries Our global partnerships have yielded important developments in new technologies. For example - the DICE collaborative is a partnership between GEANT, Internet2, CANARIE and ESnet which provides a joint forum for North American and European investment in advanced networking leadership Our collaboration has led to the development of world-leading tools like PerfSONAR and dynamic circuit networking – which I will touch on later. Our focus in 2010 is to deliver direct services to our members as a result of our development investments
  • #9 Our community has a track record of IT successes ; we haven’t looked at life sciences yet but I’m pretty sure the Internet2 community’s impact is even greater there
  • #10 R&E must keep constructing the conditions that spur innovation Give innovators an environment where they’re free to try new, untested, unpopular, ridiculously challenging things Innovation requires a big playground An innovation platform must encourage utilization, not limit it
  • #11 Life sciences research shares many of the trends we see else where in big science - data set sizes growing rapidly, increased need for collaboration – but we also see a new ecosystem fueling research. At the same time , however, diminishing R&D $ are pressuring the industry and government .
  • #12 Chris Dagdigian does a great job detailing how IT deals with the changes in Life sciences research. I have a couple of takeways from his talks, its useful to see how Internet2 addresses whats going on Scientific instrument technology – which generates scinetifc data – is changing faster than the IT refresh cycle. Organizations see the big data wave coming and are now implementing 100 G networks to get ahead of the rising tide Organizations are going to the cloud to be able to do things they’ve can’t do on theyir own, not just to save monney Centralization will not wliminate the need to move data Security concerns with high speed transfers and collaboration can be addressed Virtualized infrastructure is moving to the wide area Big science flows are more disruptive than ever to enterprise networks – theres a trend toward separating business and research networks
  • #13 One of the things we used to in the R&E community is change in scientific data growth
  • #14 The internet2 community has dealt with the data tsunami for many years now. The LHC shut down for 2 years to upgrade its power – annual output has jumped from 13 to 30 petabytes a year. This data is distributed thru out the world by the R&E networks. In Life Sciences driver is NGS, falling in price rapidly and a proliferation of devices generating data all over the world http://www.nature.com/news/large-hadron-collider-the-big-reboot-1.16095
  • #15 Our network has responded
  • #16 Back in 2012 we showed how a 10G link from beijing to UC Davis could change the game. A 24 GB file that would take 26 hours to traverse the internet was transferred in 30 seconds
  • #17 Researchers likened the difference in collaboration like going from letters to email
  • #18 So we’re seeing organizations get ahead of the tsunami by getting bigger networks. I recently helped the Department of Agriculture’s Agriculture Research Service do just this.
  • #19 I like to show this slide to illustrate how much llfe there is beyond humans, and USDA ARS has to deal with many of them – and how they impact our world. It shows the size of genomes of various species, with the x axis being a log scale. Humans are there at the top, one of a number of mammals the usda is interested in . But they are also interested in birds, crustaceans, fish, fungi , algae , bacteria and protozoans – and of course plants. And, some are extremely complex – you see the size of the wheat genome is orders of magnitude larger than the human.
  • #20 Beyond genomics , these kinds of projects create huge volumes of data as well as computational bottlenecks
  • #21 To attack this problem they gathered requirements in 2013, hired bioTeam to do an assessment and we actually completed a 6 node science network of 10 and 100G links by the end of 2015. that was fast!!
  • #22 R&E collaborations are handled at the 100G links on the coasts and another 100g feeds the new HPC in Ames Iowa
  • #24 You can view Internet2 as the medium for all the data and computing resources, forming a problem solving community around these high speed connections
  • #25 Syngenta , a life sciences company , is a great example of an organization making the most of these connections
  • #26 They are an agribusiness with a mission to improve plant productivity, they stay on the leading edge of science thru their internal research and their collaboration with the academic community
  • #27 Syngenta was challenged by many of the issues USDA saw, but on a global scale and even more pressure to innovate.
  • #28 We installed a 10G Layer 2 service that provided high speed Direct Connect access to AWS where they could do surge HPC and retrieve sequencing data outsourced to the academic community. They also could connect to NCSA to build and run custom pipelines. They can also use the connection to work with A*Star supercomputer center in Singapore , where they intend on building an asian genomic center. Finally we expect to bring up locations in switzerland and GB, completing a global research network.
  • #29 I just mentioned NCSA and this resource deserves a few seconds. NCSA does a lot of work with industry , and a comment from a VP at BP says it all….
