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jcao_s_poland.ppt Presentation Transcript

  • 1. Advance Reservations of Grid Resources for GEMSS Applications Junwei Cao Falk Zimmermann Guy Lonsdale C&C Research Laboratories NEC Europe Ltd., Sankt Augustin, Germany http://www.ccrl-nece.de/
  • 2. Outline
    • GEMSS Overview
    • GEMSS Applications
    • GEMSS Architecture
    • COSY Resource Management
    • COSY Queue Scheduling
    • COSY Advance Reservations
    • Future Work
  • 3. GEMSS Project
    • GEMSS: Grid-Enabled Medical Simulation Services
    • EC/IST FP5 Grid Project (1.9.2002-28.2.2005)
    • Grid middleware initiative within medical application setting. http://www.gemss.de/.
    • GEMSS Consortium: 10 partners from industry & academia including University clinics:
    • NEC Europe Ltd , MPI Leipzig, ISS Vienna , CFX Ltd, CRID FUNDP, IT Innovation , USFD, IDAC Ireland Ltd., ASD, IBMTP Vienna.
  • 4. GEMSS Objectives
    • Demonstrate that the grid can improve pre-operative planning & near- real-time surgical support by providing access to advanced simulation and image-processing services.
    • Build middleware on existing or developing grid technology standards to provide support for authorization, workflow, security and Quality of Service aspects.
    • Develop, evaluate and validate a test-bed for the GEMSS system, including its deployment in the end-user’s working environment.
    • Anticipate privacy, security and other legal concerns by examining and incorporating into its grid services the latest laws and EU regulations related to providing medical services over the Internet.
  • 5. GEMSS Applications Medical end-users; Doctors, researchers On demand Medicine – nuclear / in vivo diagnostics Advanced image reconstruction Medical end-users; Doctors, researchers On demand Medicine – blood flow dynamics Cardio-vascular system simulation Medical end-users; Doctors, researchers On demand / distributed supercomputing Medicine – air flow dynamics Inhaled drud delivery planning Medical end-users; Doctors, researchers On demand / distributed supercomputing Medicine – Monte Carlo treatment simulation Radiotherapy planning Medical doctors, researchers On demand Medicine – intra-operative planning Neurosurgery support Medical doctors, researchers Distributed supercomputing / On demand Medicine – pre-surgical planning Maxillo-facial surgery simulation Users Class Domain Name
  • 6. Maxillo-facial Surgery Simulation
    • Provide a virtual try-out space for the pre-operative planning of maxillo-facial surgery.
    • Based on image processing, meshing & HPC Finite Element simulation.
    • Grid scenario: client-server, doctor uses a client machine to access remote HPC services.
    Modeling the distraction procedure Pre- and post surgery Courtesy Dr. Dr. Th. Hierl, University Clinic Leipzig.
  • 7. An Example Implementation
  • 8. GEMSS Architecture
    • Service-oriented architecture
    • Client-server topology
    • Based on Web Services technologies
    • Client-side pluggable component framework
    • Protocol-independent client-side APIs for service invocation
    • Support for complex process models:
      • Business process workflows
      • QoS negotiation workflows
      • Application workflows
  • 9. Client-side Architecture
  • 10. Server-side Architecture
  • 11. Resource Management Requirements
    • From application services:
    • Start a batch job
    • Kill a job
    • Job status query
    • From QoS negotiation services:
    • Book a reservation
    • Confirm a reservation
    • Release a reservation
    • Reservation status query
  • 12. COSY Structure Operating Systems, MPI Environments (NEC, SCore …), … Users / Applications Resources Generic User Interfaces (submit, release, queue, confirm,…) Job Scheduling Log Management Resource Monitoring Database Management Configurations Job Execution Plugin Scripts Specific User Interfaces (mpirun …)
  • 13. COSY Job Scheduling
    • Queue Scheduling:
      • First-come-first-served algorithm
      • Aggressive backfilling
      • Multiple queue support
      • Queue and user priority
      • Resource limitation
      • Access control list
      • Batch and interactive support
    • Advance Reservations:
      • Exact start time
      • Latest end time
      • Two phase commitment
  • 14. When Scheduling with ARs …
    • Mean queue wait time increases and resource utilization decreases.
    • Schedulable only with limit percentage of ARs.
    • How to prevent ARs from taking start time advantages over queued jobs?
    • How to satisfy ARs as far as possible without sacrificing queue efficiency ?
  • 15. COSY Policies for ARs …
    • A mandatory shortest notice time for ARs is defined
    • Using current mean wait time of queued jobs
    • Using times of mean wait time of queued jobs
  • 16. Experimental Results
    • Queue wait time
    • Resource utilization
  • 17. Summary
    • COSY takes the current mean wait time of queued jobs as the mandatory AR shortest notice time so that ARs cannot take start time advantages.
    • The mandatory AR shortest notice time is applied in COSY as 1 time of current mean wait time of queued jobs and increases to 4 linearly as the AR percentage increases from 0% (not inclusive) to 15% so that ARs can be satisfied as far as possible without increasing the queue wait time.
    • When the AR percentage is more than 15%, COSY will stop accepting ARs temporarily in order to guarantee a proper queue scheduling.
    • Advantage: adaptive to queue workload.
    • Disadvantage: additional calculation of statistics.
  • 18. Future Work
    • Implementation of GEMSS grid architecture (9.2003 – 11.2003 – 2.2004 – 8.2004)
    • Integration of GEMSS applications with GEMSS grid infrastructure (11.2003 – 8.2004)
    • Extension of COSY with standard languages and protocols (e.g. GRAAP)
    • Cost estimation of ARs over queue jobs
    • Co-reservation and co-allocation