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Providing Scientific Software as a Service in Consideration of Service Level Agreements

Providing Scientific Software as a Service in Consideration of Service Level Agreements. Oliver Niehörster(1), André Brinkmann(1), Georg Birkenheuer (1), Sonja Herres-Pawlis(2), Julia Niehörster(3), Jens Krüger(2), Brigitta Elsässer(2), Lars Packschies(4)

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Providing Scientific Software as a Service in Consideration of Service Level Agreements

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  1. Providing Scientific Software as a Servicein Consideration of Service Level Agreements Oliver Niehörster(1), André Brinkmann(1), Georg Birkenheuer(1), Sonja Herres-Pawlis(2), Julia Niehörster(3), Jens Krüger(2), Brigitta Elsässer(2), Lars Packschies(4) (1) Paderborn Center for Parallel Computing, Universität Paderborn, Germany (2) Department Chemie, Universität Paderborn, Germany (3) Department Agricultural Sciences, Universität Hohenheim, Germany (4) Universität zu Köln, Germany

  2. Scientific SaaS Job & SLA • Provider Advantages • Horizontal & vertical scaling of virtual machines • Better resource utilization • Snapshot & live migration • High availability, possible error recovery Cloud

  3. Service Stack • Today: Concentration on business applications • Online word processors, content management, customer relationship management, human resource management • Our contribution: Support for scientific applications • Gaussian, Gromacs, MoE, NWChem, …

  4. Agenda Motivation Architecture Scientific SaaS Survey Results

  5. Architecture Stack Optimal provisioning to fulfillSLAs Finding performance functions and dynamic job scheduling Scientific SaaS Eucalyptus implements the EC2 API Consideration of local resource management systems Analysis of different mapping policies (Greedy, RR, Green-IT,...) Cloud-/IaaS Is this approach applicable to scientific applications? How do scientific applications behave? Virtualization API to handle different hypervisors Extensions of libvirt to handle Hyper-V and ESX Virtualization High-performance interconnects (like Infiniband) Analysis of VMM-bypass, MR-IOV, SR-IOV HPC Hardware

  6. Aim of the survey • Can classical SLA used to provide information about scientific applications? • Can we estimate temporal behaviour of the applications? • Can we support disaster recovery? • Can we use virtualisation capabilities to increase scheduling efficiency? • Resizing of virtual machines? • Change number of nodes?

  7. Questionnaire Activity categories • Durable service: sustainable service runs 24/7 • Batch job: one-time computations (always halts) • Experiment: unknown termination behavior • Live progress status • Benchmark • Estimation function • Unknown Method to measure performance

  8. Result Business Scientific

  9. Application Parallelism • How does application scale (max. CPUs, etc)? • Parallelism is user decision - up to 1024 CPUs • How does parallelism behave over time? • no change in parallelism • Is it possible to add nodes on line? • No! • Is the resource demand time dependent? • Application dependent: constant or increasing over time • High internode or I/O communication? • All computing intensive, most I/O intensive

  10. Application Input • Are interactive user inputs possible during calculation? • No • Is all input available initial or are workflow dependencies? • Initial available • How much Data uses the application? • Several MB to several GB

  11. Application Progress Indication • Does the application provide progress information? • Progress often not available • If available • Gromacs reliable after 1000 iterations • R -> not linear • PlabSoft, SAS, ASRemL -> unreliable • Guess end time from history

  12. Checkpoint and Restart • Is application specific checkpointing possible? • Unknown for some applications • PlabSoft, SAS, ASRemL • Manually started • Gromacs • Automatically started • R, Gaussian, NWChem • Can a checkpoint continue computation with more nodes? • If available then possible

  13. Summary & Conclusion • Summary • Scientific applications are batch jobs • Halting problem: Determination of application finish not possible in every case • No online progress indication • Virtualisation • Online horizontal scaling virtual machines not supported • Vertical scaling helps • Conclusion • Providing scientific SaaS is challenging • Limited support for SLAs

  14. Georg Birkenheuer birke@uni-paderborn.de

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