  • #30 Leveraging its talent and one of the fastest computers in the world, NCSA provides companies with a full range of services to help the innovate
  • #31 They do a lot of work in the life sciences ; the one I’ll note here is an alzheimers gwas study with Mayo clinic
  • #32 In this one they handled an enormous amount of data and kind of strong armed the computational challenge – what wouldve taken 2 years at Mayo was done in 6 hrs on Blue Waters
  • #33 Another incredible resource in the community is SDSC
  • #34 You may know them as the home of CGHUB which holds the cancer genome atlas. Note the bits/second growth from 1g to 15 G from 2012 -2015 CGHub is a large-scale data repository and portal for the National Cancer Institute’s Cancer Genome Research Programs Current capacity is 5 Petabytes, scalable to 20 Petabytes. The Cancer Genome Atlas, one data collection of many in the CGH, by itself could produce 10 PB in the next four years As an illustration of how Internet2 is making network resources accessible, consider the the UCSC Cancer Genomics Hub, operated by the University of California at Santa Cruz and located at the San Diego Supercomputer Center co-location facility. Without the “big pipes” provided between SDSC and Internet2, the CGH would not be able to keep pace with demand for its data. As both users and data in the repository grew over a three year period, the bandwidth needed to support the activity grew by 15x.
  • #35 SDSC also has other important data sources like the Protein Data base archive
  • #36 They also have consulting services very much focused to support life sciences research
  • #37 I’d also note the cloud environment they built for HHS CMS – FISMA compliant and HIPAA ready.
  • #38 The National Labs are also a huge part of the community
  • #39 Whenever I run into a Metagenomics problem I reference Jonathan Allen’s huge microbiome work with metagenomics
  • #40 We also have a number of interesting efforts to facilitate collaboration and reproducibility.
  • #41 RADII is an exciting project that virtualizes clouds leveraging iRODS and virtual networks. The idea is to allow researchers, not IT, to spin up and monitor local and cloud resources, compute and network infrastructure on demand. So for example when I need to complete collaborative a workflow and move data and compute over a number of compute resources
  • #42 Radii allows you to represent data-centric collaborations using standard modeling mechanisms; map data processes, computations, storage, and organizational entities onto the physical infrastructure with the click of a button provision and de-provision infrastructure dynamically throughout the lifecycle of the collaboration.
  • #43 Radii builds on the data management of irods and infrastructure virtualization of ORCA and Exogeni to give researchers control over the infrastructure that’s necessary for collaboration
  • #44 Here’s an example of this virtualization, with researchers at Duke UNC and Scripps sharing data and workflows on SDSC compute resources. Ease of use, Improve end to end performance perceived by the scientists To enable this vision we need two technologies with high level of programmability and automation.
  • #45 A collaboration between the FDA and Gw is looking improve reproducibility by using biocompute objects. This should accelerate regulatory approvals and reduce costs.
  • #47 This represents the process for FDA submissions supported by NGS. There is a lot of opportunities for making mistakes along the way. These mistakes result in delays and costly resubmissions
  • #48 Of the challenges in gaining agreement at the end of this process, many of which are addressed by HIVE, its potential to impact reproducibility is the most exciting
  • #49 The HIVE platform is big data analysis solution used by the FDA and available to industry. The bio compute objects repository is key to reproducibility
  • #50 To get to better reproducibility, HIVE relies on a data typing engine to define meta data for the data , computations and both algorithms and pipelines to create a biocompute object related to the submission that’s reusable by the FDA. Data typing engine- facility allowing to register structure, syntax and ontologies of the information fields of objects. Metadata type- descriptive information on the structure of data files or electronic records. Computation metadata- Description of arguments and parameters (not values) for computational analysis. Definitions of algorithms and pipeline descriptions- descriptions of the characteristics for executable applications. Data- collection of actual values observed and accumulated during experimentation by a device or an observer. Computational protocol- well parameterized computational pipeline designed to produce scientifically meritable outcomes with appropriate data. Bio-compute- instance of an actual execution of the computational protocols on a given set of data with actual values of parameters generating identifiable outcomes/results.
  • #52 HIVE would help by recording the parameters of the analysis as biocompute objects (or use existing ones in the public repository) and share them with FDA so they can verify that analysis. Data forming is done using a public hive and integrated with your usual analytic tools. The resulting biocompute objects are submitted to FDA; these biocompute objects are used in the FDA HIVe to validate the results of the submission.
  • #53 Finally I ‘ll say a few words about federated identity.
  • #54 Over 10 yrs ago the R&E Community recognized the importance of trust in collaborations and created the InCommon federated identity management solution.
  • #55 We now have a leading solution with around 8MM users. Pls stop by the booth for more information.