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

Using the Cloud for Infrastructure On Demand CloudSwitch Proprietary & Confidential2 0.0% 27.3% 18.2% 9.1% 36.4% 9.1% 0% 1-5% 6-10% 11-25% 26-50% >50% Response % Source: Insight Pharma Reports % of Life Sciences R&D Informatics Budget Devoted to Cloud Computing in 3Years Need massive computing power Used to scaling resources Bear huge costs & delays of provisioning internally Shared resource environment offers economies of scale The Market is Predicted to Have Significant Usage within 3 Years

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 CloudSwitch Proprietary & Confidential3 Source: “Cloud Computing in Life Sciences,” Insight Pharma Reports, April, 2010; and Pharma Consultant “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

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 CloudSwitch Proprietary & Confidential4 / “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 Bioinnovation Center: –Used EC2 to develop & refine models in antibody-antigen docking runs –Shortened the process from days to hours

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

What’s Needed to Make the Cloud Work CloudSwitch Proprietary & Confidential6 Orchestration layer More high-compute resources More streamlined procurement Enterprise-level implementations Flexibility Security Ease of deployment/ transparency with data center Cloud resource provisioning

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

Use Case: Bio Informatics in the Cloud Data Center (Internal) Cloud Compute Cluster Data Center LAN CloudSwitchInstance(CSI)CloudSwitchInstance(CSI) CloudSwitchAppliance(CSA)CloudSwitchAppliance(CSA) Secure Connection Data Center LAN Data Server QueueMasterQueueMaster Compute 1 Compute 2 Compute n,000 ProvisionServerProvisionServer Compute LAN Data Source 1 Data Source 2 Compute Job Submission 8 CloudSwitch Proprietary & Confidential

CloudSwitch HPC Scenario Large Pharma with 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 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 & Confidential9

Use Cases & Benefits of the Cloud for Healthcare Two Common Use Cases 1.Cluster capacity for burst/peak demand Scale-out for research and informatics usage with data center control 2.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 11/5/2010CloudSwitch Confidential10

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