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Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

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Page 1: Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

Cloud Computingat

Johnson & Johnson

Pharmaceutical Research & Development LLC

Page 2: Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

Agenda

• Introduction• Strategic Goals• Success Stories• Lessons Learned• Future Plans

Page 3: Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

What is Cloud Computing?

• Utility based computing and storage– Pay for use

• CPUs per hour of use• Storage per gigabyte used

– Scalable on demand• Provisioning new CPUs takes minutes• Storage can be grown as needed within minutes

• Multi-tiered solution– Infrastructure as a service (ex. Amazon EC2, S3)– Platform as a service (ex. Azure, Salesforce.com) – Software as a service (ex. Gmail, Google Apps)

Page 4: Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

Strategic Goals

• Resources for High Performance Computing (HPC) peak demand– Additional CPUs to shorten processing time– Additional storage for ‘scratch’ space

• Archival storage• Development instances• Training instances• Collaboration environments• Quickly extend existing infrastructure

Page 5: Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

Success Story: PK/PD

• Nonmem and Bootstrapping– Needs additional CPUs to

• shorten response times for FDA submission inquires• create more detailed models

– Larger population sizes– Greater number of parameters

– Implemented on Amazon EC2

Client PC Firewall

Amazon EC2

Cycle Cloud Head Node+

Nonmem Compiler

Compute Node

Compute Node

Compute NodeCompute Node

SSL Secured Communication/Encryption

Page 6: Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

Success Story: Cloud Storage

• Goals– Test viability of Nirvanix Storage delivery Network– Collect transfer speeds for upload and download– Integrate into Veritas Netbackup as storage media by creating Virtual

Tape Library using CloudNAS client.– Evaluate performance, cost.

• Results– All base functionality worked as planned– Tests showed increased elasticity and scalability of storage– Performance met targets and significant cost savings

• Planned usage for archival and retrieval of• NextGen Sequencer data – 10+ TBs• DNA Chip data – 5 ~10 TBs• NuGenesis data – 2 TBs

Page 7: Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

Success Story: TranSMART

• Partnership/collaboration with Recombinant Data Corporation to leverage Clinical and Biomarker data– Use Pathway Analysis and Biomarkers to direct research

investment decisions– Execute the transition from bench to bedside translational

research– Provide a collaboration platform for Pre-Clinical and Clinical,

Biologists, Clinicians and Bioinformaticists– Execute a systems biology approach for Discovery and

Development

Page 8: Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

Success Story: Image Processing

• Business opportunity or challenge– Research Capabilities uses a program called Feldkamp to convert 2D cat scan

images to 3D image slices for visualization– The processing time for each cat scan is 22 hours on a local server– The next study has 100 images that need to be processed; on a local server this

would take ~ 92 days (meaning the business would not conduct the study at all)

• Solution– Launch 11 concurrent servers in the Amazon Cloud to process one cat scan at a

time; reduce processing time from 22 hours to 2 hours

• Expected business results– For 100 cat scan files, processing time will be reduced from 92 days to 8 days– Estimate cost for processing one cat scan file = $13.82 (for all 100 files = $1,382)– Saved time = 84 days

Page 9: Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

Lessons Learned

• Security– Involve security folks early– Internal processes bigger

hurdle than technical learning

• Applicability– Cloud is not the solution for

everything• HPC heavy on Message

Passing Interface (MPI)• Business critical systems

• Architecture– Do not split your systems across

networks (e.g., app and DB)– Include security in your design

• Legal– Start work on the hosting

agreement ASAP– Educate your legal staff

– Hosting without specifying physical asset

Page 10: Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

Future Plans

• Evaluate additional applications for Cloud deployment• Develop enterprise strategy

– In progress with Corp IT

• Evaluate additional providers and vendors– Avoid lock-in to a single platform

• Work cross-sectors on cloud initiatives– In progress with MD&D

• Expand internal compute Grid to the Cloud– Done in DEV

Page 11: Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

Molecular Conformations

• Application to perform molecular conformations given an input file of molecules (e.g. SMILES) and a governing rule-set

• Business challenge– Molconf currently runs on individual users’ machines– Performance limited by the user’s hardware (> 7 hours to run 100,000 molecules)

• Solution– Distribute all computations to the Microsoft Azure cloud platform

• Expected Results– Processing time in minutes rather than hours– Major performance upgrade: all work can be distributed among multiple nodes– Scalability: spin up nodes on-demand to handle workload– Independence from users’ machines; submit a job and retrieve it later

Page 12: Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

Observational Medical Outcomes Partnership (OMOP)

• Public-private partnership between FNIH and large Pharma companies– Improve monitoring of drug safety, establish Common Data Model schema (CDM)

• OMOP Cup: contest to collect best safety surveillance scripts– Open-source scripts that leverage CDM schema

• Business challenge:– How to best harness the power of the CDM and the OMOP Cup scripts in a

federated, scalable environment

• Solution:– Work with data vendors (United Healthcare, Thomson, Premier, etc.) to embrace

CDM instead of using proprietary schemas– Work with Microsoft to establish cloud solution for accessing this data and running

the OMOP Cup scripts

Page 13: Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

External Collaboration

• Leveraged a neutral third party to develop cloud solutions in the pre-competitive space– Eli Lilly and Cycle Cloud => BLAST– J&J PRD and Cycle Cloud => Nonmem– Both Pharmas have access to BLAST and Nonmem

• Cross-Pharma HPC Group– Started 4 years ago for best practices in Advanced Computing– Now used for pre-competitive collaboration

• Vendor Management• Open source initiatives• Cloud Computing opportunities, trends and challenges• Benchmarking of HPC across the Pharma sector

Page 14: Cloud Computing at Johnson & Johnson Pharmaceutical Research & Development LLC

Acknowledgments• Sebastian Piotrowski

– IT Lead, R&D Advanced Technology CoE– [email protected]

• Tom Messina– IT Manager, R&D Advanced Technology CoE– [email protected]