1 / 8

The CERN Cloud Computing Project

The CERN Cloud Computing Project. William Lu, Ph.D. Platform Computing. CERN/Outside Resource Ratio ~1:2 Tier0/(  Tier1)/(  Tier2) ~1:1:1. ~ PByte /sec. ~100-1500 MBytes /sec. Online System. Experiment. CERN Center PBs of Disk; Tape Robot. Tier 0 +1. Tier 1. 10 Gbps.

miller
Download Presentation

The CERN Cloud Computing Project

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. The CERN Cloud Computing Project William Lu, Ph.D. Platform Computing

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

  3. Environment 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

  4. Challenges 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

  5. Batch Virtualization Requirements • How to • Insolate application environment • Increased security • How to • Automate resource provisioning and management • Scalable management practice Virtualization

  6. Solution 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 toschedule their own VMclusters with uniqueapplication environment • VM cluster capacity is elastic based on workload

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

  8. Results Increase user service level • Each experiment can control their own application stack and resource allocation policies Redeploy servers quickly and efficiently • 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

More Related