1. Copyright © 2010 Platform Computing Corporation. All Rights Reserved.1 The CERN Cloud Computing Project William Lu, Ph.D. Platform Computing

2. Copyright © 2010 Platform Computing Corporation. All Rights Reserved.2 LHC Computing Hierarchy Markus Schulz, CERN2 Emerging Vision: A Richly Structured, Global Dynamic System Tier 0 +1 Tier 1 Tier2 Center Online System CERN Center PBs of Disk; Tape Robot FNAL Center IN2P3 Center INFN Center RAL Center Institute Workstations ~100-1500 MBytes/sec 2.5-10 Gbps Tens of Petabytes by 2010. An Exabyte ~5-7 Years later. ~PByte/sec 10 Gbps Tier2 Center ~2.5-10 Gbps Tier 3 Tier 4 Tier2 Center Tier 2 Experiment CERN/Outside Resource Ratio ~1:2 Tier0/(  Tier1)/(  Tier2) ~1:1:1 0.1 to 10 Gbps Physics data cache

3. Copyright © 2010 Platform Computing Corporation. All Rights Reserved.3 Computers: 40,000 CPU cores used by multiple experiments Storage: Disks + tapes Storage management system (CASTOR) is tightly integrated with workload management (Platform LSF) Software: Apps: Open source, home grown, OS: Scientific Linux, other Linux VMs: open source XEN, KVM Environment

4. Copyright © 2010 Platform Computing Corporation. All Rights Reserved.4 IT serves users manually User requests of resource, OS, software stack etc. are handled manually, which is slow Users circumvent scheduling policies Users are not satisfied with the centralized management scheduling policies due to their unique needs They submit a pilot job to occupy resources then run scripts to prepare the application environment and schedule jobs within the resource block. This causes low resource utilization Legacy application issues Legacy applications need legacy OS, which does not run on the latest hardware Challenges

5. Copyright © 2010 Platform Computing Corporation. All Rights Reserved.5 Batch Virtualization Requirements  How to o Insolate application environment o Increased security  How to o Automate resource provisioning and management o Scalable management practice Virtualization

6. Copyright © 2010 Platform Computing Corporation. All Rights Reserved.6 Platform ISF + Platform ISF Adaptive Cluster  Integration with Platform LSF to provision VMs based on workload  Integration with provisioning system Quattor  Each experiment is able to schedule their own VM clusters with unique application environment  VM cluster capacity is elastic based on workload Solution

7. Copyright © 2010 Platform Computing Corporation. All Rights Reserved.7 How It Works? Platform ISF Shared pool of resources HPC administrator sets up VM resource pools, one for each experiment 1 Platform LSF User submits a workload that cannot be met by his VM resource pool 3 Platform ISF AC interacts with Platform ISF to adjust the size of the resource pool 4 External Provider Platform LSF Platform ISF AC HPC administrator also sets up minimum and maximum number of VMs within each pool 2

8. Copyright © 2010 Platform Computing Corporation. All Rights Reserved.8 Increase user service level Each experiment can control their own application stack and resource allocation policies Redeploy servers quickly and efficiently Results Reduce cost and save power Shares batch compute servers with data management and database servers Automated administration Allow scalability No hypervisor lock-in Freedom of choosing multiple VM hypervisors