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Faster , More Scalable Computing in the Cloud

Faster , More Scalable Computing in the Cloud. Pavan Pant, Director Product Management. Using the Cloud for Infrastructure On Demand. % of Life Sciences R&D Informatics Budget Devoted to Cloud Computing in 3Years. Response %. 0%. 0.0%. 1-5%. 27.3%. 6-10%. 18.2%. 11-25%. 9.1%. 26-50%.

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Faster , More Scalable Computing in the Cloud

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  1. Faster, More Scalable Computing in the Cloud Pavan Pant, Director Product Management

  2. Using the Cloud for Infrastructure On Demand % of Life Sciences R&D Informatics Budget Devotedto Cloud Computing in 3Years Response % 0% 0.0% 1-5% 27.3% 6-10% 18.2% 11-25% 9.1% 26-50% 36.4% >50% 9.1% Source: Insight Pharma Reports Need massive computing power Used to scaling resources Bear huge costs & delays of provisioninginternally Shared resource environment offers economies of scale The Market is Predicted to Have Significant Usage within 3 Years 2 CloudSwitch Proprietary & Confidential

  3. Apps Ripe for Cloud Computing Which Apps? • Next-generation DNA sequencing • Pattern recognition • Data mining • Molecular modeling & simulation • Protein docking Why? • Burst/peak scale out • Improving the application lifecycle • Collaboration “Often we want to take the data that we have, marry it up with data that’s publicly available – could be big genomic data sets – look at all of that collectively, and then extract value from that. So they’re very bursty. They really peak. It’s lots of data and then you’re done.” – Michael Heim, CIO, Eli Lilly Source: “Cloud Computing in Life Sciences,” Insight Pharma Reports, April, 2010; and Pharma Consultant CloudSwitch Proprietary & Confidential

  4. Early Adopters • Eli Lilly: • 64-machine cluster using Amazon EC2 • Completed sequence processing in 20 minutes vs 12 weeks • Cost: $6.40 • Plan to have up to 10 HPC applications “cloud enabled” by end of the year “For us, it's pipeline, pipeline, pipeline. Anything we can do to further our knowledge, get products into the pipeline, and develop those more quickly, is crucial to us. It's hard to underestimate the value of letting scientists work at their own pace.” – Michael Heim, CIO, Eli Lilly • Pfizer’s Biotherapeutics and BioinnovationCenter: • Used EC2 to develop & refine models in antibody-antigen docking runs • Shortened the process from days to hours http://www.sramanamitra.com/2010/10/08/boundaries-between-hpc-and-cloud-computing-vanishing /http://www.expresspharmaonline.com/20101031/market01.shtml CloudSwitch Proprietary & Confidential

  5. Changing Pharmaceutical Research Landscape Reduce operational costs and capex Reduce time to discovery & development Increasingly complex data sets& processing requirements Growing collaboration and data sharing CloudSwitch Proprietary & Confidential

  6. What’s Needed to Make the Cloud Work Orchestration layer Flexibility More high-compute resources Security More streamlined procurement Ease of deployment/transparency with data center Enterprise-level implementations Cloud resource provisioning CloudSwitch Proprietary & Confidential

  7. Cloud 1 App 2 App 2 App 2 App 3 App 3 App 3 App 5 App 4 Cloud 2 CloudSwitch Product Architecture Components work cooperatively to simplify, secure, and manage operating environments in the cloud Customer Data Center CLOUD ISOLATION TECHNOLOGYTM VIRTUAL MANAGEMENT/CONTROLS CloudSwitch Instance (CSI) App 1 App 2 CloudSwitch Appliance (CSA) VIRTUALIZED STORAGE FIREWALL ENCRYPTED TUNNEL App 3 CLOUD ISOLATION TECHNOLOGYTM CloudSwitch Instance (CSI) VIRTUALIZED STORAGE App 5 DATA CENTER SERVICES: DNS, LDAP, Identity, Infrastructure… VIRTUALIZED STORAGE CloudSwitch Proprietary & Confidential

  8. Use Case: Bio Informatics in the Cloud Cloud Compute Cluster Data Center (Internal) Queue Master Provision Server CloudSwitch Instance (CSI) CloudSwitch Appliance (CSA) Data Source 2 Data Source 1 Secure Connection Compute Job Submission Data Center LAN Firewall Administrators Data Server Compute LAN Compute 1 Compute n,000 Compute 2 Data Center LAN Scientists & Researchers CloudSwitch Proprietary & Confidential

  9. CloudSwitch HPC Scenario • Large Pharmawith 1000 Cores in Amazon EC2 • Created 500 compute node clones (1000 cores) in ~30 minutes • Provisioned all nodes via network boot (PXE) in the cloud in 45-60 minutes • Using Sun Grid Engine v6.2 as the queue master • Using Rocks v5.4 as the front-end “control” server • Started with 48-hour test for a high performance bioinformatics workload • Goal is to establish a more permanent footprint in the cloud • Elasticity and Significantly Lower Capital Expenditure • Compute nodes are brought up when needed and shut down after the compute process was finished • Total cost of less than $10,000 to run 1,000 cores in Amazon for 48 hours • All done securely and seamlessly using CloudSwitch as the management control plane for the compute nodes and the Rocks and SGE environment CloudSwitch Proprietary & Confidential

  10. Use Cases & Benefits of the Cloud for Healthcare • Two Common Use Cases • Cluster capacity for burst/peak demand • Scale-out for research and informatics usage with data center control • Dev/test environments in the cloud • Offload from production gear • Enable self-service and scale testing • Bring back on-prem for production • Benefits • Elasticity • Reduce Ongoing Costs • Process Complex Data Sets Via Horizontal Scaling in the Cloud CloudSwitch Confidential

  11. Q&A pavan@cloudswitch.com Try our CloudSwitch Enterprise software www.cloudswitch.com

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