Federated HPC Clouds applied to Radiation Therapy

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ISC Cloud‘13, Heidelberg (Germany)
Sep. 23-24th, 2013
A. Gómez, L.M. Carril, R. Valin, J.C. Mouriño, C. Cotelo

Published in: Technology, Health & Medicine
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Federated HPC Clouds applied to Radiation Therapy

  1. 1. Federated HPC Clouds applied to Radiation Therapy A. Gómez, L.M. Carril, R. Valin, J.C. Mouriño, C. Cotelo ISC Cloud‘13, Heidelberg (Germany) Sep. 23-24th, 2013
  2. 2. Overview Context. Virtual Cluster Architecture. Experiments on BonFIRE. Conclusions. The research leading to these results has received funding from the European Commision's Seventh Framework Programme (FP7/2007-2013) under grant agreement number 257386
  3. 3. Context: eIMRT service CTs Treatment Results Results TPS  Second calculation  Personalized: One patient, one treatment
  4. 4. eIMRT architecture IaaSSaaS Workflow based on Monte Carlo simulations
  5. 5. eIMRT Workflow eIMRT code: Prepares inputs for BEAMnrc MC. Seconds in master computer BEAMnrc MC simulations. Independent jobs on CEs. eIMRT code: collects outputs and prepares inputs for DOSXYZnrc Seconds in master computer eIMRT code: collects outputs and generates final output.. Seconds in master computer DOSXYZnrc MC simulations. Independent jobs on CEs.
  6. 6. SaaS issues Local cluster: – Could not be enough with many clients. – Interferences between customer’s requests. – Shared resources: Time-to-solution not guaranteed. Grid: – Interferences between clients. – Shared resources: Time-to-solution not guaranteed. Cloud: – One treatment, one virtual cluster. – No interferences between treatments, customers. – But, How to guarantee the time-to-solution in a multi- tenant out-of-control infrastructure?
  7. 7. IaaS issues for HPC/HTC SaaS Failures of sites. Needs Fault-tolerant design. Application Performance Variability between deployments. Needs elasticity. – Different IaaS back-end servers. – Multi-tenancy. Sharing resources among IaaS customers. – Different Cloud providers. – Evolution of IaaS infrastructure. J. Schad, et al, Runtime Measurements in the Cloud: Observing,Analyzing, and Reducing Variance., Proceedings of the VLDB Endowment, Vol. 3, No. 1, 2010
  8. 8. Proposal: Autonomous Virtual Cluster Architecture
  9. 9. Virtual Cluster Architecture
  10. 10. Virtual Cluster single site NFS Cluster management: OGS + custom scripts
  11. 11. Virtual Cluster-two sites
  12. 12. Fault-tolerant VC two sites
  13. 13. Elasticity Engine Controls number of CEs based on Key Application Performance measurements. Enlarges the cluster to keep performance and fulfill deadlines. Decreases size if App. Performance is higher than needed, to decrease costs.
  14. 14. Proof-of-Concept Experiments
  15. 15. BonFIRE Infrastructure Vendor Freq. (GHz) Cores RAM (GB) Intel 2.33 2*2 4 AMD 1,7 2*12 48 Intel 2,5 2*4 32 Intel 2.93 2*4 24 INRIA: Vendor Freq. (GHz) Cores RAM (GB) Intel 3.2 2*2 2 Intel 2.66 2*2 8 AMD 2.6 4*12 196 AMD 2 2 4 Intel I7 2.53 2 4 Intel I7 2.1 4 8 Intel Atom 1 2 AMD T56N 1.65 2 2 HLRS: Cloud Manager: OpenNebula 3.0
  16. 16. DISTRIBUTED VIRTUAL CLUSTER EXPERIMENT VCOC, FIRE Engineering Workshop, Ghent, Nov. 6th – 7th 2012
  17. 17. Application execution. One vs Two sites  VC Conf.: Distributed VC (_dist)  BonFIRE sites: – INRIA: Master + CEs – HLRS: CEs  Deployment time decreases.  App:Two sites faster than one site.  But because second site has better CPUs.  Impact of deployment ~ 10% total time.
  18. 18. SPECIFIC DEADLINE OBJECTIVE EXPERIMENT VCOC, FIRE Engineering Workshop, Ghent, Nov. 6th – 7th 2012
  19. 19. Horizontal elasticity  Monitoring application performance works.  We have modified software to produce information more frequently.  Execution with deadline.  Elasticity works.
  20. 20. FAULT TOLERANCE EXPERIMENT WITH ELASTICITY VCOC, FIRE Engineering Workshop, Ghent, Nov. 6th – 7th 2012
  21. 21. Virtual Cluster SYNC
  22. 22. Fault-tolerance  BonFIRE sites: – HLRS (Master + 4 CEs) – INRIA (Shadow + 4 CEs)  Demanded performance (500H/s)  Fault simulated putting HLRS VMs in CANCEL.  INRIA Shadow took control of cluster.  Elasticity worked, demanding more CEs to INRIA.
  23. 23. CONCLUSIONS VCOC, FIRE Engineering Workshop, Ghent, Nov. 6th – 7th 2012
  24. 24. Conclusions  Distributed VC can be used to speed up HTC applications.  Elasticity Engine based on Key Application Performance indicator for HTC works.  High QoS can be provided in VC using distributed VC + elasticity.  BonFIRE provides infrastructure for experiments about new concepts and services on Cloud.
  25. 25. THANKS Questions? agomez@cesga.es

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