Grid-enabled Probabilistic Model Checking with PRISM

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Grid-enabled Probabilistic Model Checking with
PRISM

• Yi Zhang, David Parker, Marta Kwiatkowska
• University of Birmingham

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Outline

• Introduction
• PRISM
• Parallel Numerical Engines
• Integrate Parallel Numerical Engines into PRISM
• Conclusion

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PRISM

• A probabilistic model checker
• Supports three types of models
• discrete-time Markov chains (DTMCs)
• continuous-time Markov chains (CTMCs)
• Markov decision processes (MDPs)
• A wide range of properties of these can be
  analysed.

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PRISM Case Studies

• Randomised distributed algorithms
• Communication and multimedia protocols
• Bluetooth, FireWire, Zeroconf
• Biological process modelling
• Security Protocols
• Dynamic Power Management
• www.cs.bham.ac.uk/dxp/prism/casestudies

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Motivation

• Probabilistic Model Checking is a powerful
  method.
• But because of state space explosion problem, it
  can be very expensive.
• Two approaches to combat the problem
• Symbolic approaches, such as MTBDD structures.
• Parallel and distributed solutions for
  probabilistic model checking.
• How to integrate parallel numerical engines into
  PRISM.

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Parallel Numerical Engines

• A Parallel Gauss-Seidel Iterative Method for
  shared memory machines.
• A Parallel Wavefront Guass-Seidel Method for
  Message passing machines.
• Based on MTBDD data structures
• Solving linear equation systems for analysis of
  CTMC and DTMC

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Integrate Parallel Numerical Engines into PRISM

• Manage remote computation resources for end
  users.
• Free end users from learning remote scheduling
  systems.
• Handling data transfer on behalf of end users.
• Monitoring job execution on remote computation
  resources.

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The Role of Globus Toolkits

• Provide building blocks for our middleware.
• GSI for security
• GRAM for job management
• GSI-Openssh for file transfering
• Grid services for data handling and job
  monitoring.

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Structures of Grid-enabled PRISM
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Job Submission Component

• Based on WS-GRAM.
• Generates job description files
• Communicates with WS-GRAM services at remote
  resources.

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Data Transfer

• Using GSI-OpenSSH for file transfer
• Matrices
• Vectors
• Create grid services for fine-grained data access
• Block by block

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Job Monitoring

• Based on WS-GRAM at current stage.
• Provide basic information about job status
• Grid service for job monitoring is under
  development.
• Information about job status
• Runtime information
• Convergence rate information

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Examples
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Examples
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Conclusion

• A grid-based middleware for PRISM.
• Provide easy access of remote parallel
  computation resources for end users.
• A foundation for future parallelisation work in
  PRISM